Skip to main content
PLOS ONE logoLink to PLOS ONE
. 2021 Feb 19;16(2):e0247396. doi: 10.1371/journal.pone.0247396

Exploring novel and potent cell penetrating peptides in the proteome of SARS-COV-2 using bioinformatics approaches

Kimia Kardani 1, Azam Bolhassani 1,*
Editor: Surajit Bhattacharjya2
PMCID: PMC7894964  PMID: 33606823

Abstract

Among various delivery systems for vaccine and drug delivery, cell-penetrating peptides (CPPs) have been known as a potent delivery system because of their capability to penetrate cell membranes and deliver some types of cargoes into cells. Several CPPs were found in the proteome of viruses such as Tat originated from human immunodeficiency virus-1 (HIV-1), and VP22 derived from herpes simplex virus-1 (HSV-1). In the current study, a wide-range of CPPs was identified in the proteome of SARS-CoV-2, a new member of coronaviruses family, using in silico analyses. These CPPs may play a main role for high penetration of virus into cells and infection of host. At first, we submitted the proteome of SARS-CoV-2 to CellPPD web server that resulted in a huge number of CPPs with ten residues in length. Afterward, we submitted the predicted CPPs to C2Pred web server for evaluation of the probability of each peptide. Then, the uptake efficiency of each peptide was investigated using CPPred-RF and MLCPP web servers. Next, the physicochemical properties of the predicted CPPs including net charge, theoretical isoelectric point (pI), amphipathicity, molecular weight, and water solubility were calculated using protparam and pepcalc tools. In addition, the probability of membrane binding potential and cellular localization of each CPP were estimated by Boman index using APD3 web server, D factor, and TMHMM web server. On the other hand, the immunogenicity, toxicity, allergenicity, hemolytic potency, and half-life of CPPs were predicted using various web servers. Finally, the tertiary structure and the helical wheel projection of some CPPs were predicted by PEP-FOLD3 and Heliquest web servers, respectively. These CPPs were divided into: a) CPP containing tumor homing motif (RGD) and/or tumor penetrating motif (RXXR); b) CPP with the highest Boman index; c) CPP with high half-life (~100 hour) in mammalian cells, and d) CPP with +5.00 net charge. Based on the results, we found a large number of novel CPPs with various features. Some of these CPPs possess tumor-specific motifs which can be evaluated in cancer therapy. Furthermore, the novel and potent CPPs derived from SARS-CoV-2 may be used alone or conjugated to some sequences such as nuclear localization sequence (NLS) for vaccine and drug delivery.

Introduction

Therapeutic and preventive vaccines are promising approaches to solve health issues globally [1]. Although there are several vaccines for saving millions of lives till now such as vaccines against rubella, mumps, varicella, rotavirus, human papillomavirus (HPV) and hepatitis B virus (HBV), it is required to develop effective vaccines against other pathogens which are incurable and unprotectable [2,3]. In this line, development of effective and novel delivery systems is vital for delivery of vaccine components into cells. In general, delivery systems can be used to transfer different biomolecules into cells including nanoparticles [4], polymers [5], chitosan [6], liposome [7], physical tools [8], and cell penetrating peptides (CPPs) [9,10]. The current focus of developing a novel delivery system has moved to peptide-based delivery systems known as CPPs [11]. CPPs contain 5–50 amino acids in length which can enter cell membranes efficiently and deliver a wide range of cargoes including peptides, proteins, nanoparticles and nucleic acids into cells [12,13]. After discovery of the first CPP, Tat peptide (originated from human immunodeficiency virus type-1 (HIV-1) trans-activating regulatory (Tat) protein), a rapid growth of new CPPs has occurred [14]. The CPPs are natural (e.g., CyLoP-1) or synthetic (e.g., oligoarginine) peptides. These short peptides are heterogeneous in sequence and structure, and can be delivered through endocytosis or direct penetration [1,10,1517]. The mechanism of internalization depends on diverse factors such as CPP sequence, cell type, CPP concentration, temperature, incubation time, and type of cargo [18]. Up to now, a large number of CPPs have been recognized but some of them showed low uptake [19]. The studies demonstrated that prediction of CPPs by bioinformatics tools prior to lab-based experiments could save time and money [20]. For instance, machine-learning-based algorithms permit users to predict CPPs from large sequence data/ proteome. In prediction methods, machine learning models utilize various algorithms including neural network (NN) [21,22], kernel extreme learning machine [23,24], random forest (RF) [25], and support vector machine (SVM) [26,27].

In December 2019, a new member of the coronavirus family was found which firstly named as 2019-nCoV. Then, on February 11, 2020, its name was changed to Coronavirus Disease-2019 (COVID-19) or severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [28]. SARS-CoV-2 is an enveloped positive single-strand RNA virus that has 10 open reading frames (ORFs). ORF1ab is about 66.66% of virus genome which encodes two large polypeptides such as pp1a and pp1ab. Meanwhile, the ORF2-10 is about 33.33% of virus genome. SARS-CoV-2 genome encodes 28 proteins which are classified into three various classes such as structural proteins, non-structural proteins (nsp), and accessory proteins. The structural proteins are spike (S), nucleoprotein (N), membrane (M), and envelope (E) proteins that form the virus particles. In addition, the non-structural proteins (e.g., nsp1-nsp16) are generated only during the translation of virus RNA in the infected host cell. The accessory proteins possess crucial functions in the assembly, virulence and pathogenesis of the virus [28,29].

Previously, several CPPs were derived from viruses such as Tat (from HIV-1 transcriptional activator protein), C105Y (from HIV-1 glycoprotein 41), MPG (from HIV-1 glycoprotein 41 conjugated to nuclear localization sequence (NLS) from simian virus 40 (SV40)), Pep-1 (from HIV-1 reverse transcriptase conjugated to SV40 NLS), pepR and pepM (originated from Dengue virus), VP22 (originated from Herpes simplex virus (HSV)-1) [14,3034]. Up to now, no complete report has been available on CPPs derived from total proteins of MERS-CoV, SARS-CoV and SARS-CoV-2. Few studies indicated that some peptides of SARS-CoV spike glycoprotein are responsible for membrane fusion or membrane binding activity. For example, the upstream region of the heptad repeat1 (HR1) (residues 892–972) in S2 domain of SARS-CoV spike glycoprotein was involved in membrane fusion. Moreover, some scientists have recognized membrane binding peptides and membrane fusogenic peptides or potential fusion peptides from the upstream region of HR1 (residues 758–890) [35]. Indeed, an efficient membrane fusion mechanism between host cell and SARS-CoV-2 can be responsible for virus infection. Sequence comparison of S protein domains between SARS-CoV-2 and SARS-CoV-1 showed high level of conservation for both S1 and S2 domains. However, variation in the fusogenic regions of S2 domain was observed between SARS-CoV-2 and SARS-CoV-1 [3638]. Hence, due to high potency of SARS-CoV-2 to spread and infect people, we decided to investigate new and potent CPPs in the proteome of this newly isolated virus using in silico approaches.

Materials and methods

Study design

The current study has several main steps to find and characterize novel and potent CPPs as a vaccine and drug delivery system. The flowchart of overall prediction and analysis procedure was illustrated in S1 Fig.

Identification of potential SARS-CoV-2-derived CPPs

Cell penetrating or non-cell penetrating peptides (CPP or non-CPP) could be predicted in the proteome of SARS-CoV-2 using bioinformatics approaches. Hence, to explore novel CPPs, our reference sequence was Wuhan-Hu-1 with GenBank accession number MN908947.3. This strain was isolated from a patient in Wuhan, china. The phylogenetic analysis of whole viral genome contain 29,903 nucleotides that has 89.1% nucleotide similarity to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) which formerly had been recognized in bats [39].

At first, CellPPD web server (https://webs.iiitd.edu.in/raghava/cellppd/index.html) was applied to determine the cell penetrating peptides. CellPPD is a support vector machine (SVM)-based web server [40,41]. To utilize this web server, the sequences of Spike (S) protein (GenBank ID: QHD43416.1), Membrane (M) glycoprotein (GenBank ID: QHD43419.1), Nucleocapsid (N) phosphoprotein (GenBank ID: QHD43423.2), Envelope (E) protein (GenBank ID: QHD43418.1), Orf1ab polyprotein (GenBank ID: QHD43415.1), ORF3a protein (GenBank ID: QHD43417.1), ORF6 protein (GenBank ID: QHD43420.1), ORF7a protein (GenBank ID: QHD43421.1), ORF8 protein (GenBank ID: QHD43422.1), and ORF10 protein (GenBank ID: QHI42199.1) were submitted to protein scanning tool with default threshold of the SVM-based prediction method (SVM threshold was set at 0.0). Moreover, Tang and colleagues [42] developed a method with an overall prediction accuracy of 83.6%; hence, they established C2Pred web server (http://lin-group.cn/server/C2Pred) to investigate the CPP probability of each peptide.

Uptake efficiency analysis of the identified CPPs

In next step, to evaluate the uptake efficiency of the identified CPPs from previous step, two web servers including CPPred-RF (http://server.malab.cn/CPPred-RF/), and MLCPP (http://www.thegleelab.org/MLCPP/) were used. For this purpose, all of the detected CPPs using CellPPD web server were submitted to these web servers. CPPred-RF is a sequence-based predictor for identifying CPPs and their uptake efficiency. In addition, CPPred-RF built a two-layer prediction framework according to the random forest (RF) algorithm [43]. Manavalan et al. established a two-layer prediction framework termed as machine-learning-based prediction of cell-penetrating peptide (MLCPPs). The first-layer predicts that a submitted peptide is categorized as a CPP or non-CPP. Meanwhile, the second-layer predicts the uptake efficiency of the predicted CPPs [44].

Peptides property calculation

It is crucial to compute the physicochemical properties of peptides for predicting and designing novel and potent CPPs. Therefore, to achieve this aim, we calculated various physicochemical features of CPPs such as net charge, theoretical isoelectric point (pI), amphipathicity, molecular weight (MW), water solubility, hydrophobicity (H), hydrophobicity ratio, and polar-, non-polar-, uncharged- and charged residues. To calculate net charge, theoretical pI, and amphipathicity, CellPPD web server (https://webs.iiitd.edu.in/raghava/cellppd/index.html) was utilized. In addition, protparam tool (https://web.expasy.org/protparam/) was used to compute molecular weight of CPPs. Furthermore, to obtain the water solubility of peptides, Peptide property calculator (PepCalc) (https://pepcalc.com/) was applied. Also, hydrophobicity (H), and polar-, non-polar-, uncharged- and charged residues were estimated using Heliquest web server (https://heliquest.ipmc.cnrs.fr/cgi-bin/ComputParams.py).

Evaluation of membrane-binding ability of CPPs

In order to investigate the potential of binding peptides to membrane, two different methods were utilized. At first, we evaluated the Boman index or protein-binding potential using APD3 web server (http://aps.unmc.edu/AP/prediction/prediction_main.php). The Boman index is the sum of solubility values for all presented amino acids in a peptide sequence and illustrates the potential of a peptide for binding to the membrane or other proteins [45]. Secondly, to evaluate the membrane-binding potential of each peptide, the discrimination factor (D) was calculated [46]. For this purpose, we used Heliquest web server (https://heliquest.ipmc.cnrs.fr/cgi-bin/ComputParams.py) to obtain hydrophobic moment (μH). After determination of hydrophobic moment and also net charge (Z), the D factor was calculated according to the following equation: D = 0.944(<μH>) + 0.33(Z).

In addition, TMHMM web server (http://www.cbs.dtu.dk/services/TMHMM/) was utilized to investigate the cellular localization of CPPs [47]. This web server analyzes the probability of binding a peptide to the bacterial cell membrane (BCM) which possesses negative charge.

Assessment of the immunogenicity

Immunogenicity of the CPPs is one of their disadvantages. It was confirmed that peptides could induce immunologic responses in vivo, resulting in allergic reactions. The existence of peptides in body can stimulate the generation of antibodies which may neutralize therapeutic effects and reduce their efficacy [48, 49]. Hence, to assess the immunogenicity of CPPs, each peptide was submitted to IEDB Immunogenicity Predictor (http://tools.iedb.org/immunogenicity/) [50].

Determination of toxicity and allergenicity

To investigate the toxicity and allergenicity of CPPs, each peptide was submitted to ToxinPred web server (https://webs.iiitd.edu.in/raghava/toxinpred/algo.php), and AllerTop (https://www.ddg-pharmfac.net/AllerTOP/) and AllergenFP (http://ddg-pharmfac.net/AllergenFP/) web servers, respectively [5153].

Estimation of hemolytic potency and half-life

The hemolytic property of peptides was predicted by HemoPI using SVM-based method (https://webs.iiitd.edu.in/raghava/hemopi/design.php). Furthermore, the half-life in E.coli and in mammalian cell was calculated using ProtLifePred web server based on N-end rule (http://protein-n-end-rule.leadhoster.com/) [54].

Prediction of structure

Three dimensional (3D) structure of some predicted CPPs was analyzed by de novo peptide structure prediction server (PEP-FOLD3) (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/). PEP-FOLD3 is a de novo method that predicts peptide structures using amino acid sequences. This approach determines the conformation of four consecutive amino acid residues according to structural alphabet (SA) letters [55]. Additionally, the helical wheel diagram of CPPs was defined by Schiffer Edmundson wheel modelling using Heliquest web server (https://heliquest.ipmc.cnrs.fr/cgi-bin/ComputParams.py) [46].

Results

Identification of potential SARS-CoV-2-derived CPPs

To obtain cell penetrating peptides in the proteome of SARS-CoV-2, the sequences of S protein, M glycoprotein, N phosphoprotein, E protein, Orf1ab polyprotein, and ORF3a, ORF6, ORF7a, ORF8 and ORF10 proteins were submitted to protein scanning tools of CellPPD web server. Then, we applied C2Pred web server to achieve the CPP probability of peptides. All of the detected CPPs, and their SVM scores and probability scores were listed in Table 1. No CPP was found in E protein, and only one CPP was identified in ORF6. Meanwhile, Orf1ab had the most CPPs in its proteome. C2pred web server identifies peptides lower than 0.5 as non-CPPs, and peptides greater than 0.5 as CPPs. Although, some peptides were predicted as CPPs by CellPPD, but C2Pred detected them as non-CPPs. For instance, DMSKFPLKLR peptide derived from Orf1ab polyprotein was predicted as CPP by CellPPD with SVM score of 0.11, while C2Pred determined this peptide as non-CPP with score 0.167688.

Table 1. Predicted CPPs and their uptake efficiency using various web servers.

