Skip to main content
NCBI home page
ACS Omega logoLink to ACS Omega
. 2025 Aug 14;10(33):37188–37200. doi: 10.1021/acsomega.5c02268

Subtractive Proteomic Approaches to Identify Drug Targets: An Analysis on Hypothetical Proteins of Multidrug-Resistant P. aeruginosa

Neelam Kushwaha †,‡, Deepak Nandi †,§, Balvinder Singh †,‡,*
PMCID: PMC12392004  PMID: 40893232

Abstract

Pseudomonas aeruginosa is a Gram-negative pathogen responsible for nosocomial infections. The exploration of novel therapeutic targets within its proteome is essential for developing successful therapies for combating infections caused by this organism. Employing in silico approaches, this study aims to identify drug targets from hypothetical proteins of P. aeruginosa. These proteins were extracted from complete proteomes of 351 strains of P. aeruginosa after removing duplicates and taxonomically misclassified strains. The essentiality analysis revealed the presence of 1923 essential hypothetical proteins (EHPs) in these strains. These proteins were characterized based on physicochemical properties, subcellular locations, antimicrobial resistance, virulence and druggability. The domain analysis and functional annotation classified these as enzymes, transporters, and secretion proteins. Among these annotated proteins, 149 EHPs were found to be virulent proteins. The majority of these proteins are found to be involved in antibiotic inactivation and antibiotic efflux pumps. One hypothetical protein was characterized as a small multidrug resistance (SMR) efflux pump protein. Sixty-one EHPs were identified as potential drug targets, and 33 among these were identified in 15 different strains. The network analysis on these targets revealed a hub protein cobH, along with small clusters and singletons. These drug targets can be identified and validated experimentally before exploiting them in development of drugs against P. aeruginosa.

graphic file with name ao5c02268_0008.jpg

Introduction

Pseudomonas aeruginosa is a rod-shaped, motile, opportunistic, nosocomial, and free-living bacterium, posing a global threat to immunocompromised individuals, and is found in water, soil, parts of plants, animals, and humans. It is known to be associated with humans, and infections range from acute to chronic and life-threatening. , The infections caused by P. aeruginosa have a greater risk of fatality as compared to other opportunistic Gram-negative pathogenic infections. , It possesses a large genome size of 5.5 to 7 Mb with a high degree of genomic flexibility. , The genomic flexibility inherently conferred intrinsic resistance to numerous antibiotics and facilitated the acquisition of genes that contribute toward antibiotic resistance. , Multiple resistance mechanisms have evolved over time in P. aeruginosa, making it harder to treat and restricting various therapeutic alternatives. ,− P. aeruginosa strains have been found to be resistant to several antimicrobial drugs, such as fluoroquinolones, third-generation cephalosporins, and carbapenems, which are used to treat severe infections caused by multidrug-resistant (MDR) strains. , P. aeruginosa spreads from hospitals into the environment due to the presence of antimicrobial compounds in hospital effluents, which impose continual selection pressure and elevate antimicrobial resistance. The prevalence rate reported for P. aeruginosa infections in India ranges from 10.5 to 30%. Consequently, P. aeruginosa was identified as a significant public health threat among the 12 bacterial families. It was categorized as a top priority bacterium for antibiotic resistance by the WHO (World Health Organization) in 2017. Earlier, it was reported in many organisms that the molecular function of around 30% proteins is uncharacterized; these proteins are referred to as HPs (hypothetical proteins). The number of HPs continues to grow rapidly due to the ongoing surge of whole genome sequencing data deposition on public databases. The functional annotation of HPs can disclose their functions in various metabolic processes and lead to the discovery of new potential therapeutic targets inside an organism. Forty-eight percent of the genome was found to encode for HPs in P. aeruginosa. These HP sequences were determined by the translation of nucleotide sequence similarity; nevertheless, an assessment of their biochemical and functional characteristics is required for these to be used in experimental analysis. It was reported that HPs can be missing links between genomic and proteomic information. To understand the genetic architecture of P. aeruginosa and its response to environmental stimuli, the genome and proteome were analyzed in many previous studies. , The functional characterization of the vast number of hypothetical proteins is both time-consuming and resource-intensive if experimental methods are used. In silico techniques provide a quick way to predict functions and prioritize candidates for laboratory validation. The functional annotations of HPs based on homology were reported in several organisms. For example, in the case of P. aeruginosa, Reem et al. successfully performed functional annotation and classified 30 HPs into different categories, such as enzymes, transporters, and binding proteins, in the PA01 strain. Similarly, Ijaq et al. identified 743 HPs in Pseudomonas sp. Lz4W and 61 HPs experimentally. In addition, Uddin and Jamil performed proteomic analysis on 68 strains of P. aeruginosa and found 321 EHPs (essential hypothetical proteins) that were nonhomologous to the human proteome and proposed 8 potential novel drug targets. Druggability analysis of a large number of strains (more than 350) has not yet been carried out, and around half of the proteome remains uncharacterized in P. aeruginosa. The hypothesis of this study was that EHPs, conserved across multiple strains of Pseudomonas aeruginosa, may harbor novel drug or vaccine targets that are currently unexplored. Thus, the aim of this study was to explore novel drug or vaccine targets from these EHPs using subtractive proteomics in multiple strains of P. aeruginosa. Furthermore, functional annotation, virulence, antimicrobial resistance, nonhomologues against human proteins, druggability, and protein–protein interaction analysis were performed using an in silico approach. This would provide a comprehensive understanding of intracellular signaling pathways and various regulatory mechanisms encompassing uncharacterized proteins. Thus, the functional annotation, domain, nonhomologous, and protein–protein interaction analyses of hypothetical proteins, which were essential across strains, can open avenues for the identification of new potential targets and facilitate drug repositioning.

Methods

Data Retrieval and Analysis

The complete proteome of 362 strains of P. aeruginosa was downloaded from the NCBI (National Center for Biotechnology Information) database. Five duplicates (based on submission date and strain name) and six taxonomically unidentified strains were removed, and thus, the remaining 351 strains were considered. The complete proteome of these strains was extracted after removing the 1085 plasmid sequences. The 2,32,641 HPs were extracted from the proteome of 351 strains using a customized Python script. The redundant HPs were eliminated using the CD-HIT suite from a total of 2,32,641 hypothetical sequences at 100% identity. To ensure that only protein sequences were present as hypothetical proteins, the sequences below 100 amino acids were removed, resulting in 24,845 hypothetical protein sequences for further analysis.

Essential Gene Analysis

Sequence similarity search against the Essential Gene Database (DEG) was carried out using the remaining 24,845 sequences at an e-value of 0.001 via BLASTp. The hypothetical protein sequences with less than 100 bit scores were removed from these. Thus, the new collection of HPs was classified as EHPs that consisted of 2126 sequences. Furthermore, this collection of EHPs was again sorted and manually verified along with DEG IDs (belonging to Pseudomonas aeruginosa), leading to the removal of duplicates and thus providing 1923 nonredundant EHPs.

Physiochemical Property Analysis

The ProtParam server from ExPASy was utilized to perform the physicochemical analysis. The physicochemical properties, i.e., Instability Index (II), Molecular Weight (MW), Aliphatic Index, Isoelectric Point, and (GRAVY) Grand Average of Hydropathicity of 1923 EHPs, were calculated using a customized Bioseq Utiliz script (https://biopython.org/docs/1.76/api/Bio.SeqUtils.ProtParam.html).

Subcellular Localization Analysis

Subcellular location provides important information about the function of a protein. PSORTb v3.0 and CELLO were used to identify the subcellular location of EHPs. The number of transmembrane domains and their topology were identified with DeepTMHMM. SignalP v6.0 was utilized for the identification of the presence of SPs (signal peptides) in EHPs.

Domain Identification

InterProScan was used for the identification of domains in the 1923 EHPs. It provides an integrative classification of proteins and can sort the protein sequences into superfamilies, families, functional domains, and conserved sites. These EHPs were subjected to analysis using a standalone LINUX-based tool available at the InterproScan database (https://www.ebi.ac.uk/interpro/search/sequence/). The databases like CDD, SMART, PROSITE, Pfam, and many others − were used to find the putative domains in EHPs.

Functional Annotation

The functional annotation of the EHPs was carried out by employing a tool/database viz. Sma3s, STRING, KEGG, and KOFAM. The results of this analysis provided Gene Ontology, i.e., GO terms. These were then manually cross-verified to obtain all possible GO terms for each and every sequence. These GO terms were plotted by using an in-house Python script to identify the most enriched terms.

Antimicrobial Resistance Analysis

The antimicrobial resistance (AMR) analysis was carried out on 1923 EHPs. In this analysis, the Resistance Gene Identifier, i.e., RGI tool, was used to identify AMR proteins in EHPs. The RGI tool uses CARD (Comprehensive Antibiotic Resistance Database) for the identification of protein sequences that provide resistance to microbes.

Virulence Analysis

The virulent protein sequences were retrieved from VFDB, i.e., Virulence Factor Database. The EHPs were subjected to a sequence similarity search via BLASTp, with an e-value of 0.001 against VFDB protein sequences, to identify virulent proteins.

