Abstract
Clades are monophyletic groups composed of a common ancestor and all its lineal descendants. As the propensity of virulence of a disease depends upon the type of clade the virus belongs to and it causes different fatality rates of disease in different countries, so the clade-wise analysis of SARS-CoV-2 isolates collected from different countries can illuminate the actual evolutionary relationships between them. In this study, 1566 SARS-CoV-2 genome sequences across ten Asian countries are collected, clustered, and characterized based on the clade they belong to. The isolates are compared to the Wuhan reference sequence” hCoV-19/Wuhan/WIV04/19″ to identify the mutations that occurred at different protein regions. Structural changes in amino acids due to mutations lead to functional instability of the proteins. Detailed clade-wise functional assessments are carried out to quantify the stability and vulnerability of the mutations occurring in SARS-CoV-2 genomes which can shade light on personalized prevention and treatment of the disease and encourage towards the invention of clade-specific vaccines.
Keywords: COVID-19, Clades, SARS-CoV-2, Deleterious mutations, Asia
1. Introduction
Viruses have a remarkable capacity to adapt to new hosts and environments (Sanjuán & Domingo-Calap, 2016). Mutations may lead to different phenotypic changes in them, which may lead to occur biodiversity. Phylogenies are frameworks for analysing biodiversity. Phylogenetic analysis based on sequence similarity is one of the very efficient way to do so (Das et al., 2020). However, it will be worth noting that due to the recent outbreak of pandemic COVID-19, people around the world are trying by every means to reach the origin, to get some ways of prevention and therapeutic pathways. Biodiversity is characterized by a continual replacement of branches in the tree of life, i.e. clade (Silvestro et al., 2015). Evolutionary pressure on host immunodeficiency leads to different clades of viruses (Tyor et al., 2013). A clade is a group of highly related sequences that share a common ancestor. They can provide hypotheses about the actual evolutionary history of that group of sequences. Some clinical studies suggest that the proclivity of virulence of a disease depends upon the type of clade the virus belongs to (Tyor et al., 2013). Clade differences can result in varying degrees of pathology. Millions of gene regulatory elements are there which contribute heavily to the variation in gene expression of complex human traits and diseases (Kumar et al., 2020a).Determining mutation types influence a lot in gene regulation and is important for studying the role of regulatory variation in evolution. Genomic evolution helps a virus to escape host immunity (Li et al., 2020a; DeDiego et al., 2008). The clade-wise analysis of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolates collected from different countries can shed a light on the actual evolutionary history of the region or continent. In order to confirm the hypothesis in Coronavirus disease 2019 (COVID-19) pathogenesis, it is highly recommended to make a thorough study of mutations occurring in SARS-CoV-2 isolates collected from different demographic areas and characterizing them based on the clades they come from (Andersen et al., 2020). A plethora of papers already have been published, where researchers have tried to study the virus isolates of SARS-CoV-2, which is solely responsible for the disease to occur in human (Zeng et al., 2004; Walls et al., 2020; Maitra et al., 2020; Biswas & Majumder, 2020; Kumar et al., 2020b). Huge numbers of investigations are reported in order to find evolutionary relationships between SARS-CoV-2 and other corona-viruses and to determine the origin and molecular characteristics of SARS-CoV-2 (DiMaio & Nathans, 1982; Banerjee et al., 2020; Foy et al., 2003). Several works are also done on characterization and comparative analysis of structured and non-structured proteins of SARS-CoV-2 (Astuti et al., 2020; Eaaswarkhanth et al., 2020). According to Hassan et al. (Hassan et al., 2020a) among all the accessory proteins of SARS-CoV-2, ORF3a plays an important role in virus pathogenesis, as it possesses various mutations which are linked with that of spike proteins.It is observed that due to its structural plasticity and high diversity, ORF8 plays important role in SARS-CoV-2 pathogenicity (Pereira, 2020).Computational biology approaches are applied to investigate genomic and proteomic variations of S protein in SARS-CoV-2. In the paper authors find possibilities to design potential inhibitors against S protein (Lokman et al., 2020). Kumar et al.stated the first observation on the deletion mutations in the C-terminal region of the envelope glycoprotein in India (Kumar et al., 2020c). Some researchers have given more stresses on codon usage bias in SARS-CoV-2 rather than mutational trends (Dutta et al., 2020). Several investigations are carried out on characterizing the mutations of a particular country and even across the globe (Saha et al., 2020; Hassan et al., 2020b; Hassan et al., 2020c). Researchers aimed too to analyse SARS-CoV-2 proteins modulating host immune response like type I interferon pathways (Li et al., 2020b). People have tried to uncover the relation between hotspot mutations and viral pathogenicity (Weber et al., 2020). Some research papers focused on characterizing B and T cell epitopes of certain proteins of SARS-CoV-2 which can help in vaccine development (Li et al., 2020c). Phylodynamic analyses of SARS-CoV-2 genomes can provide insights into the roles of some relevant factors to limit the spread of the disease (Nie et al., 2020). Priya and Shanker (Priya & Shanker, 2020) have observed that the coevolutionary forces can increase the fitness of spike glycoprotein against ACE2 and increase the infectivity of SARS-CoV-2. According to Uddin et al. (Uddin et al., 2020) antigenic epitopes of SARS-CoV-2 and SARS-CoV have highest level of similarities among them.SARS-CoV-2 is the seventh coronavirus to infect humans but the first HCoV which pandemic potential (Seyran et al., 2020). (Accession no:NC_045512) is the first SARS-CoV-2 sample SARS-CoV-2 sequence from Wuhan, and it is from clade’O′(Others) (Mercatelli & Giorgi, 2020). Clade ‘G' is the variant of the spike protein D614G which indicates significantly higher human host infectivity and better transmission efficiency to the virus. GH and GR are the most common offsprings of clade G. According to data from the public database of the Global Initiative on Sharing All Influenza Data (GISAID), three major clades of SARS-CoV-2 are clade G (variant of the spike protein S-D614G), clade V (a variant of the ORF3a coding protein NS3-G251V), and clade S (variant ORF8-L84S) (Mercatelli & Giorgi, 2020). GR clade, carrying the combination of Nucleocapsid: RG203KR, NSP3: F106F and Spike: D614G mutations. Although clade’GH’ is named after the mutation ORF3a: Q57H, but it also carries mutations NSP12b: P314L and S: D614G. Clade ‘L' refers to all genomes carrying reference alleles for all loci defined by clades G, GH, GR, S, and V. Clade O or’Others' is a general group carrying sequences which are not matching with the criteria the other clades have (Andersen et al., 2020). Different fatality rates observed in different countries may be the consequence of clade's differences in virulence. The spike protein of SARS-CoV-2 binds the host receptor angiotensin-converting enzyme 2 (ACE2) via receptor-binding domain (RBD). It is reported that immunization with SARS-CoV-2 receptor-binding domain (RBD) is able to induce clade-specific neutralizing antibodies in a host like mice (Yi et al., 2020). In some cases vaccines are immunogenic and induced antibodies can neutralize homologous and heterogeneous viruses with different degrees of cross-reactivity (Boonnak et al., 2017). Hence, in this present study, 1566 SARS-CoV-2 isolates from the Asian continent comprising 10 countries (India, Bangladesh, Pakistan, Srilanka, China, Japan, Malaysia, Iran, Thailand, and Saudi Arabia) are collected, clustered, and characterized based on the clade they belong to.