Epitope CellPPD (SVM score) CPP Probability score by C2Pred* Uptake efficiency by MLCPP Uptake efficiency by CPPred-RF
S protein
NLTTRTQLPP 0.03 0.549136 Low High
RFQTLLALHR 0.11 0.924753 Low High
YLQPRTFLLK 0.05 0.745663 Low High
SVYAWNRKRI 0.12 0.542719 Low High
YAWNRKRISN 0.21 0.581372 Low High
AWNRKRISNC 0.10 0.581372 Low High
WNRKRISNCV 0.07 0.422121 Low High
RQIAPGQTGK 0.05 0.480859 Low High
YNYLYRLFRK 0.10 0.434133 High High
YLYRLFRKSN 0.17 0.323746 High High
YRLFRKSNLK 0.18 0.651294 Low High
RLFRKSNLKP 0.18 0.633622 Low High
RKSNLKPFER 0.14 0.800554 Low High
KKSTNLVKNK 0.33 0.588285 Low High
KSTNLVKNKC 0.06 0.527724 Low High
HADQLTPTWR 0.00 0.545188 Low Non-CPP
YQTQTNSPRR 0.11 0.149319 Low High
TQTNSPRRAR 0.11 0.143146 Low High
TNSPRRARSV 0.16 0.142480 Low High
NSPRRARSVA 0.14 0.108221 Low High
PRRARSVASQ 0.17 0.236319 Low High
KQIYKTPPIK 0.46 0.944703 Low High
SQILPDPSKP 0.03 0.906582 Low High
RLITGRLQSL 0.21 0.663600 Low High
M protein
NRNRFLYIIK 0.02 0.220554 Low High
RNRFLYIIKL 0.18 0.114117 Low High
YIIKLIFLWL 0.01 0.669640 Low High
KLIFLWLLWP 0.31 0.709344 Low High
FIASFRLFAR 0.10 0.421173 Low High
ASFRLFARTR 0.20 0.594022 Low High
SFRLFARTRS 0.06 0.553645 Low High
FRLFARTRSM 0.21 0.385465 Low High
RLFARTRSMW 0.28 0.383433 Low High
FARTRSMWSF 0.04 0.463913 Low High
HGTILTRPLL 0.03 0.430916 Low High
GAVILRGHLR 0.13 0.223997 High High
RIAGHHLGRC 0.02 0.243928 Low High
YSRYRIGNYK 0.05 0.897477 Low High
N protein
PQNQRNAPRI 0.09 0.413323 Low High
ERSGARSKQR 0.06 0.261718 Low High
RSGARSKQRR 0.54 0.292041 Low High
SGARSKQRRP 0.25 0.300551 Low High
GARSKQRRPQ 0.40 0.250417 Low High
ARSKQRRPQG 0.17 0.298689 Low High
RSKQRRPQGL 0.36 0.192042 Low High
SKQRRPQGLP 0.06 0.164110 Low High
KQRRPQGLPN 0.18 0.206509 Low High
RRPQGLPNNT 0.12 0.216779 Low High
QIGYYRRATR 0.09 0.531651 Low High
IGYYRRATRR 0.23 0.604819 Low High
GYYRRATRRI 0.29 0.965070 Low High
YYRRATRRIR 0.58 0.952696 Low High
YRRATRRIRG 0.59 0.935039 Low High
RRATRRIRGG 0.56 0.939794 Low High
RATRRIRGGD 0.33 0.924855 Low High
TRRIRGGDGK 0.10 0.369598 Low High
RIRGGDGKMK 0.11 0.250269 Low High
GKMKDLSPRW 0.02 0.735642 Low High
SQASSRSSSR 0.05 0.677066 Low High
ASSRSSSRSR 0.17 0.304528 Low High
SSRSSSRSRN 0.09 0.304528 Low High
SRSSSRSRNS 0.14 0.297448 Low High
RSSSRSRNSS 0.12 0.432735 Low High
SSSRSRNSSR 0.13 0.315140 Low High
SSRSRNSSRN 0.10 0.315140 Low High
RSRNSSRNST 0.04 0.359073 Low High
GSSRGTSPAR 0.06 0.716403 Low High
AALALLLLDR 0.14 0.898706 Low High
ALALLLLDRL 0.05 0.895190 Low High
KKSAAEASKK 0.33 0.730535 Low High
KSAAEASKKP 0.13 0.669679 Low High
SAAEASKKPR 0.06 0.669679 Low High
AAEASKKPRQ 0.10 0.963042 Low High
AEASKKPRQK 0.15 0.96509 Low High
EASKKPRQKR 0.34 0.988105 Low High
ASKKPRQKRT 0.28 0.947786 Low High
SKKPRQKRTA 0.32 0.947786 Low High
KKPRQKRTAT 0.47 0.947786 Low High
KPRQKRTATK 0.42 0.941370 Low High
PRQKRTATKA 0.23 0.936878 Low High
RQKRTATKAY 0.13 0.936878 Low High
RQGTDYKHWP 0.02 0.249058 Low High
FPPTEPKKDK 0.04 0.937092 Low Non-CPP
PPTEPKKDKK 0.12 0.954110 Low High
PTEPKKDKKK 0.20 0.965728 Low High
TEPKKDKKKK 0.51 0.972132 Low High
EPKKDKKKKA 0.35 0.969009 Low High
PKKDKKKKAD 0.28 0.736106 Low High
KKDKKKKADE 0.26 0.275127 Low High
TQALPQRQKK 0.11 0.378326 Low High
ALPQRQKKQQ 0.17 0.715403 Low High
ORF3a
SASKIITLKK 0.07 0.666607 Low High
ASKIITLKKR 0.19 0.975854 Low High
SKIITLKKRW 0.31 0.977206 Low High
KIITLKKRWQ 0.56 0.977206 Low High
IITLKKRWQL 0.04 0.981679 Low High
KKRWQLALSK 0.47 0.908996 Low High
KRWQLALSKG 0.31 0.711236 Low High
VRIIMRLWLC 0.11 0.512905 Low High
RIIMRLWLCW 0.25 0.512905 Low High
IIMRLWLCWK 0.14 0.584110 Low High
IMRLWLCWKC 0.06 0.584110 High High
MRLWLCWKCR 0.17 0.584110 High High
RLWLCWKCRS 0.22 0.578205 High High
LWLCWKCRSK 0.28 0.516957 High High
WLCWKCRSKN 0.12 0.456487 High High
LCWKCRSKNP 0.16 0.456487 High High
CWKCRSKNPL 0.14 0.815386 High High
KCRSKNPLLY 0.10 0.172543 Low High
Orf1ab
IKRSDARTAP 0.00 0.611515 Low High
KRSDARTAPH 0.08 0.611515 Low High
PVAYRKVLLR 0.05 0.395891 High High
VAYRKVLLRK 0.07 0.461402 High High
AYRKVLLRKN 0.13 0.873577 Low High
YRKVLLRKNG 0.05 0.873577 Low High
RKVLLRKNGN 0.14 0.892119 Low High
KVLLRKNGNK 0.01 0.921012 Low High
FEIKLAKKFD 0.09 0.877125 Low High
KTIQPRVEKK 0.39 0.961264 Low High
TIQPRVEKKK 0.13 0.954110 Low High
IQPRVEKKKL 0.13 0.953833 Low High
SGLKTILRKG 0.02 0.624757 Low High
LKTILRKGGR 0.24 0.808425 Low High
KTILRKGGRT 0.15 0.805867 Low High
GNFKVTKGKA 0.03 0.385736 Low High
FKVTKGKAKK 0.35 0.445305 Low High
KVTKGKAKKG 0.26 0.339767 Low High
KGKAKKGAWN 0.07 0.184903 Low High
GGAKLKALNL 0.04 0.430832 Low High
SKGLYRKCVK 0.09 0.145729 Low High
KGLYRKCVKS 0.07 0.145729 Low High
GLYRKCVKSR 0.34 0.274849 Low High
GLLMPLKAPK 0.09 0.427961 Low High
QRKQDDKKIK 0.15 0.861718 Low High
RKQDDKKIKA 0.17 0.976533 Low High
KQDDKKIKAC 0.07 0.318404 Low High
DITFLKKDAP 0.03 0.682254 Low Non-CPP
MLAKALRKVP 0.28 0.671110 High High
LAKALRKVPT 0.08 0.671110 Low High
EAKTVLKKCK 0.13 0.717688 Low High
AKTVLKKCKS 0.13 0.717688 Low High
KTVLKKCKSA 0.22 0.546244 Low High
KSAFYILPSI 0.00 0.108440 Low High
KAIVSTIQRK 0.08 0.204524 Low High
STIQRKYKGI 0.07 0.433511 Low High
TIQRKYKGIK 0.13 0.433511 Low High
IQRKYKGIKI 0.08 0.433511 Low High
GARFYFYTSK 0.04 0.178363 Low High
ARYMRSLKVP 0.06 0.471501 Low High
GIEFLKRGDK 0.08 0.224238 Low High
DNLKTLLSLR 0.03 0.361469 High Non-CPP
YMSALNHTKK 0.05 0.330689 Low High
SALNHTKKWK 0.12 0.709364 Low High
ALNHTKKWKY 0.10 0.888647 Low High
LNHTKKWKYP 0.14 0.913973 Low High
NHTKKWKYPQ 0.10 0.678789 Low High
HTKKWKYPQV 0.04 0.303493 Low Non-CPP
KKPASRELKV 0.14 0.769021 Low High
KPASRELKVT 0.07 0.743805 Low High
YTPSFKKGAK 0.08 0.164140 Low High
PSFKKGAKLL 0.00 0.210028 Low High
FKKGAKLLHK 0.16 0.308486 Low High
KKGAKLLHKP 0.48 0.308486 Low High
KGAKLLHKPI 0.32 0.261312 Low High
WCIRCLWSTK 0.12 0.727461 High High
CIRCLWSTKP 0.20 0.727461 Low High
ANYAKPFLNK 0.01 0.474322 Low High
TNIVTRCLNR 0.01 0.309069 Low High
CTFTRSTNSR 0.02 0.513802 Low High
TCMMCYKRNR 0.01 0.331608 High High
MCYKRNRATR 0.25 0.936560 Low High
CYKRNRATRV 0.03 0.936560 Low High
YKRNRATRVE 0.00 0.936560 Low High
KRNRATRVEC 0.18 0.532289 Low High
RNRATRVECT 0.06 0.325111 Low High
RDLSLQFKRP 0.17 0.903779 Low High
SLQFKRPINP 0.14 0.353192 Low High
HNIALIWNVK 0.01 0.377627 Low High
LSEQLRKQIR 0.04 0.269638 Low High
QLRKQIRSAA 0.03 0.615417 Low High
LRKQIRSAAK 0.02 0.718721 Low High
RKQIRSAAKK 0.30 0.751730 Low High
KQIRSAAKKN 0.38 0.729912 Low High
QIRSAAKKNN 0.11 0.729912 Low High
AAKKNNLPFK 0.09 0.933084 Low High
KKNNLPFKLT 0.04 0.938618 Low High
NNWLKQLIKV 0.01 0.793676 High High
LAYYFMRFRR 0.04 0.301382 Low High
AYYFMRFRRA 0.05 0.289065 Low High
YYFMRFRRAF 0.08 0.182381 High High
FMRFRRAFGE 0.05 0.167797 High High
MRFRRAFGEY 0.05 0.167797 High High
KEMYLKLRSD 0.02 0.354324 Low High
YNRYLALYNK 0.03 0.241742 High High
RYLALYNKYK 0.03 0.232393 High High
FRKMAFPSGK 0.03 0.318037 Low High
TANPKTPKYK 0.15 0.944703 Low High
ANPKTPKYKF 0.03 0.944703 Low High
PKTPKYKFVR 0.12 0.944703 Low High
KTPKYKFVRI 0.10 0.917400 Low High
RWFLNRFTTT 0.02 0.601321 Low High
FQSAVKRTIK 0.08 0.586710 Low High
SEVVLKKLKK 0.24 0.204172 Low High
VVLKKLKKSL 0.09 0.270145 Low High
VLKKLKKSLN 0.12 0.910975 Low High
KKLKKSLNVA 0.15 0.190690 Low High
DAAMQRKLEK 0.00 0.372134 Low High
AAMQRKLEKM 0.01 0.372134 Low High
MQRKLEKMAD 0.16 0.525121 Low High
YKQARSEDKR 0.02 0.246035 Low High
KQARSEDKRA 0.13 0.242658 Low High
QARSEDKRAK 0.05 0.242658 Low High
MLFTMLRKLD 0.05 0.894360 Low Non-CPP
QDLKWARFPK 0.02 0.860963 Low High
DLKWARFPKS 0.08 0.860963 Low Non-CPP
LKWARFPKSD 0.22 0.397145 Low High
KGFCDLKGKY 0.06 0.207285 Low High
GVSAARLTPC 0.03 0.277471 Low High
GFAKFLKTNC 0.01 0.119116 Low Non-CPP
KTNCCRFQEK 0.08 0.481756 Low High
PHISRQRLTK 0.28 0.636709 Low High
HISRQRLTKY 0.24 0.636709 Low High
ISRQRLTKYT 0.11 0.636709 Low High
SRQRLTKYTM 0.01 0.835148 Low High
RQRLTKYTMA 0.11 0.878279 Low High
GERVRQALLK 0.09 0.849631 Low High
RVRQALLKTV 0.09 0.932330 High High
KPYIKWDLLK 0.01 0.876722 Low High
RLKLFDRYFK 0.05 0.261079 Low High
KLFDRYFKYW 0.04 0.219406 High Non-CPP
FPFNKWGKAR 0.02 0.889091 Low High
KWGKARLYYD 0.10 0.289722 Low High
YAISAKNRAR 0.03 0.154021 Low High
AISAKNRART 0.05 0.241258 Low High
KNRARTVAGV 0.12 0.254056 Low High
NRQFHQKLLK 0.14 0.872914 Low High
RQFHQKLLKS 0.06 0.938861 Low High
RIMASLVLAR 0.05 0.515705 High High
RNLQHRLYEC 0.08 0.165926 Low Non-CPP
RLYECLYRNR 0.15 0.219346 Low High
SLRCGACIRR 0.10 0.297578 High High
RCGACIRRPF 0.15 0.288701 High High
CGACIRRPFL 0.06 0.282884 High High
GACIRRPFLC 0.19 0.252159 High High
ACIRRPFLCC 0.11 0.249754 High High
CIRRPFLCCK 0.31 0.306758 High High
IRRPFLCCKC 0.11 0.355792 High High
RRPFLCCKCC 0.18 0.274986 High High
MSYYCKSHKP 0.01 0.306949 Low High
ANTCTERLKL 0.02 0.144577 Low High
SWEVGKPRPP 0.02 0.577246 Low High
VGKPRPPLNR 0.11 0.233226 Low High
GKPRPPLNRN 0.16 0.742499 Low High
KALKYLPIDK 0.20 0.613457 Low High
DKCSRIIPAR 0.01 0.588037 Low High
KCSRIIPARA 0.05 0.577426 Low High
CSRIIPARAR 0.07 0.565072 Low High
SRIIPARARV 0.11 0.783032 Low High
RIIPARARVE 0.17 0.441924 Low High
SVVNARLRAK 0.04 0.107521 Low High
VVNARLRAKH 0.10 0.101852 Low High
VNARLRAKHY 0.08 0.351672 Low High
NARLRAKHYV 0.13 0.942607 Low High
PAPRTLLTKG 0.10 0.701705 Low High
APRTLLTKGT 0.02 0.414738 Low High
FNSVCRLMKT 0.01 0.084142 Low Non-CPP
FLGTCRRCPA 0.09 0.153800 High High
DNKLKAHKDK 0.05 0.454919 Low High
KLKAHKDKSA 0.22 0.624144 Low High
FLTRNPAWRK 0.17 0.889087 Low High
RNPAWRKAVF 0.18 0.548965 Low High
GIPKDMTYRR 0.06 0.265085 Low High
DMTYRRLISM 0.10 0.234218 Low Non-CPP
GNPKAIKCVP 0.05 0.253610 Low High
WNTFTRLQSL 0.06 0.961510 Low Non-CPP
ELWAKRNIKP 0.06 0.982818 Low High
LWAKRNIKPV 0.01 0.982818 Low High
WAKRNIKPVP 0.17 0.862586 Low High
RNIKPVPEVK 0.05 0.642094 Low High
LLIGLAKRFK 0.19 0.367884 High High
GLAKRFKESP 0.18 0.908642 Low High
KMQRMLLEKC 0.17 0.500000 High High
VLRQWLPTGT 0.00 0.407571 Low High
DMSKFPLKLR 0.11 0.167688 High High
MSKFPLKLRG 0.02 0.319169 Low High
SKFPLKLRGT 0.09 0.463853 Low High
KFPLKLRGTA 0.01 0.463853 Low High
MILSLLSKGR 0.02 0.290394 Low High
LLSKGRLIIR 0.08 0.570179 High High
GRLIIRENNR 0.03 0.864554 Low High
RLIIRENNRV 0.03 0.930275 Low High
ORF6
LIIKNLSKSL 0.03 0.692987 Low High
ORF7a
HVYQLRARSV 0.00 0.333973 Low High
QLRARSVSPK 0.18 0.788277 Low High
RARSVSPKLF 0.06 0.540759 Low High
RSVSPKLFIR 0.03 0.387766 Low High
ITLCFTLKRK 0.00 0.781141 High High
TLCFTLKRKT 0.17 0.809793 Low High
LCFTLKRKTE 0.01 0.809793 Low High
ORF8
SKWYIRVGAR 0.06 0.354214 Low High
KWYIRVGARK 0.15 0.337595 Low High
YIRVGARKSA 0.13 0.276083 Low High

* Higher scores show more possibility of cell-penetrating potential.

Uptake efficiency analysis of the identified CPPs

The uptake efficiency of the predicted CPPs was evaluated using two different web servers such as CPPred-RF and MLCPP. These web servers classify CPPs in two categories: high or low uptake efficiency (Table 1).

Calculation of peptide properties

Various physicochemical characteristics of peptides were recognized by diverse web servers such as net charge, pI, MW, amphipathicity, water solubility, hydrophobicity, hydrophobicity ratio, and polar-, non-polar-, uncharged- and charged residues. For instance, a cationic CPP can bind to cell membrane (with negative charge), then can penetrate and deliver cargoes into cells [9]. All of the physicochemical properties of CPPs were determined in Table 2.

Table 2. The properties of peptides determined by diverse web servers and tools.