Nonhomologous Human Proteome Analysis

Nonhomologous sequence analysis was carried out against the human proteome on 1923 EHPs. In this analysis, the nonredundant protein sequences of the human reference proteome (https://www.uniprot.org/proteomes/UP000005640) from the UniProtKB database were retrieved and subjected to sequence similarity search against 1923 EHPs using BLASTp with an e-value of 0.0001. The EHPs, which showed no matches against the human proteome sequence, were used for further analysis.

Druggability Analysis

In this analysis, the drug targets were identified by comparing 1923 sequences of EHPs with the entire databases of TTD, ChEMBL, and DrugBank. The EHPs were subjected to a similarity search on the local server against these three databases using BLASTp and PSI-BLAST, with an e-value kept at 0.001. The results of the sequence similarity search were then compared, and redundant prediction of targets among/across databases was addressed. Finally, the EHPs that showed more than 40% sequence identity were classified as drug targets. Also, the drug targets that were identified and published by different research groups were retrieved via a literature search. These were compared with EHPs to verify novel and previously known drug targets in P. aeruginosa.

Prediction of Protein–Protein Interaction in Identified Drug Targets

Sixty-one EHPs identified as drug targets were subjected to prediction of protein–protein interaction (PPI) analysis employing the STRING Version 12.0 database to identify hub proteins and functional interactions of these drug targets. The interacting partners were identified by applying the highest confidence score criteria, i.e., 0.900. The hub protein was identified as having the highest interaction partners with EHPs and the STRING database (i.e., 10 interacting partners were selected).

Structural Identification of EHPs

Protein Data Bank (PDB) was searched for the term “Essential hypothetical protein,” and it yielded no PDB ID hits. However, another search using the keyword “hypothetical protein” was made, and it retrieved 230 PDB IDs of Pseudomonas aeruginosa. A sequence similarity search was performed by comparing 1923 EHPs against this set of 230 PDB sequences using BLASTp, with an E-value of 0.00001. Also, using another approach, a sequence similarity search was carried out against the entries in PDB for 1923 EHPs to find the structures of EHPs an at e-value of 0.00001 using BLASTp.

Results

Sequence Retrieval and Characterization

The protein sequences were downloaded for 13 and 349 strains of P. aeruginosa from RefSeq and GenBank, respectively. These were sorted alphabetically and renamed according to the strain name. Five duplicate strains (based on submission date and strain name) and six taxonomically misclassified strains were removed from the obtained data. Thus, only 351 strains in total were considered further. The protein sequences were extracted as a single FASTA format file that contained 21,30,988 sequences. However, 1085 plasmid sequences were removed from these. The 2,32,641 HPs were retrieved based on the header information from these sequences. Among these, 24,845 sequences of hypothetical proteins had more than 100 amino acids.

Essentiality Analysis

The results of sequence similarity search of 24,845 HPs against DEG led to the identification of 1923 sequences that were termed as EHPs. These EHPs were identified in 241 strains out of 351. The strains with the highest number of EHPs were PAO1 (707 EHPs), Cu1510 (215 EHPs), and VRFPA04 (64 EHPs), while there were 23 strains with 5 EHPs, 28 strains with 4 EHPs, 43 strains with 3 EHPs, 49 strains with 2 EHPs, and 47 strains with a single EHP, as shown in Table S1.

Physicochemical Analysis

One thousand nine hundred and twenty-three EHPs were subjected to physicochemical analysis based on the protein sequences, and the results of this analysis are presented in Figure and Table S2. The analysis revealed that the molecular weight of EHPs ranged between 10472.72 and 458572.98 Da (Da), as observed for WP_046094960.1 and AID85076.1 EHP, respectively. The Isoelectric Point (pI) of 1923 EHPs was observed between 4.05 (for AIL00203.1) and 11.09 (for WP_254077144.1). There were 16 EHPs that had 4.05 as the isoelectric point (pI). It was observed that 1127 EHPs were acidic and two were neutral, while 794 EHPs were basic in nature. The range of the Instability Index observed was from 6.81 (WP_222821372.1) to 87.83 (WP_023129064.1).

1.

1

Open in a new tab

Distribution of physicochemical parameters in 1923 EHPs as box plots, where the median is shown by a black middle line in each box. The lower boundary of each box shows the 25th percentile, while the upper boundary shows the 75th percentile. The whiskers above and below correspond to the highest and lowest normal values, respectively, while the outliers are shown by circles above or below the whiskers. (A) Molecular weight distribution shown in blue color. (B) Instability index is shown in green color. (C) Isoelectric point (pI) values are shown in yellow color. (D) GRAVY (Grand Average of Hydropathy) index is shown in red color.

Out of the 1923 EHPs, 921 were predicted to be stable in in vitro conditions, while the rest of 1003 were predicted to be unstable in in vitro conditions, as predicted by ProtParam. The GRAVY index was observed in the range of −1.07 to 1.44, as observed in the case of ALS14232.1 and NP_254223.1 EHPs, respectively. The negative GRAVY values corresponded to hydrophilic proteins, while the positive values corresponded to hydrophobic proteins, which accounted for 1304 and 478 EHPs, respectively. Six hundred and one sequences were found to be stable and hydrophilic in nature based on GRAVY and Instability Index.

Subcellular Localization

The analysis of subcellular localization using PSORTb classified 756 EHPs as “Cytoplasmic”, 30 as “Extracellular”, 16 as “Periplasmic”, 401 as “Cytoplasmic Membrane,” and 70 as “Outer Membrane”, as shown in Figure A. 650 EHPs were not characterized to any location and were labeled as “Unknown” by PSORTb. To predict the subcellular localization of Unknown labeled sequences and verify the predictions made by PSORTb, we used the CELLO server.

2.

2

Open in a new tab

Distribution of subcellular location of EHPs sequences by using four tools: PSORTb, CELLO, deepTMHMM, and SignalP. (A) Different subcellular locations identified in EHPs, where PSORTb predictions are presented in blue, while those of CELLO are presented in yellow. (B) Different topologies of transmembrane regions identified in EHPs, where Globular [GLOB] are shown in blue, Alpha Helix Transmembrane [TM] in green, Signal Peptide [SP] in red, Alpha Helix Transmembrane with Signal Peptide [SP+TM] in purple, and β Barrel Transmembrane [BETA] in yellow. (C) Different types of Signal Peptides are identified in EHPs. The standard signal peptides [SP] are shown in cyan-blue, lipoprotein signal peptides [LIPO] in green, and the absence of signal peptide [NA] in red.

The CELLO server was able to predict subcellular localization of all 1923 (i.e., 1143 as “Cytoplasmic”, 293 as “Intermembrane”, 287 as “Periplasmic”, 115 as “Outer Membrane,” and 85 as “Extracellular”). On manual inspection, the predictions of 1069 EHPs were found to be identical in both methods, while only 204 predictions were not found to be identical. The DeepTMHMM server predicted the presence of 2605 transmembrane domains in only 520 EHPs out of 1923 EHPs. This server also predicted the putative topology of the protein sequences, as shown in Figure B. The number of trans-membrane domains ranged between 1 to 24 among EHPs, and the accession ID NP_253341.1 had the highest number of these domains. The topology prediction of 1923 EHPs presented 1444 as “Globular”, 436 as “Alpha Helix Trans-membrane”, 259 as “Signal Peptide”, 44 as “Alpha Helix Trans-membrane with Signal Peptide,” and 40 as “β Barrel Trans-membrane” proteins. SignalP predicted the presence of signal peptides in only 317 EHPs, out of which 217 (11.3%) had standard signal peptides and 100 (5.2%) EHPs had lipoprotein signal peptides, while the remaining 1606 (83.5%) EHPs had no signal peptides, as shown in Figure C.

Domain Identification

The putative domains were predicted for 1923 EHPs from the search of InterProScan, and the results are presented in Table S3. The redundant domains were predicted by various databases as a part of InterProScan in a few EHPs. No domains were predicted for 371 EHPs, and these were removed from the list of EHPs; the rest of the 1552 EHPs were analyzed further. The highest number of domains present in the EHPs was predicted by Pfam (1111, 17.20%), followed by Gene3D (1004, 15.54%), PANTHER (993, 15.37%), Superfamily (940, 14.55%), CDD (403, 6.24%), and others. The domains were categorized into three classes, i.e., I, II, and III, depending on the occurrence frequency (n) in 1552 EHPs, as presented in Figure S1. Class I had domains that occur in more than 80 EHPs and consisted of unknown (133), outer membrane protein, i.e., OPM (130), FAD/NAD (flavin/nicotinamide adenine dinucleotide) (P)-binding (92), EAL (Glu-Ala-Leu) sequence motif (90), uncharacterized (85), and cystathionine-beta-synthase, i.e., CBS (83). Class II had domains that occur in more than 30 EHPs but less than 80, and it consisted of acyl-CoA dehydrogenase (76), PAS (Per-Arnt-Sim) domain (60), Sel1-like repeat (54), S-adenosyl-l-methionine-dependent methyltransferase superfamily (47), acyl carrier proteins, i.e., ACP-like superfamily (46), ankyrin repeat (44), bacterial IG (immunoglobulin) (41), immunoglobulin-like fold (41), and GGDEF (Gly-Gly-Asp-Glu-Phe) (37). Lastly, class III had domains that occur equal to or less than 30 EHPs, and it consisted of AMP (adenosine monophosphate) binding (30), nucleotide cyclase (30), putative phage head morphogenesis protein (30), EamA (29), reverse transcriptase/diguanylate cyclase (28), tetratricopeptide-like helical superfamily (27), carbon–nitrogen hydrolase (27), protein kinase-like domain superfamily (26), alpha/beta hydrolase fold (26), MFS (major facilitator superfamily) transporter superfamily (26), P-loop containing nucleoside triphosphate hydrolase (25), GNATs (general control nonrepressible 5 (GCN5)-related N-acetyltransferases) (24), 5-carboxymethyl-2-hydroxymuconate isomerase (24), ABC (ATP-binding cassette) transporter (24), amino acid exporter protein, LeuE-type (23), chloramphenicol acetyltransferase-like superfamily (21), condensation (20), lambda repressor-like, DNA-binding domain superfamily (20), and MORN repeat (membrane occupation and recognition nexus) (20).