2. Methods and materials
2.1. Collection of gene sequences of SARS-COV-2
One of the primary features of the investigation and analysis of the COVID-19 is availability of real-time data in global databases. To carry out the experiment We have collected DNA sequence of 1566 isolates of SARS-CoV-2 from ten different Asian countries from the National Center for Biotechnology Information (NCBI) database (https://www.nih.gov/coronavirus) on October 20, 2020. The information about collected dataset are presented as Supplemental Materials in Table S1 and summarized in Table 1. After collecting all the DNA sequences, CoVsurver mutations App of GISAID (https://www.gisaid.org/epiflu-applications/covsurver-mutations-app) is used to compare the mutated sequences with reference sequence to find out the mutations occurred in each isolates in different protein regions along with clades they belong to. In this said web site the reference sequence is hCoV-19/Wuhan/WIV04/19. However, information in detail about the dataset Collected sequences are then gone through preliminary screening for excluding noisy sequences. Here noise includes no mutations and the amino acid changes due to mutations specified by ‘X'. Thus finally 1371 isolates are taken for further investigations.It is worth to mention that like GISAID CoVsurver mutation app, CovidGC (https://covidcg.org/)is also an open resource from which we can track SARS-CoV-2 SNVs (single-nucleotide variations) (Chen et al., 2020).Starting from tracking evolution to immune interactions, identifying the clades and lineages present in a region at a specific period of time, evaluation of community outcomes after particular vaccines, or therapeutics are applied, searching for transient mutations to illuminate common mechanism of resistance to grow immunity. But it works based on the data collected from GISAID only. Whereas,CoVsurver mutation app of GISAID works on influenza data as a whole and accepts protein or nucleotide sequences in a FASTA file from other resources like NCBI GeneBank.
Table 1.
Collected SARS-CoV-2 genome sequences from COVID patients from ten different Asian Countries.
| Country | # Associated | #Isolate |
|---|---|---|
| Name | Isolate | in work |
| INDIA | 570 | 565 |
| THAILAND | 227 | 104 |
| BANGLADESH | 231 | 231 |
| IRAN | 172 | 106 |
| CHINA | 189 | 189 |
| JAPAN | 96 | 96 |
| SAUDI ARABIA | 58 | 58 |
| PAKISTAN | 10 | 9 |
| MALAYSIA | 9 | 9 |
| SRILANKA | 4 | 4 |
2.2. Methods
The present work aims to make a clade-wise classification and analysis of SARS-CoV-2 isolates of ten Asian countries. The isolates of each country are then compared with the reference sequence to find out the mutations that occurred. Clade-wise clustering of the given dataset is taken place. The observed mutations are then gone through different online software tools to investigate different biological functionalities that may change and affect the variants due to mutations. Here it is to be noted that we have used two web-based software tools (PROVEAN (Choi & Chan, 2015) and I-mutant (Capriotti et al., 2005)) for the aforesaid functional assessments.I-Mutant is a suite developed based on Support Vector Machine(SVM). (ΔΔG> − 0.5 Kcal/mol) indicates that the mutation can largely destabilize the protein, ΔΔG >0.5 Kcal/mol indicates about the strong stability and − 0.5 ≥ΔΔG≥ 0.5 Kcal/mol tells about weak effect of mutations. Isolates with a score equal to or below −2.5 are considered deleterious and scores above −2.5 are neutral. Lastly, we tracked the trend of mutations that occurred in the sequences of different clades.
3. Results
3.1. Clade-wise clustering of SARS-CoV-2 strains taken as dataset from different countries
After excluding the noisy sequences finally 1371 isolates are found. Each strain belongs to a particular clade, so the isolates are clustered according to the clade from which they belong to. It has been observed that as a whole isolates of five clades (G, GH, GR, L, S, O, and V) are participated in those countries of the Asian continent. According to (Fig. 1 ) the order of the clade-wise participation of isolates is GH > GR > O > G > S > L > V. It is to be noted here that among the entire dataset taken Indian isolates hold a big amount of data. According to the country-wise view shown in Table 2 SARS-CoV-2 isolates of clade’O′ are present in the dataset of all countries and isolates of clade’V′ have been circulated only at China and Thailand. The country-wise analysis has a mixed result. In Srilanka,Thailand, China, Malaysia, and Iran the isolates are majorly from clade’O'or’Other’. India and Saudi Arabia have a prevalence of clade’GH’. Pakistan, Bangladesh, and Japan have the prevalence of clade ‘GR’. It indicates viral diversity regarding infection as the infection is transmitting from one country to another. Remarkable viral diversities are also present even in different regions within a country too.