Epitope Net charge pI Amphipathicity MW Water solubility Hydrophobicity (H) Hydrophobic ratio by APD Polar residues + GLY (n/%) Nonpolar residues (n/%) Uncharged residues + GLY Charged residues
S protein
NLTTRTQLPP 1.00 10.11 0.37 1140.31 Poor 0.379 20% 6/60.00 4/40.00 GLN 1, THR 3, ASN 1, GLY 0 ARG 1
RFQTLLALHR 2.50 12.01 0.76 1254.50 Poor 0.535 50% 5/50.00 5/50.00 GLN 1, HIS 1, THR 1, GLY 0 ARG 2
YLQPRTFLLK 2.00 10.01 0.74 1278.56 Poor 0.661 40% 4/40.00 6/60.00 GLN 1, THR 1, GLY 0 LYS 1, ARG 1
SVYAWNRKRI 3.00 11.01 0.86 1292.51 Good 0.289 40% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 1, ARG 2
YAWNRKRISN 3.00 11.01 0.86 1307.48 Good 0.107 30% 6/60.00 4/40.00 SER 1, ASN 2, GLY 0 LYS 1, ARG 2
AWNRKRISNC 3.00 10.87 0.86 1247.45 Good 0.165 40% 6/60.00 4/40.00 SER 1, ASN 2, GLY 0 LYS 1, ARG 2
WNRKRISNCV 3.00 10.87 0.86 1275.50 Good 0.256 40% 6/60.00 4/40.00 SER 1, ASN 2, GLY 0 LYS 1, ARG 2
RQIAPGQTGK 2.00 11.01 0.86 1055.20 Good 0.065 20% 7/70.00 3/30.00 GLN 2, THR 1, GLY 2 LYS 1, ARG 1
YNYLYRLFRK 3.00 10.00 0.86 1435.69 Good 0.446 30% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 1, ARG 2
YLYRLFRKSN 3.00 10.29 0.86 1435.69 Good 0.346 30% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 1, ARG 2
YRLFRKSNLK 4.00 11.10 1.22 1324.59 Good 0.151 30% 6/60.00 4/40.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 2
RLFRKSNLKP 4.00 12.02 1.22 1258.53 Good 0.127 30% 6/60.00 4/40.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 2
RKSNLKPFER 3.00 11.01 1.35 1274.49 Good -0.107 20% 7/70.00 3/30.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 2, GLU 1
KKSTNLVKNK 4.00 10.49 1.47 1159.40 Good -0.202 20% 8/80.00 2/20.00 SER 1, THR 1, ASN 2, GLY 0 LYS 4
KSTNLVKNKC 3.00 9.81 1.10 1134.37 Good 0.051 30% 7/70.00 3/30.00 SER 1, THR 1, ASN 2, GLY 0 LYS 3
HADQLTPTWR 0.50 7.10 0.52 1224.34 Good 0.363 30% 6/60.00 4/40.00 GLN 1, HIS 1, THR 2, GLY 0 ARG 1, ASP 1
YQTQTNSPRR 2.00 10.84 0.74 1250.34 Good -0.090 0% 8/80.00 2/20.00 GLN 2, SER 1, THR 2, ASN 1, GLY 0 ARG 2
TQTNSPRRAR 3.00 12.31 0.86 1186.30 Good -0.234 10% 8/80.00 2/20.00 GLN 1, SER 1, THR 2, ASN 1, GLY 0 ARG 3
TNSPRRARSV 3.00 12.31 0.74 1143.27 Good -0.120 20% 7/70.00 3/30.00 SER 2, THR 1, ASN 1, GLY 0 ARG 3
NSPRRARSVA 3.00 12.31 0.74 1113.25 Good -0.115 30% 6/60.00 4/40.00 SER 2, ASN 1, GLY 0 ARG 3
PRRARSVASQ 3.00 12.31 0.86 1127.27 Good -0.077 30% 6/60.00 4/40.00 GLN 1, SER 2, GLY 0 ARG 3
KQIYKTPPIK 3.00 10.01 1.23 1215.50 Good 0.307 20% 5/50.00 5/50.00 GLN 1, THR 1, GLY 0 LYS 3
SQILPDPSKP 0.00 6.19 0.49 1081.23 Good 0.360 20% 5/50.00 5/50.00 GLN 1, SER 2, GLY 0 LYS 1, ASP 1
RLITGRLQSL 2.00 12.01 0.61 1156.39 Good 0.488 40% 6/60.00 4/40.00 GLN 1, SER 1, THR 1, GLY 1 ARG 2
M protein
NRNRFLYIIK 3.00 11.01 0.86 1336.60 Good 0.384 40% 5/50.00 5/50.00 ASN 2, GLY 0 LYS 1, ARG 2
RNRFLYIIKL 3.00 11.01 0.86 1335.66 Good 0.614 50% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 1, ARG 2
YIIKLIFLWL 1.00 8.94 0.37 1321.71 Poor 1.451 80% 1/10.00 9/90.00 GLY 0 LYS 1
KLIFLWLLWP 1.00 9.11 0.37 1328.71 Poor 1.462 80% 1/10.00 9/90.00 GLY 0 LYS 1
FIASFRLFAR 2.00 12.01 0.49 1227.48 Poor 0.743 70% 3/30.00 7/70.00 SER 1, GLY 0 ARG 2
ASFRLFARTR 3.00 12.31 0.74 1224.43 Good 0.309 50% 5/50.00 5/50.00 SER 1, THR 1, GLY 0 ARG 3
SFRLFARTRS 3.00 12.31 0.74 1240.43 Good 0.274 40% 6/60.00 4/40.00 SER 2, THR 1, GLY 0 ARG 3
FRLFARTRSM 3.00 12.31 0.74 1284.55 Good 0.401 50% 5/50.00 5/50.00 SER 1, THR 1, GLY 0 ARG 3
RLFARTRSMW 3.00 12.31 0.74 1323.59 Good 0.447 50% 5/50.00 5/50.00 SER 1, THR 1, GLY 0 ARG 3
FARTRSMWSF 2.00 12.01 0.49 1323.59 Poor 0.553 50% 5/50.00 5/50.00 SER 2, THR 1, GLY 0 ARG 2
HGTILTRPLL 1.50 10.11 0.39 1120.36 Poor 0.726 40% 5/50.00 5/50.00 HIS 1, THR 2, GLY 1 ARG 1
GAVILRGHLR 2.50 12.01 0.64 1091.33 Good 0.484 50% 5/50.00 5/50.00 HIS 1, GLY 2 ARG 2
RIAGHHLGRC 3.00 10.38 0.78 1119.32 Good 0.359 40% 6/60.00 4/40.00 HIS 2, GLY 2 ARG 2
YSRYRIGNYK 3.00 10.00 0.86 1319.49 Good 0.103 10% 6/60.00 4/40.00 SER 1, ASN 1, GLY 1 LYS 1, ARG 2
N protein
PQNQRNAPRI 2.00 12.01 0.74 1193.33 Good -0.011 20% 6/60.00 4/40.00 GLN 2, ASN 2, GLY 0 ARG 2
ERSGARSKQR 3.00 11.72 1.35 1174.29 Good -0.465 10% 9/90.00 1/10.00 GLN 1, SER 2, GLY 1 LYS 1, ARG 3, GLU 1
RSGARSKQRR 5.00 12.48 1.47 1201.36 Good -0.502 10% 9/90.00 1/10.00 GLN 1, SER 2, GLY 1 LYS 1, ARG 4
SGARSKQRRP 4.00 12.31 1.23 1142.29 Good -0.329 10% 8/80.00 2/20.00 GLN 1, SER 2, GLY 1 LYS 1, ARG 3
GARSKQRRPQ 4.00 12.31 1.35 1183.34 Good -0.347 10% 8/80.00 2/20.00 GLN 2, SER 1, GLY 1 LYS 1, ARG 3
ARSKQRRPQG 4.00 12.31 1.35 1183.34 Good -0.347 10% 8/80.00 2/20.00 GLN 2, SER 1, GLY 1 LYS 1, ARG 3
RSKQRRPQGL 4.00 12.31 1.35 1225.42 Good -0.208 10% 8/80.00 2/20.00 GLN 2, SER 1, GLY 1 LYS 1, ARG 3
SKQRRPQGLP 3.00 12.01 1.11 1166.35 Good -0.035 10% 7/70.00 3/30.00 GLN 2, SER 1, GLY 1 LYS 1, ARG 2
KQRRPQGLPN 3.00 12.01 1.11 1193.38 Good -0.091 10% 7/70.00 3/30.00 GLN 2, ASN 1, GLY 1 LYS 1, ARG 2
RRPQGLPNNT 2.00 12.01 0.61 1152.28 Good -0.004 10% 7/70.00 3/30.00 GLN 1, THR 1, ASN 2, GLY 1 ARG 2
QIGYYRRATR 3.00 10.91 0.86 1283.46 Good 0.104 20% 6/60.00 4/40.00 GLN 1, THR 1, GLY 1 ARG 3
IGYYRRATRR 4.00 11.56 0.98 1311.51 Good 0.025 20% 6/60.00 4/40.00 THR 1, GLY 1 ARG 4
GYYRRATRRI 4.00 11.56 0.98 1311.51 Good 0.025 20% 6/60.00 4/40.00 THR 1, GLY 1 ARG 4
YYRRATRRIR 5.00 11.84 1.23 1410.65 Good -0.076 20% 6/60.00 4/40.00 THR 1, GLY 0 ARG 5
YRRATRRIRG 5.00 12.18 1.23 1410.65 Good -0.172 20% 7/70.00 3/30.00 THR 1, GLY 1 ARG 5
RRATRRIRGG 5.00 12.61 1.23 1198.40 Good -0.268 20% 8/80.00 2/20.00 THR 1, GLY 2 ARG 5
RATRRIRGGD 3.00 12.01 0.98 1157.30 Good -0.244 20% 8/80.00 2/20.00 THR 1, GLY 2 ARG 4, ASP 1
TRRIRGGDGK 3.00 11.72 1.10 1115.26 Good -0.273 30% 9/90.00 1/10.00 THR 1, GLY 3 LYS 1, ARG 3, ASP 1
RIRGGDGKMK 3.00 11.01 1.22 1117.34 Good -0.174 20% 8/80.00 2/20.00 GLY 3 LYS 2, ARG 2, ASP 1
GKMKDLSPRW 2.00 10.01 0.98 1217.46 Good 0.210 30% 6/60.00 4/40.00 SER 1, GLY 1 LYS 2, ARG 1, ASP 1
SQASSRSSSR 2.00 12.01 0.61 1052.07 Good -0.217 10% 9/90.00 1/10.00 GLN 1, SER 6, GLY 0 ARG 2
ASSRSSSRSR 3.00 12.31 0.74 1080.13 Good -0.296 10% 9/90.00 1/10.00 SER 6, GLY 0 ARG 3
SSRSSSRSRN 3.00 12.31 0.74 1123.15 Good -0.387 0% 10/100.00 0/0.00 SER 6, ASN 1, GLY 0 ARG 3
SRSSSRSRNS 3.00 12.31 0.74 1123.15 Good -0.387 0% 10/100.00 0/0.00 SER 6, ASN 1, GLY 0 ARG 3
RSSSRSRNSS 3.00 12.31 0.74 1123.15 Good -0.387 0% 10/100.00 0/0.00 SER 6, ASN 1, GLY 0 ARG 3
SSSRSRNSSR 3.00 12.31 0.74 1123.15 Good -0.387 0% 10/100.00 0/0.00 SER 6, ASN 1, GLY 0 ARG 3
SSRSRNSSRN 3.00 12.31 0.74 1150.18 Good -0.443 0% 10/100.00 0/0.00 SER 5, ASN 2, GLY 0 ARG 3
RSRNSSRNST 3.00 12.31 0.74 1164.20 Good -0.413 0% 10/100.00 0/0.00 SER 4, THR 1, ASN 2, GLY 0 ARG 3
GSSRGTSPAR 2.00 12.01 0.49 975.03 Good -0.085 10% 8/80.00 2/20.00 SER 3, THR 1, GLY 2 ARG 2
AALALLLLDR 0.00 6.19 0.25 1068.33 Poor 0.765 80% 2/20.00 8/80.00 GLY 0 ARG 1, ASP 1
ALALLLLDRL 0.00 6.19 0.25 1110.41 Poor 0.904 80% 2/20.00 8/80.00 GLY 0 ARG 1, ASP 1
KKSAAEASKK 3.00 10.01 1.59 1047.22 Good -0.375 30% 7/70.00 3/30.00 SER 2, GLY 0 LYS 4, GLU 1
KSAAEASKKP 2.00 9.72 1.23 1016.16 Good -0.204 30% 6/60.00 4/40.00 SER 2, GLY 0 LYS 3, GLU 1
SAAEASKKPR 2.00 10.01 1.11 1044.18 Good -0.206 30% 6/60.00 4/40.00 SER 2, GLY 0 LYS 2, ARG 1, GLU 1
AAEASKKPRQ 2.00 10.01 1.23 1085.23 Good -0.224 30% 6/60.00 4/40.00 GLN 1, SER 1, GLY 0 LYS 2, ARG 1, GLU 1
AEASKKPRQK 3.00 10.30 1.60 1142.32 Good -0.354 20% 7/70.00 3/30.00 GLN 1, SER 1, GLY 0 LYS 3, ARG 1, GLU 1
EASKKPRQKR 4.00 11.10 1.84 1227.43 Good -0.486 10% 8/80.00 2/20.00 GLN 1, SER 1, GLY 0 LYS 3, ARG 2, GLU 1
ASKKPRQKRT 5.00 12.03 1.72 1199.42 Good -0.396 10% 8/80.00 2/20.00 GLN 1, SER 1, THR 1, GLY 0 LYS 3, ARG 2
SKKPRQKRTA 5.00 12.03 1.72 1199.42 Good -0.396 10% 8/80.00 2/20.00 GLN 1, SER 1, THR 1, GLY 0 LYS 3, ARG 2
KKPRQKRTAT 5.00 12.03 1.72 1213.45 Good -0.366 10% 8/80.00 2/20.00 GLN 1, THR 2, GLY 0 LYS 3, ARG 2
KPRQKRTATK 5.00 12.03 1.72 1213.45 Good -0.366 10% 8/80.00 2/20.00 GLN 1, THR 2, GLY 0 LYS 3, ARG 2
PRQKRTATKA 4.00 12.02 1.35 1156.35 Good -0.236 20% 7/70.00 3/30.00 GLN 1, THR 2, GLY 0 LYS 2, ARG 2
RQKRTATKAY 4.00 11.10 1.35 1222.41 Good -0.212 20% 7/70.00 3/30.00 GLN 1, THR 2, GLY 0 LYS 2, ARG 2
RQGTDYKHWP 1.50 8.94 0.88 1287.40 Good 0.133 10% 7/70.00 3/30.00 GLN 1, HIS 1, THR 1, GLY 1 LYS 1, ARG 1, ASP 1
FPPTEPKKDK 1.00 8.83 1.23 1186.37 Good -0.017 10% 6/60.00 4/40.00 THR 1, GLY 0 LYS 3, GLU 1, ASP 1
PPTEPKKDKK 2.00 9.55 1.59 1167.37 Good -0.295 0% 7/70.00 3/30.00 THR 1, GLY 0 LYS 4, GLU 1, ASP 1
PTEPKKDKKK 3.00 9.84 1.96 1198.43 Good -0.466 0% 8/80.00 2/20.00 THR 1, GLY 0 LYS 5, GLU 1, ASP 1
TEPKKDKKKK 4.00 10.01 2.33 1229.49 Good -0.637 0% 9/90.00 1/10.00 THR 1, GLY 0 LYS 6, GLU 1, ASP 1
EPKKDKKKKA 4.00 10.01 2.33 1199.46 Good -0.632 10% 8/80.00 2/20.00 GLY 0 LYS 6, GLU 1, ASP 1
PKKDKKKKAD 4.00 10.01 2.20 1185.43 Good -0.645 10% 8/80.00 2/20.00 GLY 0 LYS 6, ASP 2
KKDKKKKADE 3.00 9.71 2.33 1217.43 Good -0.781 10% 9/90.00 1/10.00 GLY 0 LYS 6, GLU 1, ASP 2
TQALPQRQKK 3.00 11.17 1.35 1197.40 Good -0.066 20% 7/70.00 3/30.00 GLN 3, THR 1, GLY 0 LYS 2, ARG 1
ALPQRQKKQQ 3.00 11.17 1.48 1224.43 Good -0.114 20% 7/70.00 3/30.00 GLN 4, GLY 0 LYS 2, ARG 1
ORF3a
SASKIITLKK 3.00 10.31 1.10 1088.36 Good 0.282 40% 6/60.00 4/40.00 SER 2, THR 1, GLY 0 LYS 3
ASKIITLKKR 4.00 11.27 1.35 1157.47 Good 0.185 40% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 3, ARG 1
SKIITLKKRW 4.00 11.27 1.35 1272.60 Good 0.379 40% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 3, ARG 1
KIITLKKRWQ 4.00 11.27 1.47 1313.65 Good 0.361 40% 6/60.00 4/40.00 GLN 1, THR 1, GLY 0 LYS 3, ARG 1
IITLKKRWQL 3.00 11.17 1.10 1298.64 Good 0.630 50% 5/50.00 5/50.00 GLN 1, THR 1, GLY 0 LYS 2, ARG 1
KKRWQLALSK 4.00 11.27 1.47 1257.55 Good 0.172 40% 6/60.00 4/40.00 GLN 1, SER 1, GLY 0 LYS 3, ARG 1
KRWQLALSKG 3.00 11.17 1.10 1186.42 Good 0.271 40% 6/60.00 4/40.00 GLN 1, SER 1, GLY 1 LYS 2, ARG 1