Functional Annotation

The domains of EHPs were annotated for their functions using Sma3s, KEGG, and STRING. In total, 1471 EHPs were annotated, while the remaining 81 EHPs were not annotated. These 1471 EHPs were assigned to MF, Molecular Function; BP, Biological Process; and CC, Cellular Component (CC), in accordance with their functions, as shown in Table S2. The GO terms associated with these were manually checked for redundancy. The nonredundant terms were observed to be 248, 230, and 41 for BP, MF, and CC, respectively. The Gene Ontology (GO) terms for BP, MF, and CC are shown in Figure . The functional annotation derived from all the sources was compiled to get a consensus of putative functions of the sequences. The EHPs were annotated into different biological roles, such as enzymes, transporters, associated with the secretion system, antibiotic resistance, and others. In the Biological Process category, the more frequently observed processes were lipopolysaccharide biosynthetic process (15), transport (14), oxidation–reduction process (12), proteolysis (12), methylation (11), and signal transduction (11). Similarly, cytosol (35), cell outer membrane (26), cytoplasm (20), integral component of plasma membrane (14), and membrane (10) were the most prevalent in Cellular Components, while catalytic activity (26), hydrolase activity (14), S-adenosylmethionine-dependent methyltransferase activity (11), magnesium ion binding (10), transporter activity (10), signal transducer activity (9), and flavin adenine dinucleotide binding (9) were the most prevalent in Molecular Function.

3.

3

Open in a new tab

Functional annotation in 1552 EHPs. (A) Distribution of EHPs in three GO ontology categories. (B) Cellular components are shown in purple color (each circle has particular number that represents the number of GO terms). (C) Biological processes are shown in green color. (D) Molecular functions are shown in blue color. The Y-axis shows the distribution of GO terms, while the bubble sizes represent the number of proteins in each category.

Enzymes

Enzymes are vital for various metabolic processes, growth, replication, and pathogenesis. The EHPs that were prominently identified as enzymes consisted of isomerases (21, namely, NP_249241.1, WP_023086332.1, NP_249484.1, WP_023099671.1, NP_248748.1, WP_024917324.1, NP_249550.1, WP_003088488.1, NP_250192.1, WP_015503048.1, NP_252810.1, WP_023110106.1, NP_250058.1, WP_023126116.1, NP_253404.1, WP_024917324.1, NP_252811.1, NP_252268.1, WP_003088488.1, WP_023126116.1, and WP_039026943.1), methyltransferases (11, namely, NP_253112.1, NP_249061.1, NP_253706.1, NP_249045.1, WP_240314646.1, NP_250551.1, NP_253082.1, NP_249907.1, NP_251369.1, NP_253478.1, and NP_253945.1), kinases (10, namely, NP_249274.1, NP_249971.1, NP_252145.1, NP_249743.1, WP_079758261.1, NP_249287.1, NP_248862.1, NP_248831.1, NP_251118.1 and NP_252063.1), monooxygenases (6, namely, AID86753.1, ALS13037.1, NP_252907.1, NP_249899.1, NP_249351.1 and NP_252891.1), and synthases (6, namely, NP249905.1, NP_249689.1, NP_252877.1, NP_251716.1, WP_010793930.1 and NP_253539.1).

Transporters

Functional annotation analysis identified 35 EHPs as transporters, most of which were ABC transporters (14 EHPs), including ALS13209.1, NP_252516.1, NP_252517.1, NP_252525.1, NP_254129.1, ALS13347.1, NP_248762.1, NP_248913.1, NP_251940.1, NP_252092.1, NP_252203.1, NP_253281.1, NP_253672.1, and NP_253919.1. Two EHPs, viz. AID84129.1 and ALS14232.1, were involved in the transport of multiple drugs providing resistance against these, while AID81999.1 and ALS13287.1 were involved in queuosine transport. Four EHPs, WP_071534106.1, WP_071534642.1, WP_096861907.1, and WP_170288398.1, were involved in biopolymer transport, while NP_254065.1 and NP_254075.1 were involved in glycine transport. The remaining 11 EHPs were involved in the transport of other biochemical compounds, such as sulfonate, methionine, potassium, magnesium, zinc, etc.

Secretion System

There were 33 EHPs prominently identified as type VI secretion system proteins, such as VrgG (NP_248785.1, NP_248953.1, NP_250202.1, NP_251063.1, NP_251375.1, NP_251984.1, NP_252176.1, NP_253777.1, and NP_253953.1), ImpK (ALS13352.1), ImpL (NP_250360.1, NP_251051.1, WP_010791681.1, WP_012614151.1, WP_016253424.1, WP_023122096.1, WP_025297415.1, and WP_034001666.1), and Hcp (ALS13514.1, ALS13977.1, and NP_251057.1) etc.

Antibiotic Resistance

Fifteen EHPs, namely, AID84129.1, AIL00029.1, ALS11470.1, ALS12177.1, ALS14028.1, ALS14232.1, ALS14355.1, NP_249440.1, ALS12398.1, ALS13034.1, NP_251081.1, NP_251527.1, NP_252211.1, NP_252897.1, and NP_251215.1, were observed to be involved in multidrug resistance.

Antimicrobial Resistance Analysis

One thousand five hundred thirty-one EHPs were identified as antimicrobial resistance proteins by RGI, i.e., Resistance Gene Identifier based on sequence similarity search against the Comprehensive Antibiotic Resistance Database, i.e., CARD. Among these, only five EHPs (ALS14232.1, NP_251215.1, NP_252211.1, NP_252897.1, NP_253661.1) were perfect matches, i.e., one Small Multidrug Resistance (SMR) and four Resistance Nodulation cell division (RND) antimicrobial resistance proteins. Four EHPs (AID84129.1, ALS12398.1, ALS13034.1, and NP_249440.1) were strict matches, i.e., one MATE, two RND, and one vanW antimicrobial resistance proteins, while the rest of these were loose matches, as presented in Table S2. The EHP (ALS14232.1) was identified as an SMR efflux pump. This protein has a UniParc ID, i.e., UPI00000C5EB3, and it is cross-referenced with UniProtKB entry Q9HUH5, confirming its identity as the SMR efflux protein in Pseudomonas aeruginosa. The presence of conserved residues (E14) in SMR efflux pumps has been observed. Furthermore, a similarity search of this protein against the UNIPROT database using BLASTp revealed that it belonged to a Qac (Quarternary Ammonium Compound) functional subtype. This UniProtKB entry also includes an AlphaFold-predicted structure, revealing the canonical SMR fold composed of four transmembrane helices arranged as an antiparallel homodimer. These antimicrobial resistance genes were classified based on the resistance mechanism, and the results are shown in Figure A.

4.

4

Open in a new tab

(A) Different resistance mechanisms observed in EHPs, where antibiotic inactivation in blue, efflux in orange, target alteration in green, target protection in red, reduced permeability in purple, target replacement in brown, target alteration + efflux in pink, efflux + reduced permeability in gray, target alteration + target replacement in yellow, and resistance by host-dependent nutrient acquisition in teal green colors. (B) Different AMR gene families identified in EHPs, where efflux pump in dark blue, beta-lactam in dark orange, others in green, aminoglycoside in dark red, RNA-associated in purple, vancomycin in brown, streptogramin in magenta, tetracycline in coral, ATP-binding cassette in yellow, rifampin in light blue, MCR transferase in dark blue, fusidic acid in light orange, bacterial porin in green, macrolide in dark red, and chloramphenicol in purple color. (C) Distribution of virulence categories, where effector delivery in blue, nutritional/metabolic factor in orange, immune modulation in green, adherence in red, motility in purple, exotoxin in brown, invasion in pink, biofilm in gray, stress survival in yellow, others in teal green, and post-translational modification in light blue colors.