Fig. 1.
Calculate percentage of clades representing the isolates.
Table 2.
Clade-wise counting of isolates reported in 10 countries.
| COUNTRY | G | GH | GR | L | S | O | V |
|---|---|---|---|---|---|---|---|
| THAILAND | 12 | 0 | 0 | 1 | 39 | 51 | 1 |
| BANGLADESH | 6 | 13 | 208 | 1 | 1 | 2 | 0 |
| SRILANKA | 1 | 0 | 1 | 0 | 0 | 2 | 0 |
| CHINA | 2 | 0 | 1 | 64 | 48 | 71 | 3 |
| JAPAN | 2 | 5 | 71 | 13 | 2 | 3 | 0 |
| INDIA | 191 | 303 | 18 | 7 | 19 | 27 | 0 |
| IRAN | 32 | 1 | 0 | 4 | 0 | 69 | 0 |
| MALAYSIA | 0 | 1 | 0 | 0 | 0 | 8 | 0 |
| PAKISTAN | 0 | 1 | 5 | 2 | 0 | 1 | 0 |
| SAUDI ARABIA | 2 | 32 | 22 | 0 | 1 | 1 | 0 |
3.2. Investigating trend of mutations in various clades
In this subsection firstly the positions of mutations are identified in each isolate and then it is aimed to calculate clade-wise percentage of mutations occurred in each country as shown in (Fig. 2 ). Secondly, a microscopic view has been given on clade-wise clustering of total mutations found in the whole dataset and calculating the protein-wise percentage of the mutations occurred according to the clades they belong to (Fig. 3 ).
Fig. 2.
Calculating clade-wise percentage of mutations occurred in different countries.
Fig. 3.
Quantitative analysis of clade-wise non-synonymous mutation. (a) Percentage of non-synonymous mutations found in different clades; (b) Clade-wise percentage of non-synonymous mutations occurred in different protein regions.
According to (Fig. 2), India and Saudi Arabia have a prevalence of clade’GH’ (55.76%, and 53.69% respectively). Whereas, in Pakistan, Bangladesh, and Japan strains have a prevalence of clade ‘GR’ (60.66%, 92.33% and 88.06% respectively). Mutations have occurred in strains from Clade’S′ at China and Thailand (51.63% and 45% respectively). SARS-CoV-2 isolates of clade’O′ have significant participation in Iran, Srilanka and Malaysia (59.31%, 46.67% and 94.29% respectively). SARS-CoV-2 isolates of clades G, GH, GR, S, L, and O are circulating in India and Japan. Whereas, clades V, O, S, L, GR, and G are circulating at different regions of China and clades O, S, GR, GH, and G are circulated in Saudi Arabia. In Malaysia (clades O and GH) and Srilanka (clades O, GR, and G) the SARS-CoV-2 isolates do not have the viral diversities a lot.
Mutations refer to the virus to undergo certain changes which can lead to develop some new isolates after replications. Non-synonymous substitutions play a very significant role as this type of mutation makes change in amino acid. Alteration in amino acid causes structural change. With the aim of understanding the trend of non-synonymous mutations in different clades in the context of disease severity, a detailed protein-wise comparative analysis has been taken place. Mutations identified at different protein regions in all the isolates are shown at Table S2 in Supplementary file. To do so we have considered the total dataset as a whole. Clade-wise percentages of non-synonymous mutations at different protein regions are calculated. The clade-wise characterization of mutations of different proteins are shown in (Fig. 3).
According to the dataset taken, we have got 6665 numbers of non-synonymous mutations. We can observe at Table 3 that the chronological order of clades at per number of mutations taken place in whole dataset is GR > GH > G > O > S > L > V. It can be observed in (Fig. 3) that mutations are majorly taken place at isolates of clades GH and GR which are 31.33% and 31.93% of respectively. Samples of clade V have been affected rarely (0.29%). Clade-wise distribution of mutations in each protein does not have a very similar trend(s). Although the majority of proteins mutated are either of clade GR (N, NS7a, NSP2, NSP6, NSP13, and NSP15) or GH (M, NS3, NSP1, NSP12, NSP14, and NSP16) but clade G also has large numbers of mutations in some proteins(S, NS6, and NSP11). Isolates of Clade L and clade G here have got mutations maximum only in protein E and NSP4 respectively. In proteins NS7b, NS8, and NSP7 of the isolates from clade S have maximum distributions of non-synonymous mutations. In the isolates of clade G, GR, and O number of mutations at NSP10 are equal. Mutations are also equally distributed in protein NSP5 of clades GH and GR.
Table 3.
Clade-wise segregation of mutated data.