VRIIMRLWLC 2.00 10.38 0.49 1302.72 Poor 1.122 80% 2/20.00 8/80.00 GLY 0 ARG 2
RIIMRLWLCW 2.00 10.38 0.49 1389.80 Poor 1.225 80% 2/20.00 8/80.00 GLY 0 ARG 2
IIMRLWLCWK 2.00 9.55 0.61 1361.79 Poor 1.227 80% 2/20.00 8/80.00 GLY 0 LYS 1, ARG 1
IMRLWLCWKC 2.00 9.03 0.61 1351.77 Poor 1.201 80% 2/20.00 8/80.00 GLY 0 LYS 1, ARG 1
MRLWLCWKCR 3.00 9.72 0.86 1394.80 Good 0.920 70% 3/30.00 7/70.00 GLY 0 LYS 1, ARG 2
RLWLCWKCRS 3.00 9.72 0.86 1350.68 Good 0.793 60% 4/40.00 6/60.00 SER 1, GLY 0 LYS 1, ARG 2
LWLCWKCRSK 3.00 9.53 0.98 1322.66 Good 0.795 60% 4/40.00 6/60.00 SER 1, GLY 0 LYS 2, ARG 1
WLCWKCRSKN 3.00 9.53 0.98 1323.61 Good 0.565 50% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 1
LCWKCRSKNP 3.00 9.53 0.98 1234.51 Good 0.412 40% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 1
CWKCRSKNPL 3.00 9.53 0.98 1234.51 Good 0.412 40% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 1
KCRSKNPLLY 3.00 9.80 0.98 1221.49 Good 0.299 30% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 2, ARG 1
Orf1ab
IKRSDARTAP 2.00 10.84 0.86 1114.27 Good -0.042 30% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 1, ARG 2, ASP 1
KRSDARTAPH 2.50 10.84 1.00 1138.25 Good -0.209 20% 7/70.00 3/30.00 HIS 1, SER 1, THR 1, GLY 0 LYS 1, ARG 2, ASP 1
PVAYRKVLLR 3.00 11.01 0.86 1214.52 Good 0.482 50% 3/30.00 7/70.00 GLY 0 LYS 1, ARG 2
VAYRKVLLRK 4.00 11.10 1.22 1245.58 Good 0.311 50% 4/40.00 6/60.00 GLY 0 LYS 2, ARG 2
AYRKVLLRKN 4.00 11.10 1.22 1260.55 Good 0.129 40% 5/50.00 5/50.00 ASN 1, GLY 0 LYS 2, ARG 2
YRKVLLRKNG 4.00 11.10 1.22 1246.52 Good 0.098 30% 6/60.00 4/40.00 ASN 1, GLY 1 LYS 2, ARG 2
RKVLLRKNGN 4.00 12.02 1.22 1197.45 Good -0.058 30% 7/70.00 3/30.00 ASN 2, GLY 1 LYS 2, ARG 2
KVLLRKNGNK 4.00 11.27 1.35 1169.44 Good -0.056 30% 7/70.00 3/30.00 ASN 2, GLY 1 LYS 3, ARG 1
FEIKLAKKFD 1.00 8.83 1.23 1238.49 Good 0.301 50% 5/50.00 5/50.00 GLY 0 LYS 3, GLU 1, ASP 1
KTIQPRVEKK 3.00 10.30 1.60 1226.49 Good -0.084 20% 7/70.00 3/30.00 GLN 1, THR 1, GLY 0 LYS 3, ARG 1, GLU 1
TIQPRVEKKK 3.00 10.30 1.60 1226.49 Good -0.084 20% 7/70.00 3/30.00 GLN 1, THR 1, GLY 0 LYS 3, ARG 1, GLU 1
IQPRVEKKKL 3.00 10.30 1.60 1238.54 Good 0.060 30% 6/60.00 4/40.00 GLN 1, GLY 0 LYS 3, ARG 1, GLU 1
SGLKTILRKG 3.00 11.17 0.98 1072.32 Good 0.243 30% 7/70.00 3/30.00 SER 1, THR 1, GLY 2 LYS 2, ARG 1
LKTILRKGGR 4.00 12.02 1.22 1141.43 Good 0.146 30% 7/70.00 3/30.00 THR 1, GLY 2 LYS 2, ARG 2
KTILRKGGRT 4.00 12.02 1.22 1129.37 Good 0.002 20% 8/80.00 2/20.00 THR 2, GLY 2 LYS 2, ARG 2
GNFKVTKGKA 3.00 10.31 1.10 1049.24 Good 0.001 30% 7/70.00 3/30.00 THR 1, ASN 1, GLY 2 LYS 3
FKVTKGKAKK 5.00 10.61 1.84 1134.43 Good -0.137 30% 7/70.00 3/30.00 THR 1, GLY 1 LYS 5
KVTKGKAKKG 5.00 10.61 1.84 1044.31 Good -0.316 20% 8/80.00 2/20.00 THR 1, GLY 2 LYS 5
KGKAKKGAWN 4.00 10.49 1.47 1087.29 Good -0.169 30% 7/70.00 3/30.00 ASN 1, GLY 2 LYS 4
GGAKLKALNL 2.00 10.02 0.73 984.21 Good 0.314 50% 5/50.00 5/50.00 ASN 1, GLY 2 LYS 2
SKGLYRKCVK 4.00 10.04 1.35 1181.47 Good 0.140 30% 6/60.00 4/40.00 SER 1, GLY 1 LYS 3, ARG 1
KGLYRKCVKS 4.00 10.04 1.35 1181.47 Good 0.140 30% 6/60.00 4/40.00 SER 1, GLY 1 LYS 3, ARG 1
GLYRKCVKSR 4.00 10.32 1.22 1209.48 Good 0.138 30% 6/60.00 4/40.00 SER 1, GLY 1 LYS 2, ARG 2
GLLMPLKAPK 2.00 10.02 0.73 1067.40 Good 0.610 50% 3/30.00 7/70.00 GLY 1 LYS 2
QRKQDDKKIK 3.00 10.01 1.96 1286.50 Good -0.515 10% 9/90.00 1/10.00 GLN 2, GLY 0 LYS 4, ARG 1, ASP 2
RKQDDKKIKA 3.00 10.01 1.84 1229.45 Good -0.462 20% 8/80.00 2/20.00 GLN 1, GLY 0 LYS 4, ARG 1, ASP 2
KQDDKKIKAC 2.00 9.17 1.59 1176.40 Good -0.207 30% 7/70.00 3/30.00 GLN 1, GLY 0 LYS 4, ASP 2
DITFLKKDAP 0.00 6.31 0.73 1147.34 Good 0.306 40% 5/50.00 5/50.00 THR 1, GLY 0 LYS 2, ASP 2
MLAKALRKVP 3.00 11.17 0.98 1126.48 Good 0.420 60% 3/30.00 7/70.00 GLY 0 LYS 2, ARG 1
LAKALRKVPT 3.00 11.17 0.98 1096.38 Good 0.323 50% 4/40.00 6/60.00 THR 1, GLY 0 LYS 2, ARG 1
EAKTVLKKCK 3.00 9.65 1.59 1147.45 Good 0.043 40% 6/60.00 4/40.00 THR 1, GLY 0 LYS 4, GLU 1
AKTVLKKCKS 4.00 10.05 1.47 1105.41 Good 0.103 40% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 4
KTVLKKCKSA 4.00 10.05 1.47 1105.41 Good 0.103 40% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 4
KSAFYILPSI 1.00 8.94 0.37 1138.37 Poor 0.801 50% 3/30.00 7/70.00 SER 2, GLY 0 LYS 1
KAIVSTIQRK 3.00 11.17 1.10 1143.40 Good 0.214 40% 6/60.00 4/40.00 GLN 1, SER 1, THR 1, GLY 0 LYS 2, ARG 1
STIQRKYKGI 3.00 10.30 1.10 1193.41 Good 0.157 20% 7/70.00 3/30.00 GLN 1, SER 1, THR 1, GLY 1 LYS 2, ARG 1
TIQRKYKGIK 4.00 10.47 1.47 1234.5 Good 0.062 20% 7/70.00 3/30.00 GLN 1, THR 1, GLY 1 LYS 3, ARG 1
IQRKYKGIKI 4.00 10.47 1.47 1246.56 Good 0.216 30% 6/60.00 4/40.00 GLN 1, GLY 1 LYS 3, ARG 1
GARFYFYTSK 2.00 9.72 0.61 1239.40 Poor 0.403 30% 5/50.00 5/50.00 SER 1, THR 1, GLY 1 LYS 1, ARG 1
ARYMRSLKVP 3.00 11.01 0.86 1220.51 Good 0.309 40% 4/40.00 6/60.00 SER 1, GLY 0 LYS 1, ARG 2
GIEFLKRGDK 1.00 8.93 1.11 1162.35 Good 0.089 30% 7/70.00 3/30.00 GLY 2 LYS 2, ARG 1, GLU 1, ASP 1
DNLKTLLSLR 1.00 9.10 0.61 1172.39 Good 0.365 40% 6/60.00 4/40.00 SER 1, THR 1, ASN 1, GLY 0 LYS 1, ARG 1, ASP 1
YMSALNHTKK 2.50 9.72 0.88 1192.41 Good 0.197 30% 6/60.00 4/40.00 HIS 1, SER 1, THR 1, ASN 1, GLY 0 LYS 2
SALNHTKKWK 3.50 10.31 1.25 1212.42 Good 0.104 30% 7/70.00 3/30.00 HIS 1, SER 1, THR 1, ASN 1, GLY 0 LYS 3
ALNHTKKWKY 3.50 10.01 1.25 1288.52 Good 0.204 30% 6/60.00 4/40.00 HIS 1, THR 1, ASN 1, GLY 0 LYS 3
LNHTKKWKYP 3.50 10.01 1.25 1314.55 Good 0.245 20% 6/60.00 4/40.00 HIS 1, THR 1, ASN 1, GLY 0 LYS 3
NHTKKWKYPQ 3.50 10.01 1.37 1329.52 Good 0.053 10% 7/70.00 3/30.00 GLN 1, HIS 1, THR 1, ASN 1, GLY 0 LYS 3
HTKKWKYPQV 3.50 10.01 1.37 1314.55 Good 0.235 20% 6/60.00 4/40.00 GLN 1, HIS 1, THR 1, GLY 0 LYS 3
KKPASRELKV 3.00 10.30 1.47 1155.41 Good -0.071 30% 6/60.00 4/40.00 SER 1, GLY 0 LYS 3, ARG 1, GLU 1
KPASRELKVT 2.00 10.01 1.11 1128.34 Good 0.054 30% 6/60.00 4/40.00 SER 1, THR 1, GLY 0 LYS 2, ARG 1, GLU 1
YTPSFKKGAK 3.00 10.01 1.10 1126.32 Good 0.103 20% 6/60.00 4/40.00 SER 1, THR 1, GLY 1 LYS 3
PSFKKGAKLL 3.00 10.31 1.10 1088.36 Good 0.321 40% 5/50.00 5/50.00 SER 1, GLY 1 LYS 3
FKKGAKLLHK 4.50 10.49 1.61 1169.48 Good 0.167 40% 6/60.00 4/40.00 HIS 1, GLY 1 LYS 4
KKGAKLLHKP 4.50 10.49 1.61 1119.42 Good 0.060 30% 6/60.00 4/40.00 HIS 1, GLY 1 LYS 4
KGAKLLHKPI 3.50 10.31 1.25 1104.41 Good 0.339 40% 5/50.00 5/50.00 HIS 1, GLY 1 LYS 3
WCIRCLWSTK 2.00 9.03 0.61 1295.60 Poor 0.930 60% 4/40.00 6/60.00 SER 1, THR 1, GLY 0 LYS 1, ARG 1
CIRCLWSTKP 2.00 9.03 0.61 1206.50 Poor 0.777 50% 4/40.00 6/60.00 SER 1, THR 1, GLY 0 LYS 1, ARG 1
ANYAKPFLNK 2.00 9.7 0.73 1165.36 Good 0.261 33% 4/40.00 6/60.00 ASN 2, GLY 0 LYS 2
TNIVTRCLNR 2.00 10.38 0.49 1189.40 Good 0.356 40% 6/60.00 4/40.00 THR 2, ASN 2, GLY 0 ARG 2
CTFTRSTNSR 2.00 10.38 0.49 1172.29 Good 0.141 20% 8/80.00 2/20.00 SER 2, THR 3, ASN 1, GLY 0 ARG 2
TCMMCYKRNR 3.00 9.53 0.86 1305.64 Good 0.315 40% 5/50.00 5/50.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 2
MCYKRNRATR 4.00 10.92 1.10 1298.56 Good -0.032 30% 6/60.00 4/40.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 3
CYKRNRATRV 4.00 10.92 1.10 1266.49 Good -0.033 30% 6/60.00 4/40.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 3
YKRNRATRVE 3.00 10.91 1.23 1292.46 Good -0.251 20% 7/70.00 3/30.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 3, GLU 1
KRNRATRVEC 3.00 10.77 1.23 1232.43 Good -0.193 30% 7/70.00 3/30.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 3, GLU 1
RNRATRVECT 2.00 10.29 0.86 1205.36 Good -0.068 30% 7/70.00 3/30.00 THR 2, ASN 1, GLY 0 ARG 3, GLU 1
RDLSLQFKRP 2.00 10.84 0.98 1259.47 Good 0.187 30% 6/60.00 4/40.00 GLN 1, SER 1, GLY 0 LYS 1, ARG 2, ASP 1
SLQFKRPINP 2.00 11.01 0.74 1199.42 Good 0.387 30% 5/50.00 5/50.00 GLN 1, SER 1, ASN 1, GLY 0 LYS 1, ARG 1
HNIALIWNVK 1.50 9.11 0.51 1207.44 Poor 0.702 60% 4/40.00 6/60.00 HIS 1, ASN 2, GLY 0 LYS 1
LSEQLRKQIR 2.00 10.84 1.23 1270.50 Good 0.107 30% 7/70.00 3/30.00 GLN 2, SER 1, GLY 0 LYS 1, ARG 2, GLU 1
QLRKQIRSAA 3.00 12.01 1.11 1170.38 Good 0.063 40% 6/60.00 4/40.00 GLN 2, SER 1, GLY 0 LYS 1, ARG 2
LRKQIRSAAK 4.00 12.02 1.35 1170.42 Good -0.014 40% 6/60.00 4/40.00 GLN 1, SER 1, GLY 0 LYS 2, ARG 2
RKQIRSAAKK 5.00 12.03 1.72 1185.44 Good -0.283 30% 7/70.00 3/30.00 GLN 1, SER 1, GLY 0 LYS 3, ARG 2
KQIRSAAKKN 4.00 11.27 1.47 1143.36 Good -0.242 30% 7/70.00 3/30.00 GLN 1, SER 1, ASN 1, GLY 0 LYS 3, ARG 1
QIRSAAKKNN 3.00 11.17 1.10 1129.29 Good -0.203 30% 7/70.00 3/30.00 GLN 1, SER 1, ASN 2, GLY 0 LYS 2, ARG 1
AAKKNNLPFK 3.00 10.31 1.10 1130.36 Good 0.066 40% 5/50.00 5/50.00 ASN 2, GLY 0 LYS 3
KKNNLPFKLT 3.00 10.31 1.10 1202.46 Good 0.200 30% 6/60.00 4/40.00 THR 1, ASN 2, GLY 0 LYS 3
NNWLKQLIKV 2.00 10.02 0.86 1255.53 Poor 0.527 50% 5/50.00 5/50.00 GLN 1, ASN 2, GLY 0 LYS 2
LAYYFMRFRR 3.00 10.91 0.74 1422.72 Good 0.571 50% 3/30.00 7/70.00 GLY 0 ARG 3
AYYFMRFRRA 3.00 10.91 0.74 1380.64 Good 0.432 50% 3/30.00 7/70.00 GLY 0 ARG 3
YYFMRFRRAF 3.00 10.91 0.74 1456.74 Poor 0.580 50% 3/30.00 7/70.00 GLY 0 ARG 3
FMRFRRAFGE 2.00 11.70 0.86 1316.55 Good 0.324 50% 5/50.00 5/50.00 GLY 1 ARG 3, GLU 1
MRFRRAFGEY 2.00 10.75 0.86 1332.55 Good 0.241 40% 5/50.00 5/50.00 GLY 1 ARG 3, GLU 1
KEMYLKLRSD 1.00 8.83 1.11 1282.53 Good 0.115 30% 6/60.00 4/40.00 SER 1, GLY 0 LYS 2, ARG 1, GLU 1, ASP 1
YNRYLALYNK 2.00 9.55 0.61 1317.51 Poor 0.339 30% 4/40.00 6/60.00 ASN 2, GLY 0 LYS 1, ARG 1
RYLALYNKYK 3.00 9.83 0.98 1331.58 Good 0.300 30% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 2, ARG 1
FRKMAFPSGK 3.00 11.17 0.98 1168.43 Good 0.281 40% 5/50.00 5/50.00 SER 1, GLY 1 LYS 2, ARG 1
TANPKTPKYK 3.00 10.01 1.10 1147.34 Good -0.034 10% 6/60.00 4/40.00 THR 2, ASN 1, GLY 0 LYS 3
ANPKTPKYKF 3.00 10.01 1.10 1193.41 Good 0.119 20% 5/50.00 5/50.00 THR 1, ASN 1, GLY 0 LYS 3
PKTPKYKFVR 4.00 10.47 1.35 1263.55 Good 0.169 20% 5/50.00 5/50.00 THR 1, GLY 0 LYS 3, ARG 1
KTPKYKFVRI 4.00 10.47 1.35 1279.59 Good 0.277 30% 5/50.00 5/50.00 THR 1, GLY 0 LYS 3, ARG 1