The major resistance mechanism observed was antibiotic inactivation for 680 EHPs in number and 44.97% of the total. Thus, nearly half of the EHPs were associated with the antibiotic inactivation mechanism that majorly involved beta-lactamases along with aminoglycoside, tetracycline, and rifampin-modifying enzymes. These β-lactamases are divided into four groups, where only one group is of metallo-β-lactamase (MBL), while others are of serine β-lactamases (SBL). The different classes of beta-lactamases observed in 340 EHPs were OXA (84), SMB (Serratia metallo-β-lactamase) (24), IMP (Imipenemase) (18), PME (Pseudomonas aeruginosa ESBL) (16), CTX-M (Cefotaximase-Munich) (6), PDC (Pseudomonas-derived cephalosporinase) (5), CARB (Carbenicillin-hydrolyzing beta-lactamase) (4), SHV (Sulfhydryl Variable) (3), NDM (New Delhi metallo-β-lactamase) (1), PER (Pseudomonas Extended Resistant) (1), TEM (Temoniera) (1), and others. Similarly, the different classes of aminoglycoside-modifying enzymes, such as aminoglycoside acetyltransferase (AAC) (59), aminoglycoside nucleotide transferase (ANT) (31), and aminoglycoside phosphotransferase (APH) (25), were observed in 199 EHPs. Antibiotic efflux (447 in number, and 29.20%) was the second-highest resistance mechanism observed. The different types of efflux pump families, such as resistance-nodulation-cell division, i.e., RND (244), major facilitator superfamily, i.e., MFS (128), ATP-binding cassette i.e. ABC (54), multidrug and toxic compound extrusion, i.e., MATE (20), and small multidrug resistance, i.e., SMR (6), were observed among EHPs. In addition, there were some EHPs that showed a combined nature of efflux pump proteins and such as MFS and RND (5), RND and OPR (4), RND and Bacterial porin (2), and ABC and MFS and RND (2). Furthermore, EHPs associated with antibiotic target alteration (226, 14.77%), antibiotic target protection (88, 5.75%), reduced permeability to antibiotic (36, 2.35%), and antibiotic target replacement (19, 1.24%) mechanisms were also observed. Similarly, the AMR protein families were also observed and included efflux pump (499, 32.59%), beta-lactamase (399, 20.06%), others (163, 10.65%), aminoglycoside (110, 7.18%), RNA-associated (91, 5.94%), vancomycin (58, 3.79%), streptogramin (50, 3.27%), tetracycline (47, 3.07%), rifampin (29, 1.89%), MCR transferase (25, 1.63%), fusidic acid (22, 1.44%), bacterial porin (16, 1.05%), macrolide (13, 0.85%), and chloramphenicol (9, 0.59%), as presented in Figure B.

Virulence Analysis

The results of similarity search of 1923 EHPs against VFDB yielded 149 EHPs as virulent proteins, while the rest of 1774 EHPs were nonvirulent, as presented in Table S4. In total, 964 matches from the VFDB were observed against 149 EHPs with more than 40% identity criteria. Multiple categories of virulent proteins, such as effector delivery (336), Nutritional/Metabolic factors (282), immune modulation (122), adherence (57), motility (54), exotoxins (43), invasions (30), biofilm (12), stress survival (11), others (9), post-translational modification (6), regulation (1), exoenzyme (1), were observed in 149 EHPs, as shown in Figure C.

Nonhomology against the Human Proteome

The results of the sequence similarity search against the human proteome identified 232 EHPs as similar to those in humans among 1471 EHPs. These 232 homologous EHPs were removed, and the remaining 1239 human nonhomologous EHPs were considered.

Drug Target Identification Analysis

The sequence similarity search for 1239 EHPs against TTD, ChEMBL, and DrugBank databases resulted in 3,018 targets for 491 EHPs using BLASTp. The remote homology sequence search employing PSI-BLAST (at an identity criterion of 50%) resulted in three potential new drug targets out of a total of 27 hits. The remaining 24 targets were found to be a part of a previously compiled list based on the similarity search carried out using BLASTp. However, manual verification revealed that these three drug targets/EHPs were found to be related to the CHEMBL4105713 entry. However, these EHPs were not considered further as this ChEMBL entry is 8-oxo-dGTP diphosphatase belonging to Homo sapiens. Among 491 EHPs obtained from similarity search, 63 EHPs had more than 40% sequence identity. These 63 EHPs had 61 nonredundant matches consisting of 17 ChEMBL, 12 TTD, and 32 DrugBank target sequences. On manual verification, out of these 63 target sequences, four (CHEMBL3430900, CHEMBL4105797, CHEMBL3391681, and T13741) were related to Homo sapiens and were therefore removed from further consideration. This also led to the removal of 2 EHPs viz. NP_253678.1 and NP_253724.1 from the drug target list as these were related to the four target hits of ChEMBL and TTD. Hence, 61 EHPs were considered as potential drug targets in this study. Among these 61 drug targets, six drug targets with sequence identity greater than 90% are presented in Table , while the remaining 55 drug targets are listed in Table S5, along with their corresponding UniProtKB/UniParc and PDB IDs. Sixty-one EHPs were found to have matches against these three databases (i.e., ChEMBL, TTD, DrugBank), and these included ALS15283.1 (with 7 matches), AIL00029.1 (with 6 matches), AYN82176.1 (with 3 matches), AIL00036.1, ALS10073.1, ALS12554.1, NP_249407.1, NP_249730.1, NP_250179.1, and WP_254077144.1 (with 2 matches each). There were 51 EHPs that had a single match for each of these. The sixty-one drug targets in this study were compared with 65 known already published drug targets via sequence similarity search. Five targets, i.e., NP_248883.1, NP_253279.1, ALS13667.1, NP_251512.1, and ALS14639.1, showed 100% similarity with already reported targets (NP_248883.1, NP_253279.1, NP_249114.1, NP_251512.1, and NP_253279.1). Thus, the remaining 56 drug targets were newly reported only in this study in 16 different strains (Cu1510, PA-3, AR-0110, DK2, PA59, 1334/4/1334_4, P8W, LW, DVT413, PAC-1, N17-1, NCTC13620, USDA-ARS-MARC-41639, 12-4-4_59/12-4-4(59), VRPFA04, and PaO1). The remaining 748 EHPs had no similarity with any entity of the three databases mentioned and thus were considered as novel drug targets.

1. Comprehensive Details of EHPs (Having >90% Identity with Entries in ChEMBL, DrugBank, and TTD) as Drug Targets.

NCBI ID strain name protein stability subcellular localization domain annotation functional annotation AMR gene family name virulent proteins UniparC/UniprotKB IDs PDB ID
AIL00029.1 VRFPA04 unstable periplasmic beta-lactamase/transpeptidase-like PBP-1a/beta-lactamase/transpeptidase-like OXA beta-lactamase UPI00030BC2A9/Q07806 4OON
ALS14639.1 Cu1510 stable outer membrane outer membrane protein transport protein (OMPP1/FadL/TodX) outer membrane protein transport protein (OMPP1/FadL/TodX)/long-chain fatty acid transport protein ATP-binding cassette (ABC) antibiotic efflux pump UPI0003C37684
NP_249998.1 PAO1 stable cytoplasmic nucleophile aminohydrolases, N-terminal putative glutamine amidotransferase YafJ/gamma-glutamyl-hercynylcysteine sulfoxide hydrolase EgtC-like UPI00000C5302/Q9I437 1TE5
NP_250505.1 PAO1 unstable cytoplasmic S-adenosyl-l-methionine-dependent methyltransferase superfamily methyltransferase type 11 CfxA beta-lactamase UPI00000C54AD
NP_251960.1 PAO1 stable cytoplasmic Acyl-CoA N-acyltransferase Acyl-CoA N-acyltransferase AAC(6’) UPI00000C5985/Q9HYX1 8GXJ
NP_253279.1 PAO1 stable outer membrane outer membrane protein transport tar(OMPP1/FadL/TodX) long-chain fatty acid transport protein ATP-binding cassette (ABC) antibiotic efflux pump UPI00000C5D79
Open in a new tab
a

Absence of value/NA.

b

Functional annotation has been carried out using SMA3S/STRING/kofam/KEGG.

Structural Identification of EHPs

The sequence similarity search of 1923 EHPs against the 230 PDB sequences resulted in nine EHPs out of the 15 unique PDB IDs having 90% sequence identity and more than 70% query coverage. The second sequence-based search of 1923 EHPs was performed against protein sequences in the PDB database, and sixty-two (62) entries in PDB at 100% sequence identity and more than 70% sequence coverage were retrieved. After removing three overlapping PDB IDs found in both approaches, a final set of 68 nonredundant PDB IDs was retained and are listed in Table S2 against their corresponding EHPs. Only seven structures for EHPs that are considered to be potential drug targets were found in the PDB. This can be valuable information for further functional and drug target characterization.

PPI (Protein–Protein Interaction) Analysis of Drug Targets

The interaction partners were identified using the STRING database for 61 potential drug targets, as shown in Figure . The network was analyzed with the highest confidence score (0.900) to avoid both false positives and false negatives. Out of the 61 drug targets, 5 drug target EHPs showed the interaction with two to five proteins, one EHP showed the interaction with a single protein only, and 54 EHPs did not show any interaction with other proteins in the STRING database. The drug target NP_249743.1 showed a high interaction score with GntP Permease, a probable transporter (PA1051) (score 0.989), and probable Oxidoreductase (PA1500) (score 0.914). However, AIL00298.1, Precorrin isomerase CobH (a hub protein in 61 drug targets) domain, interacted with the PA2909 hypothetical protein (score 0.976) and 7 others. NP_249407.1 interacted with the PA0715 hypothetical protein (score 0.968). NP_249798.1 interacted with the rocR protein (score 0.967). NP_250542.1 interacted with the PA3936 hypothetical protein (score 0.922). NP_251000.1 interacted with the probable permease of the ABC taurine transporter (score 0.978) and tauB, i.e., the probable ATP-binding component of ABC taurine transporter (score 0.973). NP_251560.1 interacted with the PA3825 hypothetical protein (score 0.961) and the rocR protein (score 0.952).