| PROTEIN | G | GH | GR | L | S | O | V |
|---|---|---|---|---|---|---|---|
| E | 2 | 1 | 4 | 9 | 2 | 2 | 0 |
| M | 6 | 15 | 13 | 0 | 2 | 3 | 0 |
| N | 60 | 264 | 709 | 13 | 31 | 52 | 0 |
| NS3 | 30 | 376 | 709 | 12 | 10 | 24 | 3 |
| NS6 | 1 | 1 | 3 | 0 | 1 | 47 | 0 |
| NS7a | 4 | 3 | 1 | 1 | 2 | 0 | |
| NS7b | 0 | 1 | 0 | 6 | 0 | 0 | |
| NS8 | 2 | 18 | 18 | 1 | 69 | 4 | 0 |
| NSP1 | 4 | 11 | 1 | 9 | 2 | 0 | |
| NSP10 | 4 | 3 | 4 | 0 | 1 | 4 | 0 |
| NSP11 | 2 | 1 | 1 | 0 | 13 | 1 | |
| NSP12 | 241 | 383 | 366 | 9 | 18 | 45 | 3 |
| NSP13 | 24 | 21 | 26 | 7 | 3 | 12 | 0 |
| NSP14 | 49 | 109 | 20 | 4 | 5 | 10 | 0 |
| NSP15 | 22 | 10 | 31 | 5 | 5 | 11 | 0 |
| NSP16 | 20 | 23 | 9 | 11 | 1 | 11 | 0 |
| NSP2 | 52 | 82 | 245 | 9 | 11 | 20 | 4 |
| NSP3 | 109 | 114 | 156 | 27 | 21 | 82 | 0 |
| NSP4 | 50 | 8 | 10 | 16 | 9 | 11 | 0 |
| NSP5 | 16 | 24 | 24 | 1 | 4 | 3 | 0 |
| NSP6 | 3 | 25 | 32 | 4 | 4 | 26 | 7 |
| NSP7 | 0 | 1 | 1 | 0 | 3 | 1 | 0 |
| NSP8 | 2 | 7 | 8 | 1 | 14 | 7 | 0 |
| NSP9 | 4 | 4 | 1 | 0 | 1 | 0 | 0 |
| S | 317 | 583 | 384 | 18 | 34 | 599 | 1 |
| TOTAL MUTATION | 1024 | 2088 | 2128 | 150 | 265 | 991 | 19 |
Microscopic view towards mutations occurred gives a list of mutations which are found in number of isolates, shown at Table S2 in supplementary file. Mutation D614G in Spike protein is found in huge numbers of isolates throughout all the 9 countries except China and mutation P323L in protein NSP12 is affected lots of isolates in all 8 countries except China and Iran. Few other largely occurred mutations are (G204R, R203K, S194L) in N protein, Q57H in NS3, L84S in NS8, (L54F, A262T, A829T) in Spike protein.
3.3. Quantitative assessment of functional changes occur due to mutations
Structural changes in amino acids due to mutations lead to create functional instability of the isolates themselves, cause vulnerable diseases and even increase the magnitude of virulence. In this subsection, we have tried to find the impact of single point mutations on the biological function of proteins of each isolates through the light of PROVEAN (Protein Variation Effect Analyzer) score, which may be deleterious or neutral (Choi et al., 2012). We have also calculated the change in Gibbs free energy (ΔΔG) occur due to single point mutations as the difference in folding free energy change between wild type and mutant protein (ΔΔG) is considered as an impact factor of protein stability changes (Capriotti et al., 2008). The motivation here is to understand the effect of those mutations on protein stability. The quantitative analysis will give an insight into the probable mutations that occur in a particular clade and the magnitude of virulence of them. It is to be noted that here we have excluded the mutations which are occurred only once.The deleterious mutations are shown in Table 4 . It is observed at Table 4 that if we consider the dataset as a whole, then among structural proteins the mutations occurred in spike protein(s) are more deleterious than others. Among the accessory proteins, NS3 is affected the most. NSP2, NSP5, and NSP12 are the non-structural proteins that have most of the deleterious mutations that occurred. Furthermore, we have calculated clade-wise percentage of deleterious mutations that occurred in different protein regions. To do so, we have segregated each deleterious mutation occurred in ten different countries along with their clades Table 5 . The (Fig. 4 ) shows the protein regions that are mostly affected by the deleterious mutations. Maximum deleterious mutations occurred in structural and accessory proteins belong to clade GH. Most of the deleterious mutations in non-structural protein regions are occurred in the isolates of both clades GH and GR. The isolates of clade V are rare and only found in the isolates of China and Thailand, but interestingly it is observed that few of deleterious mutations are also enlisted there. (Fig. 4) depicts the fact that most of the deleterious mutations take place in amino acid sequences of clade GH.
Table 4.
Investigate the deleterious mutations in total dataset.
| PROTEIN | DELETERIOUS | PROVEAN | PROTEIN | DELETERIOUS | PROVEAN |
|---|---|---|---|---|---|
| MUTATION | SCORE | MUTATION | SCORE | ||
| S | D936Y | −2.60 | NSP2 | T85I | −4.09 |
| S | L752R | −3.84 | NSP2 | F368V | −2.69 |
| S | A288P | −2.80 | NSP2 | G339S | −3.13 |
| S | C851W | −10.16 | NSP2 | P129S | −3.17 |
| S | D820N | −4.67 | NSP2 | E309A | −3.33 |
| S | E725K | −3.75 | NSP3 | A1914D | −3.8 |
| S | G648R | −4.093 | NSP3 | P1558L | −4.532 |
| S | G669R | −3.58 | NSP4 | L111S | −1.54 |
| S | L533K | −2.62 | NSP4 | G309C | −6.73 |
| S | L916F | −3.74 | NSP5 | P108S | −4.53 |
| S | P863H | −5.12 | NSP5 | I106S | −4.41 |
| S | R905S | −5.62 | NSP5 | L75F | −3.79 |
| S | S758I | −4.13 | NSP5 | N142L | −4.3 |
| S | S875F | −3.37 | NSP5 | R279C | −6.46 |
| S | T716P | −3.347 | NSP6 | G277S | −6 |
| S | T874P | −5.59 | NSP6 | H64N | −2.97 |
| S | V534G | −3.12 | NSP12 | D303G | −6.05 |
| N | S193I | −2.76 | NSP1 | W161L | −7.8 |
| N | S194L | −4.27 | NSP13 | R392C | −2.73 |
| N | R191L | −3.27 | NSP14 | T113I | −4.25 |
| N | S180L | −3.40 | NSP15 | M330T | −5.62 |
| PROTEIN | DELETERIOUS | PROVEAN | NSP16 | R287I | −2.54 |
| MUTATION | SCORE | NSP16 | P12S | −7.73 | |
| NS3 | Q57H | −3.286 | NSP16 | G77R | −7.35 |
| NS3 | G251V | −8.58 | NSP12 | T803I | −3.55 |
| NS3 | D155Y | −6.83 | NSP12 | M124I | −3.048 |
| NS3 | G172C | −6.75 | NSP12 | P227S | −6.00 |
| NS3 | G172V | −6.76 | NSP12 | S607I | −2.83 |
| NS3 | N257D | −3.286 | NSP12 | A97V | −3.61 |
| NS3 | G251V | −8.58 | |||
| NS3 | W45R | −8.87 | |||
| NS7a | E95K | −2.61 | |||
| NS7a | P45L | −10 | |||
| NS7a | V104F | −2.83 | |||
| NS8 | P38R | −8.5 |
Table 5.