RWFLNRFTTT 2.00 12.01 0.49 1341.54 Poor 0.569 40% 6/60.00 4/40.00 THR 3, ASN 1, GLY 0 ARG 2
FQSAVKRTIK 3.00 11.17 1.10 1177.41 Good 0.213 40% 6/60.00 4/40.00 GLN 1, SER 1, THR 1, GLY 0 LYS 2, ARG 1
SEVVLKKLKK 3.00 10.01 1.59 1171.49 Good 0.120 40% 6/60.00 4/40.00 SER 1, GLY 0 LYS 4, GLU 1
VVLKKLKKSL 4.00 10.49 1.47 1155.53 Good 0.354 50% 5/50.00 5/50.00 SER 1, GLY 0 LYS 4
VLKKLKKSLN 4.00 10.49 1.47 1170.51 Good 0.172 40% 6/60.00 4/40.00 SER 1, ASN 1, GLY 0 LYS 4
KKLKKSLNVA 4.00 10.49 1.47 1128.42 Good 0.033 40% 6/60.00 4/40.00 SER 1, ASN 1, GLY 0 LYS 4
DAAMQRKLEK 1.00 8.93 1.23 1189.40 Good -0.107 40% 6/60.00 4/40.00 GLN 1, GLY 0 LYS 2, ARG 1, GLU 1, ASP 1
AAMQRKLEKM 2.00 10.01 1.23 1205.51 Good 0.093 50% 5/50.00 5/50.00 GLN 1, GLY 0 LYS 2, ARG 1, GLU 1
MQRKLEKMAD 1.00 8.93 1.23 1249.52 Good -0.015 40% 6/60.00 4/40.00 GLN 1, GLY 0 LYS 2, ARG 1, GLU 1, ASP 1
YKQARSEDKR 2.00 9.72 1.48 1280.41 Good -0.440 10% 8/80.00 2/20.00 GLN 1, SER 1, GLY 0 LYS 2, ARG 2, GLU 1, ASP 1
KQARSEDKRA 2.00 10.00 1.48 1188.31 Good -0.505 20% 8/80.00 2/20.00 GLN 1, SER 1, GLY 0 LYS 2, ARG 2, GLU 1, ASP 1
QARSEDKRAK 2.00 10.00 1.48 1188.31 Good -0.505 20% 8/80.00 2/20.00 GLN 1, SER 1, GLY 0 LYS 2, ARG 2, GLU 1, ASP 1
MLFTMLRKLD 1.00 9.10 0.61 1267.62 Good 0.684 60% 4/40.00 6/60.00 THR 1, GLY 0 LYS 1, ARG 1, ASP 1
QDLKWARFPK 2.00 10.01 1.10 1288.52 Good 0.279 40% 5/50.00 5/50.00 GLN 1, GLY 0 LYS 2, ARG 1, ASP 1
DLKWARFPKS 2.00 10.01 0.98 1247.46 Good 0.297 40% 5/50.00 5/50.00 SER 1, GLY 0 LYS 2, ARG 1, ASP 1
LKWARFPKSD 2.00 10.01 0.98 1247.46 Good 0.297 40% 5/50.00 5/50.00 SER 1, GLY 0 LYS 2, ARG 1, ASP 1
KGFCDLKGKY 2.00 9.17 1.10 1158.39 Good 0.225 30% 6/60.00 4/40.00 GLY 2 LYS 3, ASP 1
GVSAARLTPC 1.00 8.60 0.25 974.15 Poor 0.501 50% 4/40.00 6/60.00 SER 1, THR 1, GLY 1 ARG 1
GFAKFLKTNC 2.00 9.36 0.73 1128.36 Poor 0.481 50% 5/50.00 5/50.00 THR 1, ASN 1, GLY 1 LYS 2
KTNCCRFQEK 2.00 8.98 1.23 1256.47 Good 0.068 30% 7/70.00 3/30.00 GLN 1, THR 1, ASN 1, GLY 0 LYS 2, ARG 1, GLU 1
PHISRQRLTK 3.50 12.01 1.13 1235.46 Good 0.134 20% 7/70.00 3/30.00 GLN 1, HIS 1, SER 1, THR 1, GLY 0 LYS 1, ARG 2
HISRQRLTKY 3.50 11.01 1.13 1301.52 Good 0.158 20% 7/70.00 3/30.00 GLN 1, HIS 1, SER 1, THR 1, GLY 0 LYS 1, ARG 2
ISRQRLTKYT 3.00 11.01 0.98 1265.48 Good 0.171 20% 7/70.00 3/30.00 GLN 1, SER 1, THR 2, GLY 0 LYS 1, ARG 2
SRQRLTKYTM 3.00 11.01 0.98 1283.52 Good 0.114 20% 7/70.00 3/30.00 GLN 1, SER 1, THR 2, GLY 0 LYS 1, ARG 2
RQRLTKYTMA 3.00 11.01 0.98 1267.52 Good 0.149 30% 6/60.00 4/40.00 GLN 1, THR 2, GLY 0 LYS 1, ARG 2
GERVRQALLK 2.00 10.84 1.11 1169.39 Good 0.106 40% 6/60.00 4/40.00 GLN 1, GLY 1 LYS 1, ARG 2, GLU 1
RVRQALLKTV 3.00 12.01 0.98 1183.46 Good 0.318 50% 5/50.00 5/50.00 GLN 1, THR 1, GLY 0 LYS 1, ARG 2
KPYIKWDLLK 2.00 9.55 1.10 1303.61 Good 0.539 40% 4/40.00 6/60.00 GLY 0 LYS 3, ASP 1
RLKLFDRYFK 3.00 10.29 1.22 1385.68 Good 0.317 40% 5/50.00 5/50.00 GLY 0 LYS 2, ARG 2, ASP 1
KLFDRYFKYW 2.00 9.55 0.98 1465.72 Good 0.569 40% 4/40.00 6/60.00 GLY 0 LYS 2, ARG 1, ASP 1
FPFNKWGKAR 3.00 11.17 0.98 1250.47 Good 0.327 40% 5/50.00 5/50.00 ASN 1, GLY 1 LYS 2, ARG 1
KWGKARLYYD 2.00 9.55 0.98 1299.50 Good 0.242 30% 5/50.00 5/50.00 GLY 1 LYS 2, ARG 1, ASP 1
YAISAKNRAR 3.00 11.01 0.86 1149.32 Good 0.004 40% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 1, ARG 2
AISAKNRART 3.00 12.01 0.86 1087.25 Good -0.066 40% 6/60.00 4/40.00 SER 1, THR 1, ASN 1, GLY 0 LYS 1, ARG 2
KNRARTVAGV 3.00 12.01 0.86 1071.25 Good -0.029 40% 6/60.00 4/40.00 THR 1, ASN 1, GLY 1 LYS 1, ARG 2
NRQFHQKLLK 3.50 11.17 1.37 1311.55 Good 0.129 30% 7/70.00 3/30.00 GLN 2, HIS 1, ASN 1, GLY 0 LYS 2, ARG 1
RQFHQKLLKS 3.50 11.17 1.37 1284.53 Good 0.185 30% 7/70.00 3/30.00 GLN 2, HIS 1, SER 1, GLY 0 LYS 2, ARG 1
RIMASLVLAR 2.00 12.01 0.49 1129.44 Poor 0.621 70% 3/30.00 7/70.00 SER 1, GLY 0 ARG 2
RNLQHRLYEC 1.50 8.57 0.89 1331.52 Good 0.255 30% 6/60.00 4/40.00 GLN 1, HIS 1, ASN 1, GLY 0 ARG 2, GLU 1
RLYECLYRNR 2.00 9.36 0.86 1385.61 Good 0.259 30% 5/50.00 5/50.00 ASN 1, GLY 0 ARG 3, GLU 1
SLRCGACIRR 3.00 10.43 0.74 1134.40 Good 0.382 50% 5/50.00 5/50.00 SER 1, GLY 1 ARG 3
RCGACIRRPF 3.00 10.43 0.74 1178.45 Good 0.467 50% 4/40.00 6/60.00 GLY 1 ARG 3
CGACIRRPFL 2.00 9.10 0.49 1135.42 Poor 0.738 60% 3/30.00 7/70.00 GLY 1 ARG 2
GACIRRPFLC 2.00 9.10 0.49 1135.42 Poor 0.738 60% 3/30.00 7/70.00 GLY 1 ARG 2
ACIRRPFLCC 2.00 8.82 0.49 1181.52 Poor 0.892 70% 2/20.00 8/80.00 GLY 0 ARG 2
CIRRPFLCCK 3.00 9.26 0.86 1238.61 Good 0.762 60% 3/30.00 7/70.00 GLY 0 LYS 1, ARG 2
IRRPFLCCKC 3.00 9.26 0.86 1238.61 Good 0.762 60% 3/30.00 7/70.00 GLY 0 LYS 1, ARG 2
RRPFLCCKCC 3.00 9.02 0.86 1228.60 Good 0.736 60% 3/30.00 7/70.00 GLY 0 LYS 1, ARG 2
MSYYCKSHKP 2.50 9.17 0.88 1243.47 Good 0.348 20% 5/50.00 5/50.00 HIS 1, SER 2, GLY 0 LYS 2
ANTCTERLKL 1.00 8.57 0.74 1148.35 Good 0.253 40% 6/60.00 4/40.00 THR 2, ASN 1, GLY 0 LYS 1, ARG 1, GLU 1
SWEVGKPRPP 1.00 9.10 0.74 1152.32 Good 0.295 20% 5/50.00 5/50.00 SER 1, GLY 1 LYS 1, ARG 1, GLU 1
VGKPRPPLNR 3.00 12.01 0.86 1133.36 Good 0.147 20% 5/50.00 5/50.00 ASN 1, GLY 1 LYS 1, ARG 2
GKPRPPLNRN 3.00 12.01 0.86 1148.33 Good -0.035 10% 6/60.00 4/40.00 ASN 2, GLY 1 LYS 1, ARG 2
KALKYLPIDK 2.00 9.55 1.10 1188.48 Good 0.345 40% 4/40.00 6/60.00 GLY 0 LYS 3, ASP 1
DKCSRIIPAR 2.00 9.55 0.86 1158.39 Good 0.235 40% 5/50.00 5/50.00 SER 1, GLY 0 LYS 1, ARG 2, ASP 1
KCSRIIPARA 3.00 10.87 0.86 1114.38 Good 0.343 50% 4/40.00 6/60.00 SER 1, GLY 0 LYS 1, ARG 2
CSRIIPARAR 3.00 11.71 0.74 1142.39 Good 0.341 50% 4/40.00 6/60.00 SER 1, GLY 0 ARG 3
SRIIPARARV 3.00 12.31 0.74 1138.38 Good 0.309 50% 4/40.00 6/60.00 SER 1, GLY 0 ARG 3
RIIPARARVE 2.00 11.70 0.86 1180.42 Good 0.249 50% 4/40.00 6/60.00 GLY 0 ARG 3, GLU 1
SVVNARLRAK 3.00 12.01 0.86 1113.33 Good 0.111 50% 5/50.00 5/50.00 SER 1, ASN 1, GLY 0 LYS 1, ARG 2
VVNARLRAKH 3.50 12.01 1.00 1163.39 Good 0.128 50% 5/50.00 5/50.00 HIS 1, ASN 1, GLY 0 LYS 1, ARG 2
VNARLRAKHY 3.50 11.01 1.00 1227.44 Good 0.102 40% 5/50.00 5/50.00 HIS 1, ASN 1, GLY 0 LYS 1, ARG 2
NARLRAKHYV 3.50 11.01 1.00 1227.44 Good 0.102 40% 5/50.00 5/50.00 HIS 1, ASN 1, GLY 0 LYS 1, ARG 2
PAPRTLLTKG 2.00 11.01 0.61 1053.27 Good 0.367 30% 5/50.00 5/50.00 THR 2, GLY 1 LYS 1, ARG 1
APRTLLTKGT 2.00 11.01 0.61 1057.26 Good 0.321 30% 6/60.00 4/40.00 THR 3, GLY 1 LYS 1, ARG 1
FNSVCRLMKT 2.00 9.55 0.61 1198.48 Good 0.510 50% 5/50.00 5/50.00 SER 1, THR 1, ASN 1, GLY 0 LYS 1, ARG 1
FLGTCRRCPA 2.00 9.10 0.49 1123.37 Good 0.584 50% 4/40.00 6/60.00 THR 1, GLY 1 ARG 2
DNKLKAHKDK 2.50 9.55 1.61 1196.37 Good -0.396 20% 8/80.00 2/20.00 HIS 1, ASN 1, GLY 0 LYS 4, ASP 2
KLKAHKDKSA 3.50 10.01 1.61 1125.34 Good -0.232 30% 7/70.00 3/30.00 HIS 1, SER 1, GLY 0 LYS 4, ASP 1
FLTRNPAWRK 3.00 12.01 0.86 1288.52 Good 0.342 40% 5/50.00 5/50.00 THR 1, ASN 1, GLY 0 LYS 1, ARG 2
RNPAWRKAVF 3.00 12.01 0.86 1244.47 Good 0.299 50% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 1, ARG 2
GIPKDMTYRR 2.00 10.00 0.86 1236.46 Good 0.119 20% 6/60.00 4/40.00 THR 1, GLY 1 LYS 1, ARG 2, ASP 1
DMTYRRLISM 1.00 9.10 0.49 1285.55 Good 0.435 40% 5/50.00 5/50.00 SER 1, THR 1, GLY 0 ARG 2, ASP 1
GNPKAIKCVP 2.00 9.36 0.73 1026.27 Good 0.373 40% 4/40.00 6/60.00 ASN 1, GLY 1 LYS 2
WNTFTRLQSL 1.00 10.11 0.37 1265.43 Poor 0.609 40% 6/60.00 4/40.00 GLN 1, SER 1, THR 2, ASN 1, GLY 0 ARG 1
ELWAKRNIKP 2.00 10.01 1.11 1254.50 Good 0.255 40% 5/50.00 5/50.00 ASN 1, GLY 0 LYS 2, ARG 1, GLU 1
LWAKRNIKPV 3.00 11.17 0.98 1224.52 Good 0.441 50% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 2, ARG 1
WAKRNIKPVP 3.00 11.17 0.98 1208.47 Good 0.343 40% 4/40.00 6/60.00 ASN 1, GLY 0 LYS 2, ARG 1
RNIKPVPEVK 2.00 10.01 1.11 1179.43 Good 0.145 30% 5/50.00 5/50.00 ASN 1, GLY 0 LYS 2, ARG 1, GLU 1
LLIGLAKRFK 3.00 11.17 0.98 1158.50 Good 0.601 60% 4/40.00 6/60.00 GLY 1 LYS 2, ARG 1
GLAKRFKESP 2.00 10.01 1.11 1132.33 Good 0.085 30% 6/60.00 4/40.00 SER 1, GLY 1 LYS 2, ARG 1, GLU 1
KMQRMLLEKC 2.00 9.72 1.23 1279.66 Good 0.355 50% 5/50.00 5/50.00 GLN 1, GLY 0 LYS 2, ARG 1, GLU 1
VLRQWLPTGT 1.00 10.11 0.37 1170.38 Poor 0.688 40% 5/50.00 5/50.00 GLN 1, THR 2, GLY 1 ARG 1
DMSKFPLKLR 2.00 10.01 0.98 1234.53 Good 0.334 40% 5/50.00 5/50.00 SER 1, GLY 0 LYS 2, ARG 1, ASP 1
MSKFPLKLRG 3.00 11.17 0.98 1176.49 Good 0.411 40% 5/50.00 5/50.00 SER 1, GLY 1 LYS 2, ARG 1
SKFPLKLRGT 3.00 11.17 0.98 1146.40 Good 0.314 30% 6/60.00 4/40.00 SER 1, THR 1, GLY 1 LYS 2, ARG 1
KFPLKLRGTA 3.00 11.17 0.98 1130.40 Good 0.349 40% 5/50.00 5/50.00 THR 1, GLY 1 LYS 2, ARG 1
MILSLLSKGR 2.00 11.01 0.61 1117.42 Good 0.605 50% 5/50.00 5/50.00 SER 2, GLY 1 LYS 1, ARG 1
LLSKGRLIIR 3.00 12.01 0.86 1168.49 Good 0.565 50% 5/50.00 5/50.00 SER 1, GLY 1 LYS 1, ARG 2
GRLIIRENNR 2.00 11.70 0.86 1240.43 Good 0.043 30% 7/70.00 3/30.00 ASN 2, GLY 1 ARG 3, GLU 1
RLIIRENNRV 2.00 11.70 0.86 1282.51 Good 0.165 40% 6/60.00 4/40.00 ASN 2, GLY 0 ARG 3, GLU 1
ORF6
LIIKNLSKSL 2.00 10.02 0.73 1128.42 Good 0.604 50% 5/50.00 5/50.00 SER 2, ASN 1, GLY 0 LYS 2
ORF7a
HVYQLRARSV 2.50 10.84 0.76 1228.42 Good 0.326 40% 5/50.00 5/50.00 GLN 1, HIS 1, SER 1, GLY 0 ARG 2
QLRARSVSPK 3.00 12.01 0.98 1141.34 Good 0.064 30% 6/60.00 4/40.00 GLN 1, SER 2, GLY 0 LYS 1, ARG 2
RARSVSPKLF 3.00 12.01 0.98 1160.39 Good 0.265 40% 5/50.00 5/50.00 SER 2, GLY 0 LYS 1, ARG 2
RSVSPKLFIR 3.00 12.01 0.86 1202.47 Good 0.414 40% 5/50.00 5/50.00 SER 2, GLY 0 LYS 1, ARG 2
ITLCFTLKRK 3.00 10.07 0.98 1222.56 Good 0.606 50% 5/50.00 5/50.00 THR 2, GLY 0 LYS 2, ARG 1
TLCFTLKRKT 3.00 10.07 0.98 1210.51 Good 0.452 40% 6/60.00 4/40.00 THR 3, GLY 0 LYS 2, ARG 1
LCFTLKRKTE 2.00 9.36 1.11 1238.52 Good 0.362 40% 6/60.00 4/40.00 THR 2, GLY 0 LYS 2, ARG 1, GLU 1
ORF8
SKWYIRVGAR 3.00 11.01 0.86 1235.46 Good 0.349 40% 5/50.00 5/50.00 SER 1, GLY 1 LYS 1, ARG 2
KWYIRVGARK 4.00 11.10 1.22 1276.55 Good 0.254 40% 5/50.00 5/50.00 GLY 1 LYS 2, ARG 2
YIRVGARKSA 3.00 11.01 0.86 1120.32 Good 0.155 40% 5/50.00 5/50.00 SER 1, GLY 1 LYS 1, ARG 2