5.

5

Open in a new tab

61 EHPs as drug targets are used to construct the PPI network at the highest confidence score (0.900) using the STRING database. The number of edges in the network is 59, while the number of nodes is 64. The PPI enrichment p-value is 3.01 × 10–11. The subclusters were visualized in plugin mode MCODE in Cytoscape. The name of drug targets is shown in a red square, along with their accession number.

Discussion

Pseudomonas aeruginosa, one of the three most prevalent nosocomial infections causing an omnipresent pathogenic bacterium, has developed antibiotic resistance due to self-medication and overuse of antibiotics, posing a great clinical threat. − Numerous scientists have studied the genome and proteome of the organism to find novel drug targets among the vital biomolecules such as nucleic acids and proteins. − The affordability of rapid sequencing has resulted in a notable growth in the inflow of hypothetical sequences in well-established resources like GenBank, RefSeq, etc. These hypothetical sequences were assigned to various classes, such as outer membrane proteins, enzymes, virulence, structural and secretory proteins, etc. To date, studies have been reported on hypothetical proteins in the proteome of a few strains of P. aeruginosa. The current study focused on novel drug target identification and characterization in multiple strains (i.e., more than 300 strain’s proteome) of P. aeruginosa with the help of an inclusive subtractive proteomics approach. The present study identified 2,32,641 chromosomal HPs from 351 strains of P. aeruginosa, and out of these, only 1923 EHPs were revealed, the largest number of EHPs so far. However, various studies have reported different numbers of EHPs. For example, one study found 56 EHPs, whereas Rahman et al. found 18 EHPs. The EHPs were subjected to the in silico approaches for their characterization with the aim of identifying and exploiting their role in survival of the pathogen and development as potential drug molecules.

In this study, sixty-one drug targets were identified among EHPs during druggability analysis. Five drug targets among these were found to be similar to existing known drug targets based on a sequence similarity search. Seven hundred forty-eight EHPs that did not match with any of the drug targets in three databases, i.e., TTD, ChEMBL, and DrugBank, may be considered as potential novel (drug) targets. However, the physicochemical properties of 61 EHPs were deciphered for their chemical nature and molecular attributes. The isoelectric point that helps us to understand the putative electrophoretic gel profile of the EHPs ,, ranged from 4.52 to 11.92 for these drug targets. The instability index of these drug targets ranged from 9.93 to 67.48, and 27 drug targets with the instability index (II) value less than 40 may be considered as stable, as the relationship of this value with instability is proposed by Guruprasad et al. The GRAVY index of drug targets varied from −0.935 to 1.443. Out of 61 drug targets, 29 showed a negative GRAVY index, marking these as nonpolar on similar lines, as shown by Islam et al. in their study on the structure and function of hypothetical proteins of Vibrio cholerae. The majority of domains or families identified in 61 drug targets were classified as belonging to transporters and enzymes, i.e., the phospholipid-containing peptides OPM (Outer membrane Protein), queuosine precursor transporter, alpha/beta hydrolase fold, GGDEF domain, protein kinase-like domain superfamily, OmpA-like domain superfamily, and P-loop containing nucleoside. However, the other important domains such as EAL, CBS, acetyl-CoA dehydrogenase, PAS, Sel1-like repeat, ankyrin repeat, bacterial IG, GNAT, and MORN repeats were also found in the remaining EHPs. The presence of these domains in EHPs suggested that these might have similar functions, as these domains were already reported to perform specific functions in other proteins. The thorough analysis of domain annotation predicted the outer membrane protein (OMP) in most of the EHPs. These EHPs can be used as antimicrobial and vaccine targets, as reported in proteome studies on species K. pneumoniae by Brinkworth et al. and on P. aeruginosa by Motta et al. Twenty-six cytoplasmic, 22 inner membrane, 7 outer membrane, 5 periplasmic, and one extracellular EHPs were observed in the sixty-one drug targets based on subcellular localization. These cytoplasmic EHPs can be considered as potential drug targets for the development of drugs, as shown in the study by Anis Ahamed et al., for drug target identification in Bacillus cereus through a subtractive proteomics approach. However, three hundred eighty-five EHPs were observed to be associated with membranes, i.e., transmembrane regions, and thus were part of plasma membranes. These can be possibly utilized as antimicrobial and vaccine drug targets, as proposed in another study by Baliga et al., where outer membrane proteins were predicted as vaccine candidates in Vibrio anguillarum. The functional annotation of 61 drug targets revealed that the majority of EHPs were either enzymes (23 drug targets) or outer membrane transporters (10 drug targets), others (13 drug targets), and uncharacterized (15 drug targets). However, the functional annotation of 1923 EHPs included 681 enzymes, while the remaining 1242 EHPs included transporters, secretory proteins, antimicrobial resistance proteins, cell division proteins, and others. Similar results of the functional annotations of hypothetical proteins into various categories (such as enzymes, transporters, binding proteins, cellular/regulatory proteins, etc.) were reported in a previous study of Reem et al. on P. aeruginosa strain PaO1. Most of the transporter and enzyme EHPs were assigned the same function in both domain and functional categories. Based on functional annotation, with respect to biological processes, the EHPs were involved in chemical reactions and pathways that resulted in the formation of lipopolysaccharides. The domain and Gene Ontology (GO) analysis revealed that most of the EHPs belonged to transporter, enzyme, and antimicrobial resistance class families. Ten serine/threonine kinases, seven general bacterial porins exhibiting decreased permeability to peptide antibiotics, four aminoglycosides (two AAC(6’), AAC(2’), APH(4’)), four major facilitator superfamily (MFS) transporters, three Erm 23s rRNA methyltransferases, seven beta-lactamases (three OXA, SHV, CfxA, LRG, PRC), three glycopeptide resistance gene clusters (van ligase, vanXY, vanY), three ATP-Binding cassette (ABC) efflux pumps, two Vga-type ABC-F proteins, two antibiotic-resistant isoleucyl-tRNA synthetases (ileS), Ole glycosyltransferase, methicillin-resistant PBP2, SMR, tetracycline inactivation enzyme, macrolide phosphotransferase (MPH), quinolone resistant protein (Qnr), MCR phosphoethanolamine transferase, and phosphoethanolamine transferase (Pmr) antimicrobial resistance gene families were identified in 61 drug targets. There were four drug targets that showed the absence of AMR gene families. The antimicrobial resistance (AMR) proteins were identified in 1531 out of 1923 EHPs. The major resistance mechanisms, such as antibiotic inactivation, efflux, target alteration, target protection, and reduced permeability to antibiotics, along with others, were observed among these EHPs. The most prevalent AMR protein families observed were efflux pumps, β-lactams, aminoglycosides, RNA-associated, and various antibiotic-specific proteins. The only one perfect hit of AMR protein was found in identified drug targets or EHP (ALS14232.1), i.e., SMR protein, while the remaining were loose hits. Through functional annotation, this EHP was characterized as an SMR efflux pump. Furthermore, the sequence similarity search against the UNIPROT database revealed that its functional subtype is Qac (Quaternary ammonium compounds). The SMR efflux pumps are known to expel various antibiotics, such as quaternary ammonium compounds and polyamine compounds like spermidine, leading to antimicrobial resistance, as reported in the study of Chung and Saier. The structural annotation of seven EHPs, such as AIL00029.1, NP_249998.1, NP_251960.1, ALS13667.1, NP_249810.1, NP_250542.1, and NP_253150.1, is found in PDB and is correlated with their functional prediction.