Clade-wise clustering of deleterious mutations in total dataset.
| MUTATION | G | GH | GR | L | S | O | V | MUTATION | G | GH | GR | L | S | O | V |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S193I | 1 | 7 | 0 | 0 | 0 | 0 | 0 | V104F | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| S194L | 21 | 190 | 0 | 0 | 0 | 2 | 0 | P38R | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| R191L | 2 | 0 | 0 | 0 | 0 | 0 | 0 | W161L | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| S180L | 1 | 1 | 0 | 0 | 0 | 0 | 0 | R392C | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| D936Y | 1 | 1 | 0 | 0 | 0 | 0 | 0 | T113I | 0 | 0 | 0 | 1 | 0 | 4 | 0 |
| L752R | 0 | 0 | 0 | 0 | 0 | 2 | 0 | M330T | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| A288P | 0 | 0 | 0 | 0 | 0 | 3 | 0 | R287I | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| C851W | 1 | 0 | 0 | 0 | 0 | 1 | 0 | P12S | 0 | 0 | 0 | 4 | 0 | 0 | 0 |
| D820N | 0 | 0 | 0 | 0 | 0 | 2 | 0 | G77R | 0 | 0 | 0 | 3 | 0 | 1 | 0 |
| E725K | 0 | 0 | 0 | 0 | 0 | 2 | 0 | T85I | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| G648R | 1 | 0 | 0 | 0 | 0 | 3 | 0 | F368V | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| G669R | 0 | 0 | 0 | 0 | 0 | 4 | 0 | G339S | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| L533K | 0 | 0 | 0 | 0 | 0 | 2 | 0 | P129S | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| L916F | 1 | 0 | 0 | 0 | 0 | 1 | 0 | E309A | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| P863H | 4 | 15 | 0 | 0 | 0 | 0 | 0 | A1914D | 2 | 3 | 0 | 0 | 0 | 0 | 0 |
| R905S | 2 | 0 | 0 | 0 | 0 | 0 | 0 | P1558L | 0 | 0 | 3 | 2 | 0 | 2 | 0 |
| S758I | 2 | 0 | 0 | 0 | 0 | 0 | 0 | L111S | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| S875F | 1 | 0 | 0 | 0 | 0 | 2 | 0 | G309C | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
| T716P | 0 | 0 | 0 | 0 | 0 | 2 | 0 | P108S | 0 | 0 | 7 | 0 | 0 | 0 | 0 |
| T874P | 0 | 0 | 0 | 0 | 0 | 3 | 0 | I106S | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| V534G | 0 | 0 | 0 | 0 | 0 | 2 | 0 | L75F | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
| Q57H | 12 | 342 | 7 | 0 | 0 | 7 | 0 | N142L | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
| G251V | 0 | 0 | 0 | 0 | 0 | 5 | 4 | R279C | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| D155Y | 0 | 2 | 5 | 0 | 0 | 0 | 0 | G277S | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| G172C | 0 | 0 | 3 | 0 | 0 | 0 | 0 | H64N | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| G172V | 0 | 0 | 3 | 0 | 0 | 0 | 0 | D303G | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| N257D | 1 | 1 | 0 | 0 | 0 | 0 | 0 | T803I | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| W45R | 0 | 0 | 0 | 7 | 0 | 0 | 0 | M124I | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| E95K | 1 | 1 | 0 | 0 | 0 | 0 | 0 | P227S | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| P45L | 0 | 2 | 0 | 0 | 0 | 0 | 0 | S607I | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| A97V | 0 | 0 | 0 | 3 | 0 | 22 | 0 |
Fig. 4.
Quantification of clade-wise deleterious non-synonymous mutation. (a) Clade-wise percentage of deleterious non-synonymous mutations in different protein regions; (b) Clade-wise calculations of deleterious non-synonymous mutations of total data set.
Table 6 gives us a microscopic view of the severity of the mutations that occurred in the dataset taken. In 82% of deleterious mutations protein stability has been decreased due to single point mutation. It is already observed that maximum mutations have occurred in the isolates which belong to clade GH. Out of 18 deleterious mutations happened in isolates from clade GH in 15 isolates (S194L, D936Y, P863H, W161L, F368V, E309A, A1914D, G309C, L75F, H64N, Q57H, N257D, E95K, P45L, V104F) stability have been decreased due to mutations. It can be observed at Table 7 that due to mutations majorly amino acid Glutamine and Serine are affected. Glutamine(Q) has been changed to Histidine(H), and Serine(S) changed to Leucine(L).
Table 6.
Investigating Stability of deleterious mutations in total dataset.