Evaluation of membrane-binding potential of CPPs

One of the principal criterions to design a potent CPP is the prediction of membrane-binding ability and cellular localization. Hence, the Boman index of each peptide was estimated using APD3 web server. The values higher than 2.48 kcal/ mol define high binding potential. For example, SSRSRNSSRN peptide derived from N-protein had the highest Boman index amongst all of the predicted CPPs (Boman Index: 7.5). Moreover, the D factor was calculated for each peptide based on net charge and μH. According to the computed D factor, CPPs can be divided into three different categories including D < 0.68 as non-lipid binding (helix/random coil), 0.68 < D < 1.34 as possible lipid-binding helix, and D > 1.34 as lipid-binding helix [46]. Additionally, the cellular localization of each CPP was evaluated by TMHMM server to determine the probability of CPPs which can enter the cell. The results of membrane-binding potential and cellular localization of CPPs were indicated in Table 3. Also, some examples of TMHMM prediction results were illustrated in Fig 1.

Table 3. Membrane-binding potential and cellular localization of CPPs.

Epitope Protein-binding Potential (Boman index) Hydrophobic moment (μH) Membrane-binding potential (D factor) Cellular localization by TMHMM Server Total probability of N-in by TMHMM Server
S protein
NLTTRTQLPP 2.49 0.148 0.469 Inside 0.47745
RFQTLLALHR 2.3 0.342 1.147 Inside 0.48730
YLQPRTFLLK 1.09 0.225 0.8724 Outside 0.34806
SVYAWNRKRI 3.24 0.224 1.196 Inside 0.89106
YAWNRKRISN 4.31 0.162 1.142 Inside 0.91633
AWNRKRISNC 4.17 0.259 1.234 Inside 0.92814
WNRKRISNCV 3.95 0.300 1.273 Inside 0.91952
RQIAPGQTGK 2.55 0.081 0.736 Inside 0.71347
YNYLYRLFRK 2.96 0.690 1.641 Inside 0.74845
YLYRLFRKSN 3.28 0.567 1.525 Inside 0.69159
YRLFRKSNLK 3.82 0.275 1.5796 Inside 0.75816
RLFRKSNLKP 3.81 0.322 1.623 Inside 0.61242
RKSNLKPFER 4.98 0.368 1.337 Inside 0.70344
KKSTNLVKNK 3.24 0.355 1.655 Inside 0.87353
KSTNLVKNKC 2.56 0.377 1.345 Inside 0.85159
HADQLTPTWR 2.99 0.468 0.606 Inside 0.56152
YQTQTNSPRR 5.62 0.323 0.964 Inside 0.89727
TQTNSPRRAR 6.36 0.219 1.196 Inside 0.92515
TNSPRRARSV 5.49 0.292 1.265 Inside 0.85479
NSPRRARSVA 5.05 0.329 1.300 Inside 0.82719
PRRARSVASQ 4.94 0.302 1.275 Inside 0.83300
KQIYKTPPIK 1.5 0.194 1.173 Inside 0.77001
SQILPDPSKP 1.67 0.404 0.381 Outside 0.15396
RLITGRLQSL 2.07 0.502 0.897 Inside 0.47579
M protein
NRNRFLYIIK 3.1 0.346 1.316 Inside 0.81684
RNRFLYIIKL 1.95 0.326 1.297 Inside 0.66588
YIIKLIFLWL -2.91 0.488 0.790 Outside 0.20334
KLIFLWLLWP -2.66 0.304 0.616 Outside 0.07090
FIASFRLFAR 1.08 0.522 1.152 Outside 0.32626
ASFRLFARTR 3.62 0.343 1.313 Inside 0.67733
SFRLFARTRS 4.14 0.366 1.335 Inside 0.64273
FRLFARTRSM 3.56 0.350 1.320 Inside 0.67490
RLFARTRSMW 3.63 0.589 1.546 Inside 0.78030
FARTRSMWSF 2.67 0.277 0.921 Inside 0.62650
HGTILTRPLL 0.41 0.254 0.569 Outside 0.24888
GAVILRGHLR 1.2 0.323 0.964 Outside 0.42609
RIAGHHLGRC 2.43 0.423 1.059 Inside 0.64698
YSRYRIGNYK 3.99 0.141 1.123 Inside 0.89352
N protein
PQNQRNAPRI 4.74 0.243 0.889 Inside 0.80935
ERSGARSKQR 6.67 0.143 1.124 Inside 0.89938
RSGARSKQRR 7.48 0.114 1.757 Inside 0.94959
SGARSKQRRP 5.99 0.188 1.497 Inside 0.85655
GARSKQRRPQ 6.2 0.185 1.494 Inside 0.89829
ARSKQRRPQG 6.2 0.185 1.494 Inside 0.89829
RSKQRRPQGL 5.89 0.280 1.584 Inside 0.83224
SKQRRPQGLP 4.4 0.245 1.221 Inside 0.60745
KQRRPQGLPN 4.72 0.292 1.265 Inside 0.69009
RRPQGLPNNT 4.53 0.432 1.067 Inside 0.62344
QIGYYRRATR 4.54 0.383 1.351 Inside 0.93350
IGYYRRATRR 5.48 0.445 1.740 Inside 0.95251
GYYRRATRRI 5.48 0.564 1.852 Inside 0.95251
YYRRATRRIR 7.07 0.593 2.209 Inside 0.98065
YRRATRRIRG 6.96 0.565 2.183 Inside 0.96774
RRATRRIRGG 6.85 0.478 2.101 Inside 0.94656
RATRRIRGGD 6.23 0.365 1.334 Inside 0.90958
TRRIRGGDGK 5.38 0.314 1.286 Inside 0.85699
RIRGGDGKMK 3.95 0.321 1.293 Inside 0.80713
GKMKDLSPRW 2.76 0.523 1.153 Inside 0.53414
SQASSRSSSR 5.39 0.171 0.821 Inside 0.70911
ASSRSSSRSR 6.33 0.114 1.097 Inside 0.79316
SSRSSSRSRN 7.18 0.180 1.159 Inside 0.81855
SRSSSRSRNS 7.18 0.183 1.162 Inside 0.81855
RSSSRSRNSS 7.18 0.179 1.158 Inside 0.81855
SSSRSRNSSR 7.18 0.165 1.145 Inside 0.81855
SSRSRNSSRN 7.5 0.192 1.171 Inside 0.86592
RSRNSSRNST 7.42 0.194 1.173 Inside 0.90573
GSSRGTSPAR 3.89 0.156 0.807 Inside 0.52191
AALALLLLDR -0.63 0.125 0.118 Outside 0.15140
ALALLLLDRL -0.95 0.288 0.271 Outside 0.09169
KKSAAEASKK 3.03 0.277 1.251 Inside 0.85101
KSAAEASKKP 2.48 0.385 1.023 Inside 0.66436
SAAEASKKPR 3.42 0.284 0.928 Inside 0.71820
AAEASKKPRQ 3.63 0.306 0.948 Inside 0.79675
AEASKKPRQK 4.36 0.248 1.224 Inside 0.86032
EASKKPRQKR 6.04 0.165 1.475 Inside 0.92101
ASKKPRQKRT 5.61 0.096 1.740 Inside 0.94440
SKKPRQKRTA 5.61 0.060 1.706 Inside 0.94440
KKPRQKRTAT 5.53 0.087 1.732 Inside 0.96166
KPRQKRTATK 5.53 0.094 1.738 Inside 0.96166
PRQKRTATKA 4.79 0.202 1.510 Inside 0.94133
RQKRTATKAY 4.81 0.311 1.423 Inside 0.96930
RQGTDYKHWP 3.88 0.330 0.641 Inside 0.65834
FPPTEPKKDK 3.17 0.088 0.413 Outside 0.35377
PPTEPKKDKK 4.02 0.225 0.872 Inside 0.58321
PTEPKKDKKK 4.58 0.236 1.212 Inside 0.80442
TEPKKDKKKK 5.13 0.110 1.527 Inside 0.92302
EPKKDKKKKA 4.7 0.147 1.458 Inside 0.90887
PKKDKKKKAD 4.89 0.184 1.493 Inside 0.90457
KKDKKKKADE 5.57 0.133 1.115 Inside 0.93664
TQALPQRQKK 3.84 0.308 0.950 Inside 0.86179
ALPQRQKKQQ 4.14 0.243 1.219 Inside 0.86346
ORF3a
SASKIITLKK 0.94 0.243 1.219 Inside 0.75288
ASKIITLKKR 2.09 0.314 1.452 Inside 0.86807
SKIITLKKRW 2.04 0.492 1.784 Inside 0.86095
KIITLKKRWQ 2.25 0.485 1.777 Inside 0.90139
IITLKKRWQL 1.21 0.320 1.292 Inside 0.76749
KKRWQLALSK 2.65 0.335 1.636 Inside 0.79484
KRWQLALSKG 2 0.407 1.704 Inside 0.62646
VRIIMRLWLC 0.01 0.429 1.064 Inside 0.63104
RIIMRLWLCW 0.18 0.600 1.226 Inside 0.64902
IIMRLWLCWK -0.74 0.517 1.148 Inside 0.61977
IMRLWLCWKC -0.38 0.347 0.987 Inside 0.68839
MRLWLCWKCR 1.59 0.156 1.137 Inside 0.81046
RLWLCWKCRS 2.17 0.178 1.158 Inside 0.77912
LWLCWKCRSK 1.23 0.223 1.200 Inside 0.74360
WLCWKCRSKN 2.39 0.080 1.065 Inside 0.85315
LCWKCRSKNP 2.62 0.198 1.176 Inside 0.78747
CWKCRSKNPL 2.62 0.236 1.212 Inside 0.78747
KCRSKNPLLY 2.5 0.134 1.116 Inside 0.67216
Orf1ab
IKRSDARTAP 4.15 0.217 0.864 Inside 0.82802
KRSDARTAPH 5.11 0.039 0.696 Inside 0.81819
PVAYRKVLLR 1.58 0.313 1.285 Inside 0.60465
VAYRKVLLRK 2.13 0.208 1.516 Inside 0.80830
AYRKVLLRKN 3.2 0.224 1.531 Inside 0.85177
YRKVLLRKNG 3.29 0.241 1.547 Inside 0.79673
RKVLLRKNGN 3.94 0.200 1.508 Inside 0.80930
KVLLRKNGNK 3 0.234 1.540 Inside 0.77882
FEIKLAKKFD 1.45 0.412 0.718 Inside 0.48733
KTIQPRVEKK 3.75 0.289 1.262 Inside 0.88737
TIQPRVEKKK 3.75 0.161 1.141 Inside 0.88737
IQPRVEKKKL 3 0.103 1.087 Inside 0.78283
SGLKTILRKG 1.53 0.531 1.491 Inside 0.54410
LKTILRKGGR 2.68 0.625 1.91 Inside 0.71606
KTILRKGGRT 3.43 0.472 1.765 Inside 0.84956
GNFKVTKGKA 1.51 0.141 1.123 Inside 0.68622
FKVTKGKAKK 2.05 0.306 1.938 Inside 0.87336
KVTKGKAKKG 2.25 0.248 1.884 Inside 0.88617
KGKAKKGAWN 2.1 0.165 1.475 Inside 0.82016
GGAKLKALNL -0.25 0.094 0.748 Outside 0.30336
SKGLYRKCVK 2.39 0.571 1.859 Inside 0.83410
KGLYRKCVKS 2.39 0.574 1.861 Inside 0.83410
GLYRKCVKSR 3.32 0.456 1.750 Inside 0.85872
GLLMPLKAPK -0.87 0.305 0.947 Outside 0.15005
QRKQDDKKIK 6.07 0.223 1.2 Inside 0.95960
RKQDDKKIKA 5.33 0.227 1.204 Inside 0.94962
KQDDKKIKAC 3.71 0.420 1.056 Inside 0.92486
DITFLKKDAP 1.64 0.215 0.202 Inside 0.41432
MLAKALRKVP 0.61 0.641 1.595 Inside 0.57064
LAKALRKVPT 1.1 0.661 1.613 Inside 0.61542
EAKTVLKKCK 1.95 0.508 1.469 Inside 0.87393
AKTVLKKCKS 1.61 0.525 1.815 Inside 0.87056
KTVLKKCKSA 1.61 0.517 1.808 Inside 0.87056
KSAFYILPSI -0.7 0.344 0.654 Outside 0.26827
KAIVSTIQRK 2.18 0.455 1.419 Inside 0.89354
STIQRKYKGI 2.68 0.547 1.506 Inside 0.87849
TIQRKYKGIK 2.9 0.533 1.823 Inside 0.93039
IQRKYKGIKI 2.15 0.356 1.656 Inside 0.90786
GARFYFYTSK 1.8 0.190 0.839 Inside 0.58982
ARYMRSLKVP 2.58 0.412 1.378 Inside 0.72252
GIEFLKRGDK 2.68 0.472 0.775 Inside 0.47568
DNLKTLLSLR 2.21 0.363 0.672 Outside 0.37423
YMSALNHTKK 1.94 0.373 1.012 Inside 0.74444
SALNHTKKWK 2.48 0.407 1.374 Inside 0.76830
ALNHTKKWKY 2.15 0.311 1.283 Inside 0.81149
LNHTKKWKYP 2.34 0.291 1.264 Inside 0.69869
NHTKKWKYPQ 3.38 0.362 1.331 Inside 0.82885
HTKKWKYPQV 2.31 0.217 1.194 Inside 0.78945
KKPASRELKV 3.1 0.317 1.289 Inside 0.71408
KPASRELKVT 2.8 0.356 0.996 Inside 0.66033
YTPSFKKGAK 1.7 0.425 1.391 Inside 0.60188
PSFKKGAKLL 0.44 0.488 1.450 Outside 0.28608
FKKGAKLLHK 1.12 0.639 1.923 Inside 0.56438
KKGAKLLHKP 1.42 0.544 1.833 Inside 0.55042
KGAKLLHKPI 0.37 0.590 1.546 Inside 0.43639
WCIRCLWSTK 0.93 0.223 0.870 Inside 0.79516
CIRCLWSTKP 1.17 0.298 0.941 Inside 0.70751
ANYAKPFLNK 1.64 0.408 1.045 Inside 0.53508
TNIVTRCLNR 3.3 0.562 1.190 Inside 0.89302
CTFTRSTNSR 4.67 0.140 0.792 Inside 0.87592
TCMMCYKRNR 3.74 0.228 1.205 Inside 0.96377
MCYKRNRATR 5.42 0.232 1.539 Inside 0.97415
CYKRNRATRV 5.25 0.084 1.399 Inside 0.97070
YKRNRATRVE 6.06 0.094 1.078 Inside 0.95678
KRNRATRVEC 5.92 0.077 1.062 Inside 0.96153
RNRATRVECT 5.62 0.032 0.690 Inside 0.95230
RDLSLQFKRP 4.02 0.454 1.088 Inside 0.49113
SLQFKRPINP 2.32 0.319 0.961 Outside 0.39508
HNIALIWNVK 0.05 0.188 0.507 Inside 0.60778
LSEQLRKQIR 4.19 0.686 1.307 Inside 0.75607
QLRKQIRSAA 3.64 0.527 1.487 Inside 0.86742
LRKQIRSAAK 3.64 0.621 1.906 Inside 0.89068
RKQIRSAAKK 4.68 0.451 2.075 Inside 0.95767
KQIRSAAKKN 3.86 0.413 1.709 Inside 0.93797
QIRSAAKKNN 3.97 0.319 1.291 Inside 0.92032
AAKKNNLPFK 1.84 0.198 1.176 Inside 0.63148
KKNNLPFKLT 1.96 0.238 1.214 Inside 0.54962
NNWLKQLIKV 0.87 0.728 1.347 Inside 0.65372
LAYYFMRFRR 3 0.469 1.432 Inside 0.71897
AYYFMRFRRA 3.31 0.303 1.276 Inside 0.82144
YYFMRFRRAF 3.19 0.478 1.441 Inside 0.72131
FMRFRRAFGE 3.75 0.600 1.226 Inside 0.52960
MRFRRAFGEY 4.06 0.504 1.135 Inside 0.70022
KEMYLKLRSD 3.29 0.265 0.580 Inside 0.65269
YNRYLALYNK 2.25 0.500 1.132 Inside 0.74756
RYLALYNKYK 2.14 0.392 1.360 Inside 0.80198
FRKMAFPSGK 1.83 0.313 1.285 Inside 0.47019
TANPKTPKYK 2.67 0.009 0.998 Inside 0.77069
ANPKTPKYKF 2.12 0.169 1.149 Inside 0.63687
PKTPKYKFVR 2.72 0.292 1.595 Inside 0.73005
KTPKYKFVRI 2.23 0.323 1.624 Inside 0.82704
RWFLNRFTTT 3.09 0.360 0.999 Inside 0.70898
FQSAVKRTIK 2.37 0.618 1.573 Inside 0.83635
SEVVLKKLKK 1.44 0.508 1.469 Inside 0.64932
VVLKKLKKSL 0.27 0.612 1.897 Inside 0.55852
VLKKLKKSLN 1.34 0.757 2.034 Inside 0.62971
KKLKKSLNVA 1.65 0.534 1.824 Inside 0.74490
DAAMQRKLEK 3.62 0.368 0.677 Inside 0.81844
AAMQRKLEKM 2.51 0.518 1.148 Inside 0.83287
MQRKLEKMAD 3.56 0.646 0.939 Inside 0.82292
YKQARSEDKR 6.37 0.233 0.879 Inside 0.92834
KQARSEDKRA 6.17 0.232 0.879 Inside 0.92248
QARSEDKRAK 6.17 0.186 0.835 Inside 0.92248
MLFTMLRKLD 0.93 0.562 0.860 Outside 0.39049
QDLKWARFPK 2.82 0.337 0.978 Inside 0.59730
DLKWARFPKS 2.61 0.356 0.996 Inside 0.48806
LKWARFPKSD 2.61 0.331 0.972 Inside 0.48806
KGFCDLKGKY 1.44 0.467 1.1 Inside 0.55047
GVSAARLTPC 0.6 0.109 0.432 Inside 0.44779
GFAKFLKTNC 0.53 0.530 1.160 Inside 0.51312
KTNCCRFQEK 4.2 0.333 0.974 Inside 0.92234
PHISRQRLTK 4.17 0.167 1.147 Inside 0.78638
HISRQRLTKY 4.18 0.190 1.169 Inside 0.87931
ISRQRLTKYT 3.97 0.208 1.186 Inside 0.90259
SRQRLTKYTM 4.23 0.231 1.208 Inside 0.90877
RQRLTKYTMA 3.71 0.210 1.188 Inside 0.92213
GERVRQALLK 3.11 0.401 1.038 Inside 0.66110
RVRQALLKTV 2.37 0.490 1.452 Inside 0.78888
KPYIKWDLLK 0.84 0.143 0.794 Inside 0.49602
RLKLFDRYFK 3.4 0.622 1.577 Inside 0.61889
KLFDRYFKYW 2.18 0.889 1.499 Inside 0.59390
FPFNKWGKAR 2.16 0.338 1.309 Inside 0.52642
KWGKARLYYD 2.5 0.177 0.827 Inside 0.72287
YAISAKNRAR 3.52 0.076 1.061 Inside 0.91112
AISAKNRART 3.76 0.037 1.024 Inside 0.91919
KNRARTVAGV 3.19 0.315 1.287 Inside 0.88250
NRQFHQKLLK 3.55 0.436 1.401 Inside 0.73407
RQFHQKLLKS 3.23 0.495 1.457 Inside 0.65716
RIMASLVLAR 0.84 0.180 0.829 Inside 0.57528
RNLQHRLYEC 4.25 0.519 0.819 Inside 0.77699
RLYECLYRNR 4.73 0.430 1.065 Inside 0.85428
SLRCGACIRR 3.3 0.356 1.326 Inside 0.83575
RCGACIRRPF 3.15 0.633 1.587 Inside 0.77458
CGACIRRPFL 1.17 0.422 1.058 Inside 0.52389
GACIRRPFLC 1.17 0.269 0.913 Inside 0.52389
ACIRRPFLCC 1.13 0.416 1.052 Inside 0.66539
CIRRPFLCCK 1.87 0.493 1.455 Inside 0.75457
IRRPFLCCKC 1.87 0.298 1.271 Inside 0.75457
RRPFLCCKCC 2.23 0.347 1.317 Inside 0.81002
MSYYCKSHKP 1.92 0.271 0.915 Inside 0.68047
ANTCTERLKL 2.61 0.348 0.988 Inside 0.76670
SWEVGKPRPP 2.33 0.123 0.446 Outside 0.22968
VGKPRPPLNR 3.21 0.403 1.370 Inside 0.41222
GKPRPPLNRN 4.28 0.261 1.236 Inside 0.48502
KALKYLPIDK 0.89 0.154 0.805 Inside 0.53339
DKCSRIIPAR 3.45 0.418 1.054 Inside 0.82505
KCSRIIPARA 2.4 0.376 1.344 Inside 0.83189
CSRIIPARAR 3.34 0.425 1.391 Inside 0.84740
SRIIPARARV 3.06 0.289 1.262 Inside 0.80072
RIIPARARVE 3.4 0.233 0.879 Inside 0.79867
SVVNARLRAK 2.88 0.144 1.125 Inside 0.81132
VVNARLRAKH 3.00 0.138 1.120 Inside 0.83250
VNARLRAKHY 3.42 0.100 1.084 Inside 0.86059
NARLRAKHYV 3.42 0.057 1.043 Inside 0.86059
PAPRTLLTKG 1.3 0.242 0.888 Outside 0.35023
APRTLLTKGT 1.55 0.313 0.955 Inside 0.54170
FNSVCRLMKT 1.75 0.603 1.229 Inside 0.71012
FLGTCRRCPA 1.92 0.606 1.232 Inside 0.60210
DNKLKAHKDK 4.42 0.269 0.913 Inside 0.80851
KLKAHKDKSA 3.04 0.217 1.194 Inside 0.79044
FLTRNPAWRK 3.25 0.256 1.231 Inside 0.70721
RNPAWRKAVF 2.9 0.167 1.147 Inside 0.75095
GIPKDMTYRR 3.86 0.181 0.830 Inside 0.81853
DMTYRRLISM 3.01 0.334 0.645 Inside 0.78592
GNPKAIKCVP 0.47 0.301 0.944 Inside 0.53539
WNTFTRLQSL 2.04 0.432 0.737 Inside 0.51104
ELWAKRNIKP 2.54 0.248 0.894 Inside 0.69594
LWAKRNIKPV 1.46 0.307 1.279 Inside 0.70434
WAKRNIKPVP 1.95 0.435 1.4 Inside 0.69474
RNIKPVPEVK 2.64 0.365 1.004 Inside 0.65866
LLIGLAKRFK 0.06 0.556 1.514 Outside 0.34306
GLAKRFKESP 2.55 0.567 1.195 Inside 0.41824
KMQRMLLEKC 2.25 0.534 1.164 Inside 0.77714
VLRQWLPTGT 0.84 0.442 0.747 Inside 0.37448
DMSKFPLKLR 2.29 0.438 1.073 Outside 0.39910
MSKFPLKLRG 1.33 0.425 1.391 Outside 0.33087
SKFPLKLRGT 1.82 0.312 1.284 Outside 0.37440
KFPLKLRGTA 1.3 0.337 1.308 Outside 0.41667
MILSLLSKGR 0.43 0.383 1.021 Outside 0.30296
LLSKGRLIIR 1.32 0.419 1.385 Outside 0.44682
GRLIIRENNR 4.91 0.336 0.977 Inside 0.81939
RLIIRENNRV 4.6 0.443 1.078 Inside 0.85576
ORF6
LIIKNLSKSL 0 0.601 1.227 Outside 0.38147
ORF7a
HVYQLRARSV 2.87 0.205 0.853 Inside 0.75260
QLRARSVSPK 3.69 0.081 1.066 Inside 0.70446
RARSVSPKLF 2.84 0.344 1.314 Inside 0.52495
RSVSPKLFIR 2.53 0.158 1.139 Inside 0.51018
ITLCFTLKRK 1.21 0.272 1.246 Inside 0.72907
TLCFTLKRKT 1.96 0.280 1.254 Inside 0.78296
LCFTLKRKTE 2.38 0.341 0.981 Inside 0.71141
ORF8
SKWYIRVGAR 2.48 0.303 1.276 Inside 0.82389
KWYIRVGARK 2.7 0.208 1.516 Inside 0.89799
YIRVGARKSA 2.54 0.273 1.247 Inside 0.83725

Fig 1.

Fig 1

Prediction of cellular localization for CPPs using TMHMM web server: (A) EASKKPRQKR peptide containing tumor penetrating motif (RXXR); (B) GIEFLKRGDK peptide containing tumor homing motif (RGD); (C) RSGARSKQRR peptide with +5.00 net charge; (D) VVLKKLKKSL peptide with high half-life (~100 hour) in mammalian cells; (E) SSRSRNSSRN peptide with the highest Boman index (~7.5); (F) MCYKRNRATR peptide containing tumosr penetrating motif (RXXR). All of the prediction results showed the cell localization of CPPs.

Assessment of the immunogenicity

As we mentioned earlier, it is important that CPPs as a delivery system should not have any immune activity. Hence, we analyzed the immunogenicity activity of each peptide using IEDB Immunogenicity Predictor. The results were listed in Table 4.

Table 4. Evaluation of immunogenicity, toxicity, allergenicity, half-life and hemolytic potency.