Out of 61 EHPs, 26 EHPs were virulent, while the remaining were nonvirulent. Only one hundred forty-nine of 1923 EHPs were virulent proteins, and these were classified into various categories such as nutritional/metabolic factor, exotoxins, invasion, exoenzymes, antimicrobial activity, adherence, regulation, motility, biofilm production, effector delivery system, immune modulation, post-translational modification, stress survival, and others. A majority of EHPs were involved in the effector delivery secretion system category that includes type VI secretion system virulent proteins. These proteins could possibly be involved in cystic fibrosis infections caused by P. aeruginosa, indicating their potential as targets for antivirulent drugs, as reported in the study of Qin et al. Similarly, EHPs were identified as virulent proteins related to motility along with other categories. These act as flagella or pili, considered to play important roles in degradation and modulation of host defense mechanisms, as shown by various studies, including those by Huber et al., Rocha et al., and Solanki et al. − CobH was identified as the hub protein in the protein–protein interaction analysis of 61 drug targets. The drug target AL00298.1 (CobH, a hub protein) interacted with PA2909, a hypothetical protein, and others. It was already reported that cobH was involved in the pathway of the cobalamin biosynthesis enzyme. Thus, if CobH (AL00298.1) is used as a drug target, and its knockout may disrupt the aerobic cobalamin pathway, i.e., B12 production. The B12 production is required for biofilm growth in P. aeruginosa, as reported in the study of Crespo et al. Thus, the knockout or inhibition of the cobH gene may lead to inhibiting the B12 pathway, and ultimately, it will help to eradicate the P. aeruginosa infections. Furthermore, the nonhomology analysis found that the cobH gene was nonhomologous to human proteins, thus underpinning it as a potential drug target. The present study identified thirty-three EHPs as strain-specific drug targets in 15 different strains of P. aeruginosa for the first time. Therefore, the development of these as drug targets, followed by experimental validation, can inhibit infections caused by these 15 different strains. In addition, ten drug targets of P. aeruginosa strains were found to match with known Mycobacterium tuberculosis drug targets listed in databases such as TTD, ChEMBL, and DrugBank. Therefore, antimicrobials against these drug targets may be developed and used against both the organisms (i.e., P. aeruginosa and M. tuberculosis). Thus, sixty-one potential drug targets in P. aeruginosa were characterized as potential drug/vaccine targets, with some having antimicrobial resistance properties. This in silico study is a first step toward the functional characterization of hypothetical proteins before experimental confirmation. Hence, the structural study and experimental validation may be carried out for these drug targets that can possibly be employed as potential therapeutic targets. Therefore, this study helped to add a considerable amount of knowledge to our understanding of EHPs in P. aeruginosa and in the development of novel drug targets, with the potential to aid in combating the growing challenge of antimicrobial resistance in P. aeruginosa infections.

Conclusions

This study employing a subtractive proteomics approach identified the highest number of EHPs that were functionally annotated as enzymes, transporters, secretion proteins, etc., thus implicating these in the vital roles in the overall metabolism and pathogenicity. Based on domain and functional analysis of these EHPs, one of these was identified as an SMR efflux pump. The protein–protein interaction analysis revealed one hub protein, i.e., CobH, implicated in cobalamin biosynthesis pathway (B12 pathway) required for biofilm formation. Over 60 EHPs identified as drug targets showing no cross reactivity with human proteins are suggested to be potential candidates for further validation. This study facilitated the identification of EHPs in a large number of strains of P. aeruginosa in addition to drug targets.

Supplementary Material

ao5c02268_si_001.pdf (4MB, pdf)

Acknowledgments

NK is thankful to the University Grants Commission (UGC) for the fellowship, and DN acknowledges CSIR-IMTECH for providing computational resources and guidance and RIMT University.

The data used in this study is available from the corresponding author upon reasonable request.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c02268.

  • Domain types identified in 1923 EHPs; distribution of EHPs among 351 strains; structural and functional annotation of EHPs; detailed information on domain identification; virulence categories in 1923 EHPs; and details of identified drug targets in P. aeruginosa (PDF)

N.K. has conceptualized and performed initial data curation and analysis. D.N. performed the analysis of results and has written initial draft of the manuscript along with N.K. However, writing, reviewing, and editing were carried out after inputs from B.S. and under his supervision. All authors have read and approved the final version of manuscript for publication.

The authors declare no competing financial interest.