| Mutation | Mutation | Stability | ΔΔG | Mutation | Mutation | Stability | ΔΔG |
|---|---|---|---|---|---|---|---|
| type | type | ||||||
| S194L | Deleterious | Decreased | −0.47 | G77R | Deleterious | Decreased | −0.65 |
| D936Y | Deleterious | Decreased | −0.35 | T85I | Deleterious | Decreased | −0.91 |
| L752R | Deleterious | Decreased | −1.39 | F368V | Deleterious | Decreased | −1.74 |
| C851W | Deleterious | Decreased | −0.09 | G339S | Deleterious | Decreased | −1.2 |
| D820N | Deleterious | Decreased | −1.25 | P129S | Deleterious | Decreased | −1.82 |
| E725K | Deleterious | Decreased | −0.35 | E309A | Deleterious | Decreased | −0.96 |
| G648R | Deleterious | Decreased | −0.6 | A1914D | Deleterious | Decreased | −0.68 |
| G669R | Deleterious | Decreased | −0.14 | P1558L | Deleterious | Decreased | −0.36 |
| L533K | Deleterious | Decreased | −1.81 | L111S | Deleterious | Decreased | −2.29 |
| L916F | Deleterious | Decreased | −1.12 | G309C | Deleterious | Decreased | −1.07 |
| P863H | Deleterious | Decreased | −1.44 | P108S | Deleterious | Decreased | −1.73 |
| R905S | Deleterious | Decreased | −1.18 | I106S | Deleterious | Decreased | −2.24 |
| T716P | Deleterious | Decreased | −0.8 | L75F | Deleterious | Decreased | −1.09 |
| T874P | Deleterious | Decreased | −0.22 | R279C | Deleterious | Decreased | −0.67 |
| V534G | Deleterious | Decreased | −2.029 | G277S | Deleterious | Decreased | −1.3 |
| Q57H | Deleterious | Decreased | −0.9 | H64N | Deleterious | Decreased | −0.03 |
| G251V | Deleterious | Decreased | −0.54 | D303G | Deleterious | Decreased | −0.9 |
| G172C | Deleterious | Decreased | −0.83 | M124I | Deleterious | Decreased | −0.53 |
| G172V | Deleterious | Decreased | −0.41 | P227S | Deleterious | Decreased | −1.49 |
| N257D | Deleterious | Decreased | −0.21 | S193I | Deleterious | Increased | −0.29 |
| W45R | Deleterious | Decreased | −1.05 | R191L | Deleterious | Increased | −0.26 |
| E95K | Deleterious | Decreased | −0.58 | S180L | Deleterious | Increased | −0.12 |
| P45L | Deleterious | Decreased | −0.71 | A288P | Deleterious | Increased | −0.29 |
| V104F | Deleterious | Decreased | −1.47 | S758I | Deleterious | Increased | 0.43 |
| P38R | Deleterious | Decreased | −0.9 | S875F | Deleterious | Increased | 0.66 |
| W161L | Deleterious | Decreased | −0.51 | D155Y | Deleterious | Increased | 0.21 |
| R392C | Deleterious | Decreased | −1.29 | N142L | Deleterious | Increased | −0.05 |
| T113I | Deleterious | Decreased | −0.43 | T803I | Deleterious | Increased | 0.18 |
| M330T | Deleterious | Decreased | −1 | S607I | Deleterious | Increased | 0.37 |
| R287I | Deleterious | Decreased | −0.66 | A97V | Deleterious | Increased | −0.31 |
| P12S | Deleterious | Decreased | −1.29 |
Table 7.
Amino Acid changed due to deleterious mutations taken place.
| MUTATION | COUNT | MUTATION | COUNT |
|---|---|---|---|
| Q > H | 368 | M > I | 3 |
| S > L | 215 | P > R | 3 |
| A > V | 25 | R > I | 3 |
| P > H | 19 | S > F | 3 |
| P > S | 16 | C > W | 2 |
| G > R | 12 | D > G | 2 |
| G > V | 12 | D > N | 2 |
| S > I | 12 | E > A | 2 |
| P > L | 9 | E > L | 2 |
| T > I | 9 | F > V | 2 |
| D > Y | 9 | H > N | 2 |
| W > R | 7 | I > S | 2 |
| G > C | 6 | L > K | 2 |
| A > D | 5 | L > R | 2 |
| L > F | 5 | L > S | 2 |
| N > L | 5 | M > T | 2 |
| T > P | 5 | N > D | 2 |
| E > K | 4 | R¿ > L | 2 |
| R > C | 4 | R > S | 2 |
| A > P | 3 | V > F | 2 |
| G > S | 3 | V > G | 2 |
4. Discussion
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously changing its characteristics and degree of infectivity. Mutations can strengthen its severity and infectivity. It is an important observation that although both the mutations P323L in protein region NSP12 and D614G in Spike protein are mostly occurred mutations in the whole dataset taken and is globally dominant. Although the mutations change the structural stability of proteins by making changes in free energy but they are not deleterious. It reveals that the mutations are not deleterious but have great impact on the biological functions of the proteins respectively. According to the papers reported, G614 variant has higher transmission ability than the D614 variant, associated with low RT-PCR cycle thresholds, but does not increase disease severity (Korber et al., 2020). In other hand the P323L mutation is located in the interface domain of the RNA-dependent RNA polymerase (RdRp) and it changes the intramolecular interactions in the protein as its stability changes. Among the remaining most frequently observed non-synonymous mutations, S194L in N protein and Q57H in NS3 are deleterious. Clade-wise analysis of mutations performed here in this present study indicates that in Asian countries SARS-CoV-2 isolates responsible for COVID-19 majorly belong to the clades GR and GH. Among them mutations that occurred in isolates of clade GH are deleterious in nature, so have an impact on the biological function of proteins. The mutations also change the structural stability of proteins by making changes in free energy(ΔG). It is reported that the human genome may carry large numbers of deleterious mutations which as a whole make a significant contribution to fatal diseases. Identification and analysis of deleterious mutations can shade lights on personalized treatment and medicine (Chun & Fay, 2009). Hence, the identification of these kinds of mutations in SARS-CoV-2 isolates and their impacts on the host body seek attention of virologists.
5. Conclusions
The in silico analysis performed in this study states that the isolates in ten Asian countries are from clades G, GH, GR, L, S, O, and V. It indicates the diversity of the infection indeed. O, GH, and GR are the most widely affected ancestors of isolates among them. But when there is a talk about mutations, 31.93% of total mutations have taken place in the isolates of clade GR, and 31.33% of the mutations from GH. Hence, number of mutations are really high in the isolates belong to both the clades. When clades G, GH, and GR traversed almost in all countries specified here, the isolates of clade V are affected rarely. The most frequently mutated amino acids are Glutamine and Serine. In most of the cases glutamine is changed into Histidine and serine is changed to Leucine. It is to be noted that both the mutations are deleterious and the isolates of clade GH carry the major deleterious mutation load (44.19% of the total dataset). The majority of mutations taken place in the isolatess of clade GH are deleterious in nature. 82% of deleterious mutations are unstable and so their biological functions are affected. As a whole in this present work, the investigation provides us clade-wise characteristics of the SARS-CoV-2 isolates of the Asian continent. When reported research papers shed the light on development of clade-specific vaccines (Yi et al., 2020), our analysis can encourage drug designers for development of customized drugs or vaccines for Asian continent in order to combat COVID-19.