Epitope Immunogenicity (IEDB) Toxicity Allergenicity (AllerTop) Allergenicity (AllergenFP) Half-life in E.coli Half-life in mammalian HemoPI
S protein
NLTTRTQLPP 0.01498 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN - - 0.49
RFQTLLALHR 0.02623 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
YLQPRTFLLK 0.1338 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
SVYAWNRKRI 0.14453 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.50
YAWNRKRISN -0.02983 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
AWNRKRISNC -0.12774 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
WNRKRISNCV -0.21289 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
RQIAPGQTGK 0.04829 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
YNYLYRLFRK 0.15304 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
YLYRLFRKSN -0.07586 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
YRLFRKSNLK -0.21307 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.44
RLFRKSNLKP -0.42247 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
RKSNLKPFER -0.13415 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
KKSTNLVKNK -0.17866 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KSTNLVKNKC -0.29897 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
HADQLTPTWR 0.06938 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 3.5 hour 0.49
YQTQTNSPRR -0.20731 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
TQTNSPRRAR -0.05055 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
TNSPRRARSV 0.01979 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.51
NSPRRARSVA 0.0702 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.50
PRRARSVASQ -0.07931 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN - - 0.49
KQIYKTPPIK -0.07476 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >0 hour ~ >20 hour 0.49
SQILPDPSKP -0.23322 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
RLITGRLQSL -0.0392 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
M protein
NRNRFLYIIK 0.33978 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN - - 0.49
RNRFLYIIKL 0.2318 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.50
YIIKLIFLWL 0.17526 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
KLIFLWLLWP 0.47552 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
FIASFRLFAR 0.12185 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.49
ASFRLFARTR 0.29647 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
SFRLFARTRS 0.26946 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
FRLFARTRSM 0.18626 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.49
RLFARTRSMW -0.02793 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
FARTRSMWSF -0.12986 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.49
HGTILTRPLL 0.2064 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 3.5 hour 0.49
GAVILRGHLR 0.23964 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.50
RIAGHHLGRC 0.1582 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.50
YSRYRIGNYK 0.21736 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
N protein
PQNQRNAPRI -0.01685 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >20 hour 0.49
ERSGARSKQR -0.33651 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1 hour 0.49
RSGARSKQRR -0.33085 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
SGARSKQRRP -0.34144 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
GARSKQRRPQ -0.38655 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
ARSKQRRPQG -0.36142 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
RSKQRRPQGL -0.17416 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
SKQRRPQGLP -0.03284 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.48
KQRRPQGLPN -0.03866 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
RRPQGLPNNT -0.11764 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
QIGYYRRATR 0.1585 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 0.8 hour 0.49
IGYYRRATRR 0.19571 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 20 hour 0.49
GYYRRATRRI 0.24984 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
YYRRATRRIR 0.31494 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
YRRATRRIRG 0.33565 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.48
RRATRRIRGG 0.34132 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
RATRRIRGGD 0.3418 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
TRRIRGGDGK 0.30454 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
RIRGGDGKMK -0.1472 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
GKMKDLSPRW -0.39805 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
SQASSRSSSR -0.65648 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
ASSRSSSRSR -0.53253 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
SSRSSSRSRN -0.53253 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
SRSSSRSRNS -0.4467 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
RSSSRSRNSS -0.38427 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
SSSRSRNSSR -0.35469 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
SSRSRNSSRN -0.37068 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
RSRNSSRNST -0.36531 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
GSSRGTSPAR -0.07305 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
AALALLLLDR 0.00733 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
ALALLLLDRL 0.01846 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
KKSAAEASKK -0.10752 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KSAAEASKKP -0.27606 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
SAAEASKKPR -0.40103 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
AAEASKKPRQ -0.48135 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
AEASKKPRQK -0.60821 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
EASKKPRQKR -0.66654 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1 hour 0.49
ASKKPRQKRT -0.5082 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
SKKPRQKRTA -0.2802 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
KKPRQKRTAT -0.16998 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KPRQKRTATK -0.16712 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
PRQKRTATKA -0.22281 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >0 hour ~ >20 hour 0.49
RQKRTATKAY -0.06462 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
RQGTDYKHWP 0.02178 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.48
FPPTEPKKDK -0.24988 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.49
PPTEPKKDKK -0.41773 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >20 hour 0.49
PTEPKKDKKK -0.68578 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >0 hour ~ >20 hour 0.49
TEPKKDKKKK -0.92 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
EPKKDKKKKA -0.9864 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1 hour 0.49
PKKDKKKKAD -0.82982 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >20 hour 0.49
KKDKKKKADE -0.78682 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
TQALPQRQKK -0.2971 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
ALPQRQKKQQ -0.63524 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
ORF3a
SASKIITLKK -0.11032 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.56
ASKIITLKKR -0.08712 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.63
SKIITLKKRW -0.15064 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
KIITLKKRWQ -0.16844 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
IITLKKRWQL -0.25058 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ >20 hour 0.50
KKRWQLALSK 0.0469 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.48
KRWQLALSKG -0.29342 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.48
VRIIMRLWLC 0.22654 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 100 hour 0.49
RIIMRLWLCW 0.07375 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
IIMRLWLCWK 0.27346 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 20 hour 0.49
IMRLWLCWKC 0.22073 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 20 hour 0.49
MRLWLCWKCR 0.151 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
RLWLCWKCRS 0.00568 Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
LWLCWKCRSK -0.15588 Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.48
WLCWKCRSKN -0.27401 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
LCWKCRSKNP -0.48871 Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.48
CWKCRSKNPL -0.44989 Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.2 hour 0.48
KCRSKNPLLY -0.39225 Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
Orf1ab
IKRSDARTAP 0.01437 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 20 hour 0.48
KRSDARTAPH 0.1202 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
PVAYRKVLLR -0.1276 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >0 hour ~> 20 hour 0.49
VAYRKVLLRK -0.10848 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ 100 hour 0.48
AYRKVLLRKN -0.26356 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.48
YRKVLLRKNG -0.18712 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.48
RKVLLRKNGN -0.14682 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
KVLLRKNGNK -0.15563 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
FEIKLAKKFD -0.4631 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.47
KTIQPRVEKK -0.0364 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.48
TIQPRVEKKK -0.17191 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 7.2 hour 0.48
IQPRVEKKKL -0.32482 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 20 hour 0.46
SGLKTILRKG -0.14596 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.52
LKTILRKGGR 0.03152 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.47
KTILRKGGRT -0.03682 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
GNFKVTKGKA -0.4014 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.48
FKVTKGKAKK -0.42052 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.49
KVTKGKAKKG -0.65257 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KGKAKKGAWN -0.25629 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
GGAKLKALNL -0.40141 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.53
SKGLYRKCVK -0.17774 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.47
KGLYRKCVKS -0.30883 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.47
GLYRKCVKSR -0.45142 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.45
GLLMPLKAPK -0.37836 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.49
QRKQDDKKIK -0.4506 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 0.8 hour 0.49
RKQDDKKIKA -0.42036 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
KQDDKKIKAC -0.42434 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
DITFLKKDAP -0.25182 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
MLAKALRKVP -0.28616 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.49
LAKALRKVPT -0.16567 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 5.5 hour 0.50
EAKTVLKKCK -0.41804 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1 hour 0.49
AKTVLKKCKS -0.54116 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
KTVLKKCKSA -0.74717 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KSAFYILPSI 0.14004 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
KAIVSTIQRK 0.0192 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
STIQRKYKGI -0.39864 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.48
TIQRKYKGIK -0.29576 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
IQRKYKGIKI -0.39558 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 20 hour 0.49
GARFYFYTSK 0.17762 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
ARYMRSLKVP -0.45692 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
GIEFLKRGDK 0.01978 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.47
DNLKTLLSLR -0.35014 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
YMSALNHTKK -0.09422 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
SALNHTKKWK -0.19639 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.50
ALNHTKKWKY -0.28381 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
LNHTKKWKYP -0.34609 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 5.5 hour 0.49
NHTKKWKYPQ -0.4113 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN - - 0.49
HTKKWKYPQV -0.36182 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 3.5 hour 0.49
KKPASRELKV -0.11604 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.45
KPASRELKVT -0.17419 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
YTPSFKKGAK -0.41761 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
PSFKKGAKLL -0.50765 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >20 hour 0.51
FKKGAKLLHK -0.2087 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.48
KKGAKLLHKP -0.27957 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.47
KGAKLLHKPI -0.38393 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.44
WCIRCLWSTK 0.12355 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
CIRCLWSTKP -0.08152 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.2 hour 0.48
ANYAKPFLNK -0.08557 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
TNIVTRCLNR 0.10905 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
CTFTRSTNSR -0.09922 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.2 hour 0.49
TCMMCYKRNR -0.4529 Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
MCYKRNRATR -0.06968 Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.48
CYKRNRATRV 0.12601 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.2 hour 0.49
YKRNRATRVE 0.20313 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
KRNRATRVEC 0.26794 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
RNRATRVECT 0.23623 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
RDLSLQFKRP -0.33523 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
SLQFKRPINP 0.0187 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.48
HNIALIWNVK 0.42854 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 3.5 hour 0.49
LSEQLRKQIR -0.26746 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.49
QLRKQIRSAA -0.25144 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 0.8 hour 0.44
LRKQIRSAAK -0.10641 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 5.5 hour 0.37
RKQIRSAAKK -0.07053 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
KQIRSAAKKN -0.29889 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.50
QIRSAAKKNN -0.46225 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 0.8 hour 0.49
AAKKNNLPFK -0.251 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
KKNNLPFKLT -0.10849 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
NNWLKQLIKV -0.28618 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN - - 0.49
LAYYFMRFRR 0.11584 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 5.5 hour 0.49
AYYFMRFRRA 0.18012 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
YYFMRFRRAF 0.14096 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
FMRFRRAFGE 0.39083 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.49
MRFRRAFGEY 0.37588 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
KEMYLKLRSD -0.34494 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
YNRYLALYNK 0.02304 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
RYLALYNKYK -0.16888 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
FRKMAFPSGK -0.22486 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.50
TANPKTPKYK -0.38006 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
ANPKTPKYKF -0.52572 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
PKTPKYKFVR -0.29224 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >30 hour 0.49
KTPKYKFVRI -0.25892 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
RWFLNRFTTT 0.23658 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
FQSAVKRTIK -0.02489 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.50
SEVVLKKLKK -0.50422 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.48
VVLKKLKKSL -0.92306 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 100 hour 0.48
VLKKLKKSLN -0.8569 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 100 hour 0.48
KKLKKSLNVA -0.58348 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.47
DAAMQRKLEK -0.38026 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
AAMQRKLEKM -0.3851 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
MQRKLEKMAD -0.44184 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.49
YKQARSEDKR -0.12196 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
KQARSEDKRA -0.1297 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
QARSEDKRAK -0.16703 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 0.8 hour 0.49
MLFTMLRKLD -0.2445 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
QDLKWARFPK 0.17424 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 0.8 hour 0.48
DLKWARFPKS 0.21623 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
LKWARFPKSD -0.01343 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.49
KGFCDLKGKY -0.30585 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
GVSAARLTPC 0.09001 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.48
GFAKFLKTNC -0.27512 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
KTNCCRFQEK 0.01249 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.48
PHISRQRLTK -0.12363 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >0 hour ~ >20 hour 0.47
HISRQRLTKY -0.1677 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 3.5 hour 0.48
ISRQRLTKYT -0.20778 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 20 hour 0.49
SRQRLTKYTM -0.14196 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
RQRLTKYTMA -0.23988 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
GERVRQALLK 0.01693 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.48
RVRQALLKTV -0.24222 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.48
KPYIKWDLLK 0.14346 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
RLKLFDRYFK 0.16844 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.46
KLFDRYFKYW 0.03316 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
FPFNKWGKAR -0.09005 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.49
KWGKARLYYD -0.13322 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
YAISAKNRAR -0.23472 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
AISAKNRART -0.11865 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.49
KNRARTVAGV 0.23605 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
NRQFHQKLLK -0.21994 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN - - 0.49
RQFHQKLLKS -0.39805 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
RIMASLVLAR -0.13717 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
RNLQHRLYEC 0.00668 Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.49
RLYECLYRNR 0.07267 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
SLRCGACIRR 0.12546 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.46
RCGACIRRPF 0.21339 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
CGACIRRPFL 0.24621 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.2 hour 0.43
GACIRRPFLC 0.2991 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.43
ACIRRPFLCC 0.20422 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 min ~ 4.4 hour 0.48
CIRRPFLCCK 0.08464 Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.2 hour 0.49
IRRPFLCCKC -0.11341 Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 20 hour 0.49
RRPFLCCKCC -0.21335 Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
MSYYCKSHKP -0.52185 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
ANTCTERLKL 0.00701 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
SWEVGKPRPP -0.0762 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
VGKPRPPLNR -0.06514 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 100 hour 0.49
GKPRPPLNRN 0.04122 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.49
KALKYLPIDK -0.12016 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.48
DKCSRIIPAR 0.13481 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.48
KCSRIIPARA 0.3104 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
CSRIIPARAR 0.37289 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.2 hour 0.49
SRIIPARARV 0.3164 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
RIIPARARVE 0.22517 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
SVVNARLRAK 0.15149 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.9 hour 0.48
VVNARLRAKH 0.03261 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 100 hour 0.49
VNARLRAKHY -0.02189 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 100 hour 0.48
NARLRAKHYV -0.08372 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN - - 0.48
PAPRTLLTKG -0.0282 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >0 hour ~ >20 hour 0.49
APRTLLTKGT -0.0914 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 4.4 hour 0.49
FNSVCRLMKT -0.2989 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.1 hour 0.48
FLGTCRRCPA 0.0447 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.47
DNKLKAHKDK -0.39165 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
KLKAHKDKSA -0.46691 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.48
FLTRNPAWRK 0.30159 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.1 hour 0.48
RNPAWRKAVF 0.15601 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.48
GIPKDMTYRR -0.30634 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
DMTYRRLISM 0.1149 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
GNPKAIKCVP -0.27728 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.48
WNTFTRLQSL 0.01374 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.49
ELWAKRNIKP -0.0681 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1 hour 0.49
LWAKRNIKPV -0.2234 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.50
WAKRNIKPVP -0.08286 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 2.8 hour 0.49
RNIKPVPEVK -0.04622 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.48
LLIGLAKRFK 0.01368 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 5.5 hour 0.52
GLAKRFKESP -0.25506 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.52
KMQRMLLEKC -0.21926 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.49
VLRQWLPTGT 0.14726 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 100 hour 0.49
DMSKFPLKLR -0.36642 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 1.1 hour 0.49
MSKFPLKLRG -0.15592 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 30 hour 0.52
SKFPLKLRGT -0.14092 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.53
KFPLKLRGTA -0.13556 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1.3 hour 0.52
MILSLLSKGR -0.5096 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.50
LLSKGRLIIR -0.00766 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.50
GRLIIRENNR 0.39533 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 30 hour 0.49
RLIIRENNRV 0.34371 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1 hour 0.49
ORF6
LIIKNLSKSL -0.62529 Non-Toxin PROBABLE ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.49
ORF7a
HVYQLRARSV -0.09431 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 min ~ 3.5 hour 0.49
QLRARSVSPK -0.16177 Non-Toxin PROBABLE ALLERGEN PROBABLE ALLERGEN ~ >10 hour ~ 0.8 hour 0.47
RARSVSPKLF -0.46949 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
RSVSPKLFIR -0.20775 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 1 hour 0.48
ITLCFTLKRK -0.06825 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 20 hour 0.49
TLCFTLKRKT -0.15802 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 7.2 hour 0.49
LCFTLKRKTE -0.25434 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 5.5 hour 0.49
ORF8
SKWYIRVGAR 0.3385 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ >10 hour ~ 1.9 hour 0.49
KWYIRVGARK 0.31848 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE ALLERGEN ~ 2 min ~ 1.3 hour 0.49
YIRVGARKSA -0.1005 Non-Toxin PROBABLE Non-ALLERGEN PROBABLE Non-ALLERGEN ~ 2 min ~ 2.8 hour 0.48

Determination of toxicity and allergenicity

The toxicity and allergenicity of each peptide were determined using diverse web servers (Table 4). In detail, most of the predicted CPPs were non-toxic. The toxic CPPs were derived from ORF3a and Orf1ab polyproteins. Furthermore, there are some differences between allergenicity prediction by AllerTop, and AllergenFP web servers. Some CPPs were determined as probable allergen by AllerTop, whereas they were identified as probable non-allergen by AllergenFP. It is rational to select CPPs which were determined as probable non-allergen by both web servers.

Estimation of hemolytic potency and half-life

It should be considered that high hydrophobicity of a peptide enhances its probability of hydrolysis in the host; therefore, the probability of hydrolysis and half-life of each peptide in E.coli and mammalian were evaluated using HemoPI and ProtLifePred web servers (Table 4). The results of hemolytic potency vary between 0 and 1 (i.e., 0 very unlikely to be hemolytic, and 1 very likely to be hemolytic). For example, seven predicted CPPs had the highest half-life in mammalian cells (~ 100 hours) which all of them were derived from Orf1ab polyprotein including VAYRKVLLRK, VVLKKLKKSL, VLKKLKKSLN, VGKPRPPLNR, VVNARLRAKH, VNARLRAKHY, and VLRQWLPTGT peptides.

Prediction of CPP structure

The 3D spatial shapes of CPPs were predicted by PEP-FOLD3 web server (Fig 2). Also, the helical wheel projection of these short peptides were obtained via Heliquest web server as indicated in Fig 3. A peptide comprising at least five adjacent hydrophobic residues (such as Leu, Ile, Ala, Val, Pro, Met, Phe, Trp, and Tyr) illustrates a hydrophobic face on a helical wheel projection [46].

Fig 2.

Fig 2

The 3D spatial shape of CPPs predicted by PEP-FOLD3 web server: (A) EASKKPRQKR peptide containing tumor penetrating motif (RXXR); (B) GIEFLKRGDK peptide containing tumor homing motif (RGD); (C) RSGARSKQRR peptide with +5.00 net charge; (D) VVLKKLKKSL peptide with high half-life (~100 hour) in mammalian cells; (E) SSRSRNSSRN peptide with the highest Boman index (~7.5); (F) MCYKRNRATR peptide containing tumor penetrating motif (RXXR).

Fig 3.

Fig 3

Helical wheel projection of the six selected CPPs by HeliQuest: These data indicated the possible amphipathic α-helical conformation of the selected CPPs: (A) EASKKPRQKR peptide containing tumor penetrating motif (RXXR); (B) GIEFLKRGDK peptide containing tumor homing motif (RGD); (C) RSGARSKQRR peptide with +5.00 net charge; (D) VVLKKLKKSL peptide with high half-life (~100 hour) in mammalian cells; (E) SSRSRNSSRN peptide with the highest Boman index (~7.5); (F) MCYKRNRATR peptide containing tumor penetrating motif (RXXR). The structural motifs were shown as hydrophobic (yellow) and cationic (blue). Arrow illustrates direction of the hydrophobic moment (μH).

Discussion

Vaccination is one of the most effective strategies for control of dangerous pathogens. A potent vaccine must stimulate strong humoral and cellular immune responses in host [56]. The vaccine efficacy relies on various factors including the selected antigen, adjuvant and delivery system [57]. Therefore, many researchers have focused on development of novel and powerful delivery systems [9,5860]. Since the discovery of CPPs, these short peptides were considered as a significant delivery system to enter diverse types of cargoes into cells due to their high cellular uptake efficiency. Several viruses such as HIV-1, Influenza A virus subtype H5N1, Dengue virus and HSV-1 contain CPPs in their proteome [11,30,34,61].

The bioinformatics strategies take scientists one step forward in screening and evaluating CPPs. Hence, the current study was planned to screen and identify novel and potent CPPs in the proteome of SARS-CoV-2 using in silico tools. To achieve this aim, we extracted the sequences of S, M, N, E, ORF1ab, ORF3a, ORF6, ORF7a, ORF8, and ORF10 proteins and submitted to CellPPD web server. The CellPPD is a support vector machine (SVM)-based prediction approach which was established to predict highly efficient cell penetrating peptides. The CellPPD method was based on binary profile of peptides that settle the information of both composition and order of residues in peptides [40]. The output of analysis using CellPPD web server was a large number of CPPs which subjected to several web servers for further analysis such as their physicochemical properties, uptake efficiency, toxicity, allergenicity, cellular localization, tendency for binding to plasma membrane, and prediction of 3D structure. Our results showed that the proteome of SARS-CoV-2 contains a large number of cell penetrating peptides. Most of the predicted CPPs were originated from Orf1ab polyprotein. Orf1ab polyprotein forms about two thirds of the SARS-CoV-2 genome that is translated into two polypeptides such as pp1a and pp1b. Next, these two polypeptides are processed and cleaved into sixteen non-structural proteins (nsp). Non-structural proteins possess crucial functions in the replication, transcription and pathogenesis of viral RNA [29]. Despite Orf1ab polyprotein, our data indicated that no CPP was found in the E protein. This protein is responsible for virus production and maturation [28]. Herein, twenty-four CPPs were predicted in spike (S) protein, as well. Furthermore, most of the predicted CPPs in S protein are amphipathic in nature. On the other hand, most of the predicted CPPs showed high uptake efficiency using in silico approaches. The studies demonstrated that several factors affect the uptake efficiency such as the number of arginine, the existence of tryptophan and its affinity to form helical structure, and orientation of tryptophan and arginine around the helix [6264]. In addition, it should be considered that CPPs because of their natural pore-forming propensity and high hydrophobic moment (μH) could damage or destabilize the lipid bilayers irreversibly and so they showed cytotoxic effects. Hence, minimizing μH should be performed to reduce the membrane-disturbing by CPPs [65,66]. Our data indicated that most of the CPPs predicted from the proteome of SARS-CoV-2 were not toxic and allergen, and had appropriate half-life, as well as they could bind to plasma membrane with high potential and subsequently penetrate into cells. For example, Kajiwara et al. showed that H5N1 highly pathogenic avian influenza virus (HPAIV) infects host cells by recruiting CPP activity of the C-terminal domain of HA1 protein (HA314-346) [61]. Moreover, the N-terminal tail of capsid protein (CaP) from the plant-infecting brome mosaic virus (BMV) containing the arginine-rich motif was essential for penetration through cellular membranes [67]. Thus, it is possible that CPPs found in the SARS-CoV-2 proteome possess the potency for virus penetration into host cells.

On the other hand, CPPs are not cell-specific and thus they are internalized in most of the cell types through receptor-independent approach. Hence, to determine CPPs that might be cancer-specific or might enter cancerous cells effectively, the peptide sequence should possess the tumor homing motif (RGD) and/or tumor penetrating motif (RXXR). Moreover, the peptides harboring RXXR motif at their C-terminal region could enter tumor cells through binding to neuropilin receptor which was commonly expressed at the surface of tumor cells [19]. In our study, one of the SARS-CoV-2-derived CPPs (i.e., GIEFLKRGDK) contains RGD motif. This CPP with +1.00 net charge was soluble in water, non-toxic, and its half-life was about 30 hours in mammalian. Its cellular localization was predicted using TMHMM server. Interestingly, two CPPs such as EASKKPRQKR and MCYKRNRATR peptides included RXXR motif at their C-terminal regions. In detail, EASKKPRQKR peptide had +4.00 net charge and good water solubility. This peptide was non-toxic and non-allergen with about one hour half-life in mammalian. Also, the Boman index was 6.04 for this CPP (i.e., the values higher than 2.48 kcal/mol showed high binding potential), and its cellular localization was confirmed by TMHMM web server. Moreover, MCYKRNRATR peptide had +4.00 net charge and good water solubility. But this CPP was predicted as a toxic and allergen peptide with the estimated Boman index about 5.42. Additionally, TMHMM web server predicted its localization inside the cell. Therefore, based on our data, the efficiency of GIEFLKRGDK and EASKKPRQKR peptides can be further evaluated in vitro and in vivo as a delivery system in cancer therapy.