References

  1. Lund-Palau H., Turnbull A. R., Davies J. C.. et al. Pseudomonas Aeruginosa Infection in Cystic Fibrosis: Pathophysiological Mechanisms and Therapeutic Approaches. Expert Rev. Respir. Med. 2016;10(6):685–697. doi: 10.1080/17476348.2016.1177460. [DOI] [PubMed] [Google Scholar]
  2. Liu Q., Li X., Li W., Du X., He J. Q., Tao C., Feng Y.. Influence of Carbapenem Resistance on Mortality of Patients with Pseudomonas Aeruginosa Infection: A Meta-Analysis. Sci. Rep. 2015;5:11715. doi: 10.1038/srep11715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Watanabe R., Matsumoto T., Sano G., Ishii Y., Tateda K., Sumiyama Y., Uchiyama J., Sakurai S., Matsuzaki S., Imai S., Yamaguchi K.. Efficacy of Bacteriophage Therapy against Gut-Derived Sepsis Caused by Pseudomonas Aeruginosa in Mice. Antimicrob. Agents Chemother. 2007;51(2):446. doi: 10.1128/AAC.00635-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Zaborina O., Kohler J. E., Wang Y., Bethel C., Shevchenko O., Wu L., Turner J. R., Alverdy J. C.. Identification of Multi-Drug Resistant Pseudomonas Aeruginosa Clinical Isolates That Are Highly Disruptive to the Intestinal Epithelial Barrier. Ann. Clin. Microbiol. Antimicrob. 2006;5:14. doi: 10.1186/1476-0711-5-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Abril D., Marquez-Ortiz R. A., Castro-Cardozo B., Moncayo-Ortiz J. I., Olarte Escobar N. M., Corredor Rozo Z. L., Reyes N., Tovar C., Sánchez H. F., Castellanos J., Guaca-González Y. M., Llanos-Uribe C. E., Vanegas Gómez N., Escobar-Pérez J.. Genome Plasticity Favours Double Chromosomal Tn4401b-BlaKPC-2 Transposon Insertion in the Pseudomonas Aeruginosa ST235 Clone. BMC Microbiol. 2019;19(1):45. doi: 10.1186/s12866-019-1418-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Coulter S. N., Folger K. R., Kas A., Larbig K., Lim R., Smith K., Spencer D., Wong G. K., Wu Z., Paulsen I. T., Reizer J., Saier M. H., Hancock R. E., Lory S., Olson M. V., Kowalik D. J., Lagrou M., Garber R. L., Goltry L., Tolentino E., Westbrock-Wadman S., Yuan Y., Brody L., Stover C. K., Pham X. Q., Erwin A. L., Mizoguchi S. D., Warrener P., Hickey M. J., Brinkman F. S., Hufnagle W. O.. Complete Genome Sequence of Pseudomonas Aeruginosa PAO1, anopportunistic Pathogen. Nature. 2000;406(6799):959–964. doi: 10.1038/35023079. [DOI] [PubMed] [Google Scholar]
  7. Feng W., Sun F., Wang Q., Xiong W., Qiu X., Dai X., Xia P.. Epidemiology and Resistance Characteristics of Pseudomonas Aeruginosa Isolates from the Respiratory Department of a Hospital in China. J. Glob. Antimicrob. Resist. 2017;8:142. doi: 10.1016/j.jgar.2016.11.012. [DOI] [PubMed] [Google Scholar]
  8. Coulter S. N., Folger K. R., Kas A., Larbig K., Lim R., Smith K., Spencer D., Wong G. K., Wu Z., Paulsen I. T., Reizer J., Saier M. H., Hancock R. E., Lory S., Olson M. V., Feng W., Sun F., Wang Q., Xiong W., Qiu X., Dai X., Xia P., Watanabe R., Matsumoto T., Sano G., Ishii Y., Tateda K., Sumiyama Y., Uchiyama J., Sakurai S., Matsuzaki S., Imai S., Yamaguchi K., Zaborina O., Kohler J. E., Wang Y., Bethel C., Shevchenko O., Wu L., Turner J. R., Alverdy J. C., Roulová N., Mot’ková P., Brožková I., Pejchalová M., Reddy A., Imanah E., Beshiru A., Igbinosa E., Abril D., Marquez-Ortiz R. A., Castro-Cardozo B., Moncayo-Ortiz J. I., Olarte Escobar N. M., Corredor Rozo Z. L., Reyes N., Tovar C., Sánchez H. F., Castellanos J., Guaca-González Y. M., Llanos-Uribe C. E., Vanegas Gómez N., Escobar-Pérez J., Kowalik D. J., Lagrou M., Garber R. L., Goltry L., Tolentino E., Westbrock-Wadman S., Yuan Y., Brody L., Stover C. K., Pham X. Q., Erwin A. L., Mizoguchi S. D., Warrener P., Hickey M. J., Brinkman F. S., Hufnagle W. O.. Antibiogram Profie of Pseudomonas Aeruginosa Isolated from Some Selected Hospital Environmental Drains. BMC Microbiol. 2017;8(1):45. doi: 10.1186/s12866-019-1418-6. [DOI] [Google Scholar]
  9. Azam M. W., Khan A. U.. Updates on the Pathogenicity Status of Pseudomonas Aeruginosa. Drug Discovery Today. 2019;24:350. doi: 10.1016/j.drudis.2018.07.003. [DOI] [PubMed] [Google Scholar]
  10. Golle A., Janezic S., Rupnik M.. Low Overlap between Carbapenem Resistant Pseudomonas Aeruginosa Genotypes Isolated from Hospitalized Patients and Wastewater Treatment Plants. PLoS One. 2017;12(10):e0186736. doi: 10.1371/journal.pone.0186736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gómez-Martínez J., Rocha-Gracia R. D. C., Bello-López E., Cevallos M. A., Castañeda-Lucio M., Sáenz Y., Jiménez-Flores G., Cortés-Cortés G., López-García A., Lozano-Zarain P.. Comparative Genomics of Pseudomonas Aeruginosa Strains Isolated from Different Ecological Niches. Antibiot. (Basel, Switzerland) 2023;12(5):866. doi: 10.3390/antibiotics12050866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Roulová N., Mot’ková P., Brožková I., Pejchalová M.. Antibiotic Resistance of Pseudomonas Aeruginosa Isolated from Hospital Wastewater in the Czech Republic. J. Water Health. 2022;20(4):692. doi: 10.2166/wh.2022.101. [DOI] [PubMed] [Google Scholar]
  13. Guruprasad K., Reddy B. V. B., Pandit M. W.. Correlation between Stability of a Protein and Its Dipeptide Composition: A Novel Approach for Predicting in Vivo Stability of a Protein from Its Primary Sequence. Protein Eng. Des. Sel. 1990;4(2):155. doi: 10.1093/protein/4.2.155. [DOI] [PubMed] [Google Scholar]
  14. Shahbaaz M., Bisetty K., Ahmad F., Hassan M. I.. In Silico Approaches for the Identification of Virulence amongst Hypothetical Proteins of Mycoplasma Pneumoniae. Comput. Biol. Chem. 2015;59 Pt A:67–80. doi: 10.1016/j.compbiolchem.2015.09.007. [DOI] [PubMed] [Google Scholar]
  15. Pires D. P., Monteiro R., Mil-Homens D., Fialho A., Lu T. K., Azeredo J.. Designing P. Aeruginosa Synthetic Phages with Reduced Genomes. Sci. Rep. 2021;11(1):2164. doi: 10.1038/s41598-021-81580-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dey S., Shahrear S., Zinnia M. A., Tajwar A., Islam A. B. M. M. K.. Functional Annotation of Hypothetical Proteins From the Enterobacter Cloacae B13 Strain and Its Association With Pathogenicity. Bioinform. Biol. Insights. 2022;16:11779322221115535. doi: 10.1177/11779322221115535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ijaq J., Chandra D., Ray M. K., Jagannadham M. V.. Investigating the Functional Role of Hypothetical Proteins From an Antarctic Bacterium Pseudomonas Sp. Lz4W: Emphasis on Identifying Proteins Involved in Cold Adaptation. Front. Genet. 2022;13:825269. doi: 10.3389/fgene.2022.825269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Wright B. W., Kamath K. S., Krisp C., Molloy M. P.. Proteome Profiling of Pseudomonas Aeruginosa PAO1 Identifies Novel Responders to Copper Stress. BMC Microbiol. 2019;19(1):69. doi: 10.1186/s12866-019-1441-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Reem A., Zhong Z.-H., AL-Shehari W., Al-Shaebi F., Amran G., Moeed Y., Sami R., Khojah E., Askary A.. Functional Annotation of Hypothetical Proteins Related to Antibiotic Resistance in Pseudomonas Aeruginosa PA01. Clin. Lab. 2021;67:1–7. doi: 10.7754/Clin.Lab.2021.210536. [DOI] [PubMed] [Google Scholar]
  20. Uddin R., Jamil F.. Prioritization of Potential Drug Targets against P. Aeruginosa bycore Proteomic Analysis Using Computational Subtractive genomicsand Protein-Protein Interaction Network. Comput. Biol. Chem. 2018;74:115–122. doi: 10.1016/j.compbiolchem.2018.02.017. [DOI] [PubMed] [Google Scholar]
  21. Sayers E. W., Cavanaugh M., Clark K., Pruitt K. D., Schoch C. L., Sherry S. T., Karsch-Mizrachi I.. GenBank. Nucleic Acids Res. 2022;50(D1):D161–D164. doi: 10.1093/nar/gkab1135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li W., Godzik A.. Cd-Hit: A Fast Program for Clustering and Comparing Large Sets of Protein or Nucleotide Sequences. Bioinformatics. 2006;22(13):1658–1659. doi: 10.1093/bioinformatics/btl158. [DOI] [PubMed] [Google Scholar]
  23. Prabhu D., Rajamanikandan S., Anusha S. B., Chowdary M. S., Veerapandiyan M., Jeyakanthan J.. In Silico Functional Annotation and Characterization ofHypothetical Proteins from Serratia Marcescens FGI94. Biol. Bull. Russ. Acad. Sci. 2020;47(4):319–331. doi: 10.1134/S1062359020300019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Luo H., Lin Y., Gao F., Zhang C. T., Zhang R.. DEG 10, an Update of the Database of Essential Genes That Includes Both Protein-Coding Genes and Noncoding Genomic Elements. Nucleic Acids Res. 2014;42(D1):D574–D580. doi: 10.1093/nar/gkt1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., Madden T. L.. BLAST+: Architecture and Applications. BMC Bioinformatics. 2009;10(1):421. doi: 10.1186/1471-2105-10-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gasteiger, E. ; Hoogland, C. ; Gattiker, A. ; Duvaud, S. ; Wilkins, M. R. ; Appel, R. D. ; Bairoch, A. . Protein Identification and Analysis Tools on the ExPASy Server. In The Proteomics Protocols Handbook; Walker, J. M. , Ed.; Humana Press: Totowa, NJ, 2005; pp 571–607. [Google Scholar]
  27. Yu N. Y., Wagner J. R., Laird M. R., Melli G., Rey S., Lo R., Dao P., Sahinalp S. C., Ester M., Foster L. J., Brinkman F. S. L.. PSORTb 3.0: Improved Protein Subcellular Localization with Refined Localization Subcategories and Predictive for All Prokaryotes. Bioinformatics. 2010;26(13):1608–1615. doi: 10.1093/bioinformatics/btq249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Yu C.-S., Cheng C.-W., Su W.-C., Chang K.-C., Huang S.-W., Hwang J.-K., Lu C.-H.. CELLO2GO: A Web Server for Protein SubCELlular LOcalization with Functional Gene Ontology Annotation. PLoS One. 2014;9(6):e99368. doi: 10.1371/journal.pone.0099368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hallgren, J. ; Tsirigos, K. D. ; Damgaard Pedersen, M. ; Juan, J. ; Armenteros, A. ; Marcatili, P. ; Nielsen, H. ; Krogh, A. ; Winther, O. . DeepTMHMM Predicts Alpha and Beta Transmembrane Proteins Using Deep Neural Networks. bioRxiv, 2022. [Google Scholar]
  30. Teufel F., Armenteros J. J. A., Johansen A. R., Gíslason M. H., Pihl S. I., Tsirigos K. D., Winther O., Brunak S., von Heijne G., Nielsen H.. SignalP 6.0 Predicts All Five Types of Signal Peptides Using Protein Language Models. Nat. Biotechnol. 2022;40(7):1023–1025. doi: 10.1038/s41587-021-01156-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Zdobnov E. M., Apweiler R.. InterProScan – an Integration Platform for the Signature-Recognition Methods in InterPro. Bioinformatics. 2001;17(9):847–848. doi: 10.1093/bioinformatics/17.9.847. [DOI] [PubMed] [Google Scholar]
  32. Marchler-Bauer A., Derbyshire M. K., Gonzales N. R., Lu S., Chitsaz F., Geer L. Y., Geer R. C., He J., Gwadz M., Hurwitz D. I., Lanczycki C. J., Lu F., Marchler G. H., Song J. S., Thanki N., Wang Z., Yamashita R. A., Zhang D., Zheng C., Bryant S. H.. CDD: NCBI’s Conserved Domain Database. Nucleic Acids Res. 2015;43(D1):D222–D226. doi: 10.1093/nar/gku1221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Schultz J., Copley R. R., Doerks T., Ponting C. P., Bork P.. SMART: A Web-Based Tool for the Study of Genetically Mobile. Nucleic Acids Res. 2000;28(1):231–234. doi: 10.1093/nar/28.1.231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sigrist C. J. A., Cerutti L., de Castro E., Langendijk-Genevaux P. S., Bulliard V., Bairoch A., Hulo N.. PROSITE, a Protein Domain Database for Functional Characterization and Annotation. Nucleic Acids Res. 2010;38(Database issue):D161–D166. doi: 10.1093/nar/gkp885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mistry J., Chuguransky S., Williams L., Qureshi M., Salazar G. A., Sonnhammer E. L. L., Tosatto S. C. E., Paladin L., Raj S., Richardson L. J., Finn R. D., Bateman A.. Pfam: The Protein Families Database in 2021. Nucleic Acids Res. 2021;49(D1):D412–D419. doi: 10.1093/nar/gkaa913. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Akiva E., Brown S., Almonacid D. E., 2nd A. E. B., Custer A. F., Hicks M. A., Huang C. C., Lauck F., Mashiyama S. T., Meng E. C., Mischel D., Morris J. H., Ojha S., Schnoes A. M., Stryke D., Yunes J. M., Ferrin T. E., Holliday G. L., Babbitt P. C.. The Structure-Function Linkage Database. Nucleic Acids Res. 2014;42(Database issue):D521–D530. doi: 10.1093/nar/gkt1130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Attwood T. K., Croning M. D., Flower D. R., Lewis A. P., Mabey J. E., Scordis P., Selley J. N., Wright W.. PRINTS-S: The Database Formerly Known as PRINTS. Nucleic Acids Res. 2000;28(1):225–227. doi: 10.1093/nar/28.1.225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Haft D. H., Selengut J. D., White O.. The TIGRFAMs Database of Protein Families. Nucleic Acids Res. 2003;31(1):371–373. doi: 10.1093/nar/gkg128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lima T., Auchincloss A. H., Coudert E., Keller G., Michoud K., Rivoire C., Bulliard V., de Castro E., Lachaize C., Baratin D., Phan I., Bougueleret L., Bairoch A.. HAMAP: A Database of Completely Sequenced Microbial Proteome Sets and Manually Curated Microbial Protein Families in UniProtKB/Swiss-Prot. Nucleic Acids Res. 2009;37(Database issue):D471–D478. doi: 10.1093/nar/gkn661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Pandurangan A. P., Stahlhacke J., Oates M. E., Smithers B., Gough J.. The SUPERFAMILY 2.0 Database: A Significant Proteome updateand a New Webserver. Nucleic Acids Res. 2019;47(D1):D490–D494. doi: 10.1093/nar/gky1130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Wu C. H., Nikolskaya A., Huang H., Yeh L.-S. L., Natale D. A., Vinayaka C. R., Hu Z.-Z., Mazumder R., Kumar S., Kourtesis P., Ledley R. S., Suzek B. E., Arminski L., Chen Y., Zhang J., Cardenas J. L., Chung S., Castro-Alvear J., Dinkov G., Barker W. C.. PIRSF: Family Classification System at the Protein Information. Nucleic Acids Res. 2004;32(Database issue):D112–D114. doi: 10.1093/nar/gkh097. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Pedruzzi I., Rivoire C., Auchincloss A. H., Coudert E., Keller G., de Castro E., Baratin D., Cuche B. A., Bougueleret L., Poux S., Redaschi N., Xenarios I., Bridge A.. HAMAP in 2015: Updates to the Protein Family Classification and Annotation System. Nucleic Acids Res. 2015;43(D1):D1064–D1070. doi: 10.1093/nar/gku1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Casimiro-Soriguer C. S., Muñoz-Mérida A., Pérez-Pulido A. J.. Sma3s: A Universal Tool for Easy Functional Annotation of Proteomes and Transcriptomes. Proteomics. 2017;17(12):1700071. doi: 10.1002/pmic.201700071. [DOI] [PubMed] [Google Scholar]
  44. Szklarczyk D., Gable A. L., Lyon D., Junge A., Wyder S., Huerta-Cepas J., Simonovic M., Doncheva N. T., Morris J. H., Bork P., Jensen L. J., von Mering C.. STRING V11: Protein-Protein Association Networks with Increased, Supporting Functional Discovery in Genome-Wide Datasets. Nucleic Acids Res. 2019;47(D1):D607–D613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kanehisa M., Goto S.. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Aramaki T., Blanc-Mathieu R., Endo H., Ohkubo K., Kanehisa M., Goto S., Ogata H.. KofamKOALA: KEGG Ortholog Assignment Based on Profile HMM and Adaptive Score Threshold. Bioinformatics. 2020;36(7):2251–2252. doi: 10.1093/bioinformatics/btz859. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Alcock B. P., Huynh W., Chalil R., Smith K. W., Raphenya A. R., Wlodarski M. A., Edalatmand A., Petkau A., Syed S. A., Tsang K. K., Baker S. J. C., Dave M., McCarthy M. C., Mukiri K. M., Nasir J. A., Golbon B., Imtiaz H., Jiang X., Kaur K., Kwong M., Liang Z. C., Niu K. C., Shan P., Yang J. Y. J., Gray K. L., Hoad G. R., Jia B., Bhando T., Carfrae L. A., Farha M. A., French S., Gordzevich R., Rachwalski K., Tu M. M., Bordeleau E., Dooley D., Griffiths E., Brown E. D. Z. H. L., Maguire F., Beiko R. G., Hsiao W. W. L., Brinkman F. S. L., Domselaar G.Van, McArthur A. G.. CARD 2023: Expanded Curation, Support for Machine Learning,and Resistome Prediction at the Comprehensive Antibiotic Database. Nucleic Acids Res. 2023;51(D1):D690–D699. doi: 10.1093/nar/gkac920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Chen L., Yang J., Yu J., Yao Z., Sun L., Shen Y., Jin Q.. VFDB: A Reference Database for Bacterial Virulence Factors. Nucleic Acids Res. 2005;33(Database issue):D325–D328. doi: 10.1093/nar/gki008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zhou Y., Zhang Y., Lian X., Li F., Wang C., Zhu F., Qiu Y., Chen Y.. Therapeutic Target Database Update 2022: Facilitating Drug Discovery with Enriched Comparative Data of Targeted Agents. Nucleic Acids Res. 2022;50(1):D1398–D1407. doi: 10.1093/nar/gkab953. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Gaulton A., Bellis L. J., Bento A. P., Chambers J., Davies M., Hersey A., Light Y., McGlinchey S., Michalovich D., Al-Lazikani B., Overington J. P.. ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery. Nucleic Acids Res. 2012;40(Database issue):D1100–D1107. doi: 10.1093/nar/gkr777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. da Silva R., Gabriel C., Scantlebury J., Jasinski M., Ackerman D., Jewison T., Sajed T., Gautam V., Wishart D. S., Lee B., Liigand J., Peters H., Wang R. Q. R., Nguyen T., Sharp M. S. D., Knox C., Wilson M., Klinger C. M., Franklin M., Oler E., Wilson A., Pon A., Cox J., Chin N. E. L., Strawbridge S. A., Garcia-Patino M., Kruger R., Sivakumaran A., Sanford S., Doshi R., Khetarpal N., Fatokun O., Doucet D., Zubkowski A., Rayat D. Y., Jackson H., Harford K., Anjum A., Zakir M., Tian S. W. F.. DrugBank 6.0: The DrugBank Knowledgebase for 2024. Nucleic Acids Res. 2024;52(D1):D1265–D1275. doi: 10.1093/nar/gkad976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Altschul S. F., Madden T. L., Schäffer A. A., Zhang J., Zhang Z., Miller W., Lipman D. J.. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Res. 1997;25(17):3389–3402. doi: 10.1093/nar/25.17.3389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Berman H. M., Westbrook J., Feng Z., Gilliland G., Bhat T. N., Weissig H., Shindyalov I. N., Bourne P. E.. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. McLean K., Lee D., Holmes E. A., Penewit K., Waalkes A., Ren M., Lee S. A., Gasper J., Manoil C., Salipante S. J.. Genomic Analysis Identifies Novel Pseudomonas Aeruginosa Resistance Genes under Selection during Inhaled Aztreonam Therapy in Vivo. Antimicrob. Agents Chemother. 2019;63(9):e00866-19. doi: 10.1128/AAC.00866-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Moradali M. F., Ghods S., Rehm B. H. A.. Activation Mechanism and Cellular Localization of Membrane-Anchored Alginate Polymerase in Pseudomonas Aeruginosa. Appl. Environ. Microbiol. 2017;83(9):e03499-16. doi: 10.1128/AEM.03499-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Xipell M., Bodro M., Marco F., Martinez J. A., Soriano A.. Successful Treatment of Three Severe Mdr or XDR Pseudomonas Aeruginosa INfections with Ceftolozane/Tazobactam. Future Microbiol. 2017;12(14):1323–1326. doi: 10.2217/fmb-2017-0018. [DOI] [PubMed] [Google Scholar]
  57. Irum S., Andleeb S., Ali A., Rashid M. I., Majid M.. Quest for Novel Preventive and Therapeutic Options Against Multidrug-Resistant Pseudomonas Aeruginosa. Int. J. Pept. Res. Ther. 2021;27(4):2313. doi: 10.1007/s10989-021-10255-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Peng B., Li H., Peng X.. Proteomics Approach to Understand Bacterial Antibiotic Resistance Strategies. Expert Rev. Proteomics. 2019;16(10):829–839. doi: 10.1080/14789450.2019.1681978. [DOI] [PubMed] [Google Scholar]
  59. Rahman A., Sarker M. T., Islam M. A., Hossain M. U., Hasan M., Susmi T. F.. Targeting Essential Hypothetical Proteins of Pseudomonas Aeruginosa PAO1 for Mining of Novel Therapeutics: An In Silico Approach. Biomed Res. Int. 2023;2023:1–28. doi: 10.1155/2023/1787485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Atron B., Yousif Z., Gómez-Martínez J., Rocha-Gracia R. D. C., Bello-López E., Cevallos M. A., Castañeda-Lucio M., Sáenz Y., Jiménez-Flores G., Cortés-Cortés G., López-García A., Lozano-Zarain P., Wright B. W., Kamath K. S., Krisp C., Molloy M. P., Shahbaaz M., Ahmad F., Hassan M. I., Golle A., Janezic S., Rupnik M., Rahman A., Sarker M. T., Islam M. A., Hossain M. U., Hasan M., Susmi T. F.. In Silico Identification of Novel Therapeutic Targets and Epitopes among the Essential Hypothetical Protein of Pseudomonas Aeruginosa: A Novel Approach for Antivirulence Therapy. BMC Microbiol. 2023;12(5):69. doi: 10.21203/rs.3.rs-2679079/v1. [DOI] [Google Scholar]
  61. Rahman A., Sarker M. T., Islam M. A., Hossain M. U., Hasan M., Susmi T. F.. Targeting Essential Hypothetical Proteins of Pseudomonas Aeruginosa PAO1 for Mining of Novel Therapeutics: An In Silico Approach. Biomed