The following are the supplementary data related to this article.
Country-wise specifications of isolates with the clades they belong to.
Table S2. Identifying mutations at different protein regions of the dataset taken.
Credit authorship contribution statement
Antara Sengupta: Investigation, Conceptualization, Data curation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing. Sk. Sarif Hassan: Conceptualization, Visualization, Validation, Writing - review & editing. Pabitra Pal Choudhury: Supervision, Validation, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- Andersen K.G., Rambaut A., Lipkin W.I., Holmes E.C., Garry R.F. The proximal origin of sars-cov-2. Nat. Med. 2020;26:450–452. doi: 10.1038/s41591-020-0820-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Astuti I., et al. Diabetes & Metabolic Syndrome; Clinical Research & Reviews: 2020. Severe Acute Respiratory Syndrome Coronavirus 2 (Sars-Cov-2): An Overview of Viral Structure and Host Response. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Banerjee A., Sarkar R., Mitra S., Lo M., Dutta S., Chawla-Sarkar M. The novel coronavirus enigma: phylogeny and analyses of coevolving mutations among the sars-cov-2 viruses circulating in India. JMIR Bioinformatics and Biotechnology. 2020;1 doi: 10.2196/20735. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Biswas N.K., Majumder P.P. Analysis of rna sequences of 3636 sars-cov-2 collected from 55 countries reveals selective sweep of one virus type. Indian J. Med. Res. 2020;151(5):450–458. doi: 10.4103/ijmr.IJMR_1125_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Boonnak K., Matsuoka Y., Wang W., Suguitan A.L., Chen Z., Paskel M., Baz M., Moore I., Jin H., Subbarao K. 2017. Development of Clade-Specific and Broadly Reactive Live Attenuated Influenza Virus Vaccines against Rapidly Evolving h5 Subtype Viruses, Journal of Virology 91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Capriotti E., Fariselli P., Casadio R. I-mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 2005;33:W306–W310. doi: 10.1093/nar/gki375. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Capriotti E., Fariselli P., Rossi I., Casadio R. A three-state prediction of single point mutations on protein stability changes. BMC bioinformatics. 2008;9:S6. doi: 10.1186/1471-2105-9-S2-S6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chen A.T., Altschuler K., Zhan S.H., Chan Y.A., Deverman B.E. Covid-19 cg: tracking sars-cov-2 mutations by locations and dates of interest. Biorxiv. 2020 doi: 10.7554/eLife.63409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Choi Y., Chan A.P. Provean web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015;31:2745–2747. doi: 10.1093/bioinformatics/btv195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Choi Y., Sims G.E., Murphy S., Miller J.R., Chan A.P. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7 doi: 10.1371/journal.pone.0046688. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chun S., Fay J.C. Identification of deleterious mutations within three human genomes. Genome Res. 2009;19:1553–1561. doi: 10.1101/gr.092619.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Das J.K., Sengupta A., Choudhury P.P., Roy S. Mapping sequence to feature vector using numerical representation of codons targeted to amino acids for alignment-free sequence analysis. Gene. 2020;766:145096. doi: 10.1016/j.gene.2020.145096. [DOI] [PubMed] [Google Scholar]
- DeDiego M.L., Pewe L., Alvarez E., Rejas M.T., Perlman S., Enjuanes L. Pathogenicity of severe acute respiratory coronavirus deletion mutants in hace-2 transgenic mice. Virology. 2008;376:379–389. doi: 10.1016/j.virol.2008.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- DiMaio D., Nathans D. Regulatory mutants of simian virus 40: effect of mutations at a t antigen binding site on dna replication and expression of viral genes. J. Mol. Biol. 1982;156:531–548. doi: 10.1016/0022-2836(82)90265-0. [DOI] [PubMed] [Google Scholar]
- Dutta R., Buragohain L., Borah P. Analysis of codon usage of severe acute respiratory syndrome corona virus 2 (sars-cov-2) and its adaptability in dog. Virus Res. 2020;288:198113. doi: 10.1016/j.virusres.2020.198113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Eaaswarkhanth M., Al Madhoun A., Al-Mulla F. Could the d614 g substitution in the sars-cov-2 spike (s) protein be associated with higher covid-19 mortality? Int. J. Infect. Dis. 2020;96:459–460. doi: 10.1016/j.ijid.2020.05.071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Foy E., Li K., Wang C., Sumpter R., Ikeda M., Lemon S.M., Gale M. Regulation of interferon regulatory factor-3 by the hepatitis c virus serine protease. Science. 2003;300:1145–1148. doi: 10.1126/science.1082604. [DOI] [PubMed] [Google Scholar]
- Hassan S.S., Choudhury P.P., Basu P., Jana S.S. Molecular conservation and differential mutation on orf3a gene in indian sars-cov2 genomes. Genomics. 2020;112(5):3226–3237. doi: 10.1016/j.ygeno.2020.06.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hassan S.S., Choudhury P.P., Roy B., Jana S.S. 2020. Missense Mutations in Sars-cov2 Genomes from Indian Patients. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hassan S.S., Choudhury P.P., Roy B. 2020. Sars-cov2 Envelope Protein: Non-synonymous Mutations and its Consequences. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Korber B., Fischer W.M., Gnanakaran S., Yoon H., Theiler J., Abfalterer W., Hengartner N., Giorgi E.E., Bhattacharya T., Foley B., et al. Tracking changes in sars-cov-2 spike: evidence that d614g increases infectivity of the covid-19 virus. Cell. 2020;182:812–827. doi: 10.1016/j.cell.2020.06.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kumar S.U., Kumar D.T., Christopher B.P., Doss C. The rise and impact of covid-19 in India. Frontiers in Medicine. 2020;7:250. doi: 10.3389/fmed.2020.00250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kumar R., Verma H., Singhvi N., Sood U., Gupta V., Singh M., Kumari R., Hira P., Nagar S., Talwar C., et al. 2020. Comparative Genomic Analysis of Rapidly Evolving Sars-Cov-2 Reveals Mosaic Pattern of Phylogeographical Distribution, Msystems 5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kumar B.K., Rohit A., Prithvisagar K.S., Rai P., Karunasagar I., Karunasagar I. Deletion in the c-terminal region of the envelope glycoprotein in some of the indian sars-cov-2 genome. Virus Res. 2020;198222 doi: 10.1016/j.virusres.2020.198222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li Y., Yang X., Wang N., Wang H., Yin B., Yang X., Jiang W. The divergence between sars-cov-2 and ratg13 might be overestimated due to the extensive rna modification. Futur. Virol. 2020;15(6):341–347. [Google Scholar]
- Li J.-Y., Liao C.-H., Wang Q., Tan Y.-J., Luo R., Qiu Y., Ge X.-Y. The orf6, orf8 and nucleocapsid proteins of sars-cov-2 inhibit type i interferon signaling pathway. Virus Res. 2020;286:198074. doi: 10.1016/j.virusres.2020.198074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li L., Sun T., He Y., Li W., Fan Y., Zhang J. Epitope-based peptide vaccines predicted against novel coronavirus disease caused by sars-cov-2. BioRxiv. 2020;288 doi: 10.1016/j.virusres.2020.198082. 1980822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lokman S.M., Rasheduzzaman M., Salauddin A., Barua R., Tanzina A.Y., Rumi M.H., Hossain M.I., Siddiki A.Z., Mannan A., Hasan M.M. Infection; Genetics and Evolution: 2020. Exploring the Genomic and Proteomic Variations of Sars-Cov-2 Spike Glycoprotein: A Computational Biology Approach; p. 104389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Maitra A., Sarkar M.C., Raheja H., Biswas N.K., Chakraborti S., Singh A.K., Ghosh S., Sarkar S., Patra S., Mondal R.K., et al. 2020. Mutations in Sars-Cov-2 Viral Rna Identified in Eastern India: Possible Implications for the Ongoing Outbreak in India and Impact on Viral Structure and Host Susceptibility, Journal of Biosciences 45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mercatelli D., Giorgi F.M. 2020. Geographic and Genomic Distribution of Sars-Cov-2 Mutations. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nie Q., Li X., Chen W., Liu D., Chen Y., Li H., Li D., Tian M., Tan W., Zai J. Phylogenetic and phylodynamic analyses of sars-cov-2. Virus Res. 2020;287:198098. doi: 10.1016/j.virusres.2020.198098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pereira F. Evolutionary dynamics of the sars-cov-2 orf8 accessory gene, infection. Genetics and Evolution. 2020;85:104525. doi: 10.1016/j.meegid.2020.104525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Priya P., Shanker A. Infection; Genetics and Evolution: 2020. Coevolutionary Forces Shaping the Fitness of Sars-Cov-2 Spike Glycoprotein against Human Receptor ace2; p. 104646. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saha I., Ghosh N., Maity D., Sharma N., Mitra K. Inferring the genetic variability in indian sars-cov-2 genomes using consensus of multiple sequence alignment techniques, infection. Genetics and Evolution. 2020;85:104522. doi: 10.1016/j.meegid.2020.104522. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sanjuán R., Domingo-Calap P. Mechanisms of viral mutation. Cell. Mol. Life Sci. 2016;73:4433–4448. doi: 10.1007/s00018-016-2299-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Seyran M., Pizzol D., Adadi P., El-Aziz T.M.A., Hassan S.S., Soares A., Kandimalla R., Lundstrom K., Tambuwala M., Aljabali A.A., et al. Questions concerning the proximal origin of sars-cov-2. J. Med. Virol. 2020:1–3. doi: 10.1002/jmv.26478. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Silvestro D., Antonelli A., Salamin N., Quental T.B. The role of clade competition in the diversification of north american canids. Proc. Natl. Acad. Sci. 2015;112:8684–8689. doi: 10.1073/pnas.1502803112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tyor W., Fritz-French C., Nath A. Effect of hiv clade differences on the onset and severity of hiv-associated neurocognitive disorders. J. Neurovirol. 2013;19:515–522. doi: 10.1007/s13365-013-0206-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Uddin M.B., Hasan M., Harun-Al-Rashid A., Ahsan M.I., Imran M.A.S., Ahmed S.S.U. Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (sars-cov-2): global burden and Bangladesh perspective, infection. Genetics and Evolution. 2020;84:104440. doi: 10.1016/j.meegid.2020.104440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Walls A.C., Park Y.-J., Tortorici M.A., Wall A., McGuire A.T., Veesler D. 2020. Structure, Function, and Antigenicity of the Sars-Cov-2 Spike Glycoprotein, Cell. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Weber S., Ramirez C., Doerfler W. Signal hotspot mutations in sars-cov-2 genomes evolve as the virus spreads and actively replicates in different parts of the world. Virus Res. 2020;289:198170. doi: 10.1016/j.virusres.2020.198170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yi C., Sun X., Ye J., Ding L., Liu M., Yang Z., Lu X., Zhang Y., Ma L., Gu W., et al. Key residues of the receptor binding motif in the spike protein of sars-cov-2 that interact with ace2 and neutralizing antibodies. Cellular & Molecular Immunology. 2020:1–10. doi: 10.1038/s41423-020-0458-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zeng R., Yang R.-F., Shi M.-D., Jiang M.-R., Xie Y.-H., Ruan H.-Q., Jiang X.-S., Shi L., Zhou H., Zhang L., et al. Characterization of the 3a protein of sars-associated coronavirus in infected vero e6 cells and sars patients. J. Mol. Biol. 2004;341:271–279. doi: 10.1016/j.jmb.2004.06.016. [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
Country-wise specifications of isolates with the clades they belong to.
Table S2. Identifying mutations at different protein regions of the dataset taken.