In the present study, only CPPs with 10 residues in length were predicted. As known, CPPs contain 5–50 residues in length [11]. Thus, we can design novel CPPs with more length and higher efficiency by addition of some sequences for delivery of different cargoes. For instance, we can add a hydrophilic lysine-rich domain derived from NLS of SV40 large T-antigen (KKKRKV) and a spacer domain (WSQP) to improve the efficiency of CPPs in DNA delivery as used in other studies [33]. In this study, as an example, by merging 11 overlapped CPPs derived from N protein such as KKSAAEASKK, KSAAEASKKP, SAAEASKKPR, AAEASKKPRQ, AEASKKPRQK, EASKKPRQKR, ASKKPRQKRT, SKKPRQKRTA, KKPRQKRTAT, KPRQKRTATK, and PRQKRTATKA peptides (with net charges of +4.00 and +5.00), a novel CPP was designed with 21 residues in length (i.e., KKSAAEASKKPRQKRTATKAY). This CPP had +7.00 net charge and good water solubility. Moreover, it was non-allergen and non-toxic with immunogenicity score about -0.70123 and D factor about 2.46 which would be located into cells as predicted by TMHMM web server. Surprisingly, when the SV40 large T-antigen NLS sequence and a spacer domain were conjugated to this CPP, we had a new CPP with 31 amino acids in length (i.e., KKSAAEASKKPRQKRTATKAYWSQPKKKRKV), +12.00 net charge, and good water solubility. This peptide was non-allergen and non-toxic with immunogenicity score about -1.49065 and D factor about 4.11, which was localized into cells as predicted by TMHMM web server. Indeed, using the conjugation of NLS and spacer to the designed CPP, the net charge and the probability of cellular localization inside cells were enhanced. Our predicted and designed CPP is similar to MPG CPP (27 residues in length, and +4.00 net charge) composed of peptide derived from HIV-1 glycoprotein 41, SV40 NLS and spacer domain. The MPG peptide was reported for delivery of DNA-based vaccine both in vitro and in vivo [33,68,69].

Conclusion

In conclusion, novel and potent CPPs derived from the proteome of SARS-CoV-2 were identified using in silico methods. It is possible for relationship between these CPPs and rapid spreading the virus in host. Moreover, we designed a long and novel CPP conjugated to SV40 NLS and spacer domain that had high binding ability to membrane and localization inside cells. The designed CPP was similar to MPG CPP. This CPP can be further evaluated for DNA delivery in vitro and in vivo in future. Generally, the predicted and designed CPPs derived from the proteome of SARS-CoV-2 with different properties can be applied to deliver different cargoes in vaccine and drug development.

Supporting information

S1 Fig. The flowchart of overall study plan.

(TIF)

Data Availability

All relevant data are within the manuscript.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Florindo HF , Kleiner R, Vaskovich-Koubi D, Acúrcio RC, Carreira B, Yeini E, et al. Immune-mediated approaches against COVID-19. Nature Nanotechnology. 2020; 15: 630–645. 10.1038/s41565-020-0732-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Zareba G. A new combination vaccine for measles, mumps, rubella and varicella. Drugs Today. 2006; 42: 321–329. 10.1358/dot.2006.42.5.973586 [DOI] [PubMed] [Google Scholar]
  • 3.Kardani K, Basimi P, Fekri M, Bolhassani A. Antiviral therapy for the sexually transmitted viruses: recent updates on vaccine development. Expert Rev. Clin. Pharmacol. 2020; 13 (9): 1001–1046. [DOI] [PubMed] [Google Scholar]
  • 4.Garg A, Dewangan HK. Nanoparticles as adjuvants in vaccine delivery. Crit. Rev. Ther. Drug. 2020; 37 (2): 183–204. 10.1615/CritRevTherDrugCarrierSyst.2020033273 [DOI] [PubMed] [Google Scholar]
  • 5.Nevagi RJ, Skwarczynski M, Toth I. Polymers for subunit vaccine delivery. Eur. Polym. J. 2019; 114: 397–410. [Google Scholar]
  • 6.Şenel S, Yüksel S. Chitosan-based particulate systems for drug and vaccine delivery in the treatment and prevention of neglected tropical diseases. Drug. Deliv. Transl. Res. 2020; 10: 1644–1674. 10.1007/s13346-020-00806-4 [DOI] [PubMed] [Google Scholar]
  • 7.Yu R, Mai Y, Zhao Y, Hou Y, Liu Y, Yang J. Targeting strategies of liposomal subunit vaccine delivery systems to improve vaccine efficacy. J. Drug Target. 2019; 27: 780–789. 10.1080/1061186X.2018.1547734 [DOI] [PubMed] [Google Scholar]
  • 8.Du X, Wang J, Zhou Q, Zhang L, Wang S, Zhang Z, et al. Advanced physical techniques for gene delivery based on membrane perforation. Drug Deliv. 2018; 25: 1516–1525. 10.1080/10717544.2018.1480674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Bolhassani A, Jafarzade BS, Mardani G. In vitro and in vivo delivery of therapeutic proteins using cell penetrating peptides. Peptides. 2017; 87: 50–63. 10.1016/j.peptides.2016.11.011 [DOI] [PubMed] [Google Scholar]
  • 10.Shahbazi S, Bolhassani A. Comparison of six cell penetrating peptides with different properties for in vitro and in vivo delivery of HPV16 E7 antigen in therapeutic vaccines. Int. Immunopharmacol. 2018; 62: 170–180. 10.1016/j.intimp.2018.07.006 [DOI] [PubMed] [Google Scholar]
  • 11.Kardani K, Milani A, Shabani SH, Bolhassani A. Cell penetrating peptides: the potent multi-cargo intracellular carriers. Expert Opin. Drug Deliv. 2019; 16: 1227–1258. 10.1080/17425247.2019.1676720 [DOI] [PubMed] [Google Scholar]
  • 12.Hoffmann K, Milech N, Juraja SM, Cunningham PT, Stone SR, Francis RW, et al. A platform for discovery of functional cell-penetrating peptides for efficient multi-cargo intracellular delivery. Sci. Rep. 2018; 8: 1–6. 10.1038/s41598-017-17765-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Xia H, Gao X, Gu G, Liu Z, Hu Q, Tu Y, et al. Penetratin-functionalized PEG-PLA nanoparticles for brain drug delivery. Int. J. Pharm. 2012; 436: 840–850. 10.1016/j.ijpharm.2012.07.029 [DOI] [PubMed] [Google Scholar]
  • 14.Yang J, Luo Y, Shibu MA, Toth I, Skwarczynskia M. Cell-penetrating peptides: Efficient vectors for vaccine delivery. Curr. Drug Deliv. 2019; 16: 430–443. 10.2174/1567201816666190123120915 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Milletti F. Cell-penetrating peptides: classes, origin, and current landscape. Drug Discov. Today. 2012; 17: 850–860. 10.1016/j.drudis.2012.03.002 [DOI] [PubMed] [Google Scholar]
  • 16.Maiolo JR, Ferrer M, Ottinger EA. Effects of cargo molecules on the cellular uptake of arginine-rich cell-penetrating peptides. Biochim. Biophys. Acta Biomembr. 2005; 1712: 161–172. 10.1016/j.bbamem.2005.04.010 [DOI] [PubMed] [Google Scholar]
  • 17.Futaki S. Membrane-permeable arginine-rich peptides and the translocation mechanisms. Adv. Drug Deliv. Rev. 2005; 57: 547–558. 10.1016/j.addr.2004.10.009 [DOI] [PubMed] [Google Scholar]
  • 18.Madani F, Lindberg S, Langel Ü, Futaki S, Gräslund A. Mechanisms of cellular uptake of cell-penetrating peptides. J. Biophys. 2011; 2011: 1–10. 10.1155/2011/414729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gautam A, Sharma M, Vir P, Chaudhary K, Kapoor P, Kumar R, et al. Identification and characterization of novel protein-derived arginine-rich cell-penetrating peptides. Eur. J. Pharm. Biopharm. 2015; 89: 93–106. 10.1016/j.ejpb.2014.11.020 [DOI] [PubMed] [Google Scholar]
  • 20.Kardani K, Bolhassani A. CPPsite 2.0: An available database of experimentally validated cell-penetrating peptides predicting their secondary and tertiary structures. J. Mol. Biol. 2020; 166703 10.1016/j.jmb.2020.11.002 [DOI] [PubMed] [Google Scholar]
  • 21.Liu Z, Cui Y, Xiong Z, Nasiri A, Zhang A, Hu J. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction. Sci. Rep. 2019; 9: 794 10.1038/s41598-018-37214-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q, et al. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinformatics. 2020; 21: 621–636. 10.1093/bib/bby127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Li Y, Niu M, Zou Q. ELM-MHC: an improved MHC identification method with extreme learning machine algorithm. J. Proteome Res. 2019; 18: 1392–1401. 10.1021/acs.jproteome.9b00012 [DOI] [PubMed] [Google Scholar]
  • 24.Yin J, Sun W, Li F, Hong J, Li X, Zhou Y, et al. VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res. 2020; 48: D1042–D1050. 10.1093/nar/gkz779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Chen L, Chu C, Huang T, Kong X, Cai YD. Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models. Amino Acids. 2015; 47: 1485–1493. 10.1007/s00726-015-1974-5 [DOI] [PubMed] [Google Scholar]
  • 26.Tang H, Su ZD, Wei HH, Chen W, Lin H. Prediction of cell-penetrating peptides with feature selection techniques. Biochem. Biophys. Res. Commun. 2016; 477: 150–154. 10.1016/j.bbrc.2016.06.035 [DOI] [PubMed] [Google Scholar]
  • 27.Liu B, Chen J, Guo M, Wang X. Protein remote homology detection and fold recognition based on Sequence-Order Frequency Matrix. TCBB. 2017; 16: 292–300. 10.1109/TCBB.2017.2765331 [DOI] [PubMed] [Google Scholar]
  • 28.Kardani K, Bolhassani A. Vaccine development against SARS-CoV-2: From virology to vaccine clinical trials. Coronaviruses. 2020; 1: 1–13. [Google Scholar]
  • 29.Wabalo EK, Dubiwak AD, Gizaw TS, Kotu UG. Role of structural and functional proteins of SARS-COV-2. GSC. Biol. Pharm. Sci. 2020; 12: 117–129. [Google Scholar]
  • 30.Freire JM, Veiga AS, Rego de Figueiredo I, de la Torre BG, Santos NC, Andreu D, et al. Nucleic acid delivery by cell penetrating peptides derived from dengue virus capsid protein: design and mechanism of action. FEBS. J. 2014; 281: 191–215. 10.1111/febs.12587 [DOI] [PubMed] [Google Scholar]
  • 31.Rhee M, Davis P. Mechanism of uptake of C105Y, a novel cell-penetrating peptide. J. Biol. Chem. 2006; 281: 1233–1240. 10.1074/jbc.M509813200 [DOI] [PubMed] [Google Scholar]
  • 32.Morris MC, Depollier J, Mery J, Heitz F, Divita G. A peptide carrier for the delivery of biologically active proteins into mammalian cells. Nat. Biotechnol. 2001; 19: 1173–1176. 10.1038/nbt1201-1173 [DOI] [PubMed] [Google Scholar]
  • 33.Kadkhodayan S, Bolhassani A, Sadat SM, Irani S, Fotouhi F. The efficiency of Tat cell penetrating peptide for intracellular uptake of HIV-1 Nef expressed in E. coli and mammalian cell. Curr. Drug Deliv. 2017; 14(4): 536–542. 10.2174/1567201813666161006114448 [DOI] [PubMed] [Google Scholar]
  • 34.Elliott G, O’Hare P. Intercellular trafficking and protein delivery by a herpes virus structural protein. Cell. 1997; 88: 223–233. 10.1016/s0092-8674(00)81843-7 [DOI] [PubMed] [Google Scholar]
  • 35.Chakraborty H, Bhattacharjya S. Mechanistic insights of host cell fusion of SARS-CoV-1 and SARS-CoV-2 from atomic resolution structure and membrane dynamics. Biophysical Chemistry. 2020; 106438 10.1016/j.bpc.2020.106438 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mahajan M, Chatterjee D, Bhuvaneswari K, Pillay S, Bhattacharjya S. NMR structure and localization of a large fragment of the SARS-CoV fusion protein: Implications in viral cell fusion. Biochimica et Biophysica Acta (BBA)-Biomembranes. 2018; 1860: 407–415. 10.1016/j.bbamem.2017.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Mahajan M, Bhattacharjya S. NMR structures and localization of the potential fusion peptides and the pre-transmembrane region of SARS-CoV: Implications in membrane fusion. Biochimica et Biophysica Acta (BBA)-Biomembranes. 2015; 1848: 721–730. 10.1016/j.bbamem.2014.11.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kumar S, Maurya VK, Prasad AK, Bhatt MLB, Saxena SK. Structural, glycosylation and antigenic variation between 2019 novel coronavirus (2019-nCoV) and SARS coronavirus (SARS-CoV). Virus Disease. 2020; 31: 13–21. 10.1007/s13337-020-00571-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020; 579: 265–269. 10.1038/s41586-020-2008-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Gautam A, Chaudhary K, Kumar R, Sharma A, Kapoor P, Tyagi A, et al. In silico approaches for designing highly effective cell penetrating peptides. J. Transl. Med. 2013; 11: 74 10.1186/1479-5876-11-74 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Gautam A, Chaudhary K, Kumar R, Raghava GP. Computer-aided virtual screening and designing of cell-penetrating peptides In Cell-Penetrating Peptides 2015; 59–69); Humana Press, New York. [DOI] [PubMed] [Google Scholar]
  • 42.Tang H, Su ZD, Wei HH, Chen W, Lin H. Prediction of cell-penetrating peptides with feature selection techniques. Biochem. Biophys. Res. Commun. 2016; 477: 150–154. 10.1016/j.bbrc.2016.06.035 [DOI] [PubMed] [Google Scholar]
  • 43.Wei L, Xing P, Su R, Shi G, Ma ZS, Zou Q. CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J. Proteome Res. 2017; 16: 2044–2053. 10.1021/acs.jproteome.7b00019 [DOI] [PubMed] [Google Scholar]
  • 44.Manavalan B, Subramaniyam S, Shin TH, Kim MO, Lee G. Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy. J. Proteome Res. 2018; 17: 2715–2726. 10.1021/acs.jproteome.8b00148 [DOI] [PubMed] [Google Scholar]
  • 45.Boman HG. Antibacterial peptides: basic facts and emerging concepts. J. Intern. Med. 2003; 254: 197–215. 10.1046/j.1365-2796.2003.01228.x [DOI] [PubMed] [Google Scholar]
  • 46.Gautier R, Douguet D, Antonny B, Drin G. HELIQUEST: a web server to screen sequences with specific α-helical properties. Bioinformatics. 2008; 24: 2101–2102. 10.1093/bioinformatics/btn392 [DOI] [PubMed] [Google Scholar]
  • 47.Sonnhammer EL, Von Heijne G, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. InIsmb 1998; 6: 175–182. [PubMed] [Google Scholar]
  • 48.Shankar G, Arkin S, Cocea L, Devanarayan V, Kirshner S, Kromminga A, et al. Assessment and reporting of the clinical immunogenicity of therapeutic proteins and peptides-harmonized terminology and tactical recommendations. AAPS. J. 2014; 16: 658–673. 10.1208/s12248-014-9599-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Kuriakose A, Chirmule N, Nair P. Immunogenicity of biotherapeutics: causes and association with posttranslational modifications. J. Immunol. Res. 2016; 2016: 1–18. 10.1155/2016/1298473 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 2013; 9: e1003266 10.1371/journal.pcbi.1003266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP. In silico approach for predicting toxicity of peptides and proteins. PLoS One. 2013; 8: e73957 10.1371/journal.pone.0073957 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2-a server for in silico prediction of allergens. J. Mol. Model. 2014; 20: 2278 10.1007/s00894-014-2278-5 [DOI] [PubMed] [Google Scholar]
  • 53.Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 2014; 30: 846–851. 10.1093/bioinformatics/btt619 [DOI] [PubMed] [Google Scholar]
  • 54.Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, et al. A web server and mobile app for computing hemolytic potency of peptides. Sci. Rep. 2016; 6: 1–3. 10.1038/s41598-016-0001-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 2016; 44: W449–W454. 10.1093/nar/gkw329 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Nabel GJ. Designing tomorrow’s vaccines. N. Engl. J. Med. 2013; 368: 551–560. 10.1056/NEJMra1204186 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Dong Y, Dai T, Wei Y, Zhang L, Zheng M, Zhou F. A systematic review of SARS-CoV-2 vaccine candidates. Signal Transduct. Target Ther. 2020; 5: 1–4. 10.1038/s41392-019-0089-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Dhakal S, Renukaradhya GJ. Nanoparticle-based vaccine development and evaluation against viral infections in pigs. Vet Res. 2019; 50: 90 10.1186/s13567-019-0712-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Wallis J, Katti P, Martin AM, Hills T, Seymour LW, Shenton DP, et al. A liposome-based cancer vaccine for a rapid and high-titre anti-ErbB-2 antibody response. Eur. J. Pharm. Sci. 2020; 152: 105456. [DOI] [PubMed] [Google Scholar]
  • 60.Zhang M, Hong Y, Chen W, Wang C. Polymers for DNA vaccine delivery. ACS Biomater. Sci. Eng. 2017; 3: 108–125. 10.1021/acsbiomaterials.6b00418 [DOI] [PubMed] [Google Scholar]
  • 61.Kajiwara N, Nomura N, Ukaji M, Yamamoto N, Kohara M, Yasui F, et al. Cell-penetrating peptide-mediated cell entry of H5N1 highly pathogenic avian influenza virus. Scientific Reports. 2020; 10: 1–3. 10.1038/s41598-019-56847-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Rydberg HA, Matson M, Amand HL, Esbjorner EK, Nordén B. Effects of tryptophan content and backbone spacing on the uptake efficiency of cell-penetrating peptides. Biochemistry. 2012; 51: 5531–5539. 10.1021/bi300454k [DOI] [PubMed] [Google Scholar]
  • 63.Wender PA, Mitchell DJ, Pattabiraman K, Pelkey ET, Steinman L, Rothbard JB. The design, synthesis, and evaluation of molecules that enable or enhance cellular uptake: peptoid molecular transporters. Proc. Natl. Acad. Sci. 2000; 97: 13003–13008. 10.1073/pnas.97.24.13003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Caesar CE, Esbjörner EK, Lincoln P, Nordén B. Membrane interactions of cell-penetrating peptides probed by tryptophan fluorescence and dichroism techniques: correlations of structure to cellular uptake. Biochemistry. 2006; 45: 7682–7692. 10.1021/bi052095t [DOI] [PubMed] [Google Scholar]
  • 65.Hällbrink M, Florén A, Elmquist A, Pooga M, Bartfai T, Langel Ü. Cargo delivery kinetics of cell-penetrating peptides. Biochim. Biophys. Acta Biomembr. 2001; 1515: 101–109. 10.1016/s0005-2736(01)00398-4 [DOI] [PubMed] [Google Scholar]
  • 66.Räägel H, Pooga M. Peptide and protein delivery with cell-penetrating peptides In Peptide and Protein Delivery 2011; 221–246; Academic Press. [Google Scholar]
  • 67.Qi X, Droste T, Kao CC. Cell-penetrating peptides derived from viral capsid proteins. Molecular Plant-Microbe Interactions 2011; 24: 25–36. 10.1094/MPMI-07-10-0147 [DOI] [PubMed] [Google Scholar]
  • 68.Habault J, Poyet JL. Recent advances in cell penetrating peptide-based anticancer therapies. Molecules. 2019; 24: 927 10.3390/molecules24050927 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Kwon SJ, Han K, Jung S, Lee JE, Park S, Cheon YP, Lim HJ. Transduction of the MPG-tagged fusion protein into mammalian cells and oocytes depends on amiloride-sensitive endocytic pathway. BMC Biotechnology. 2009; 9: 73 10.1186/1472-6750-9-73 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The flowchart of overall study plan.

(TIF)

Data Availability Statement

All relevant data are within the manuscript.


Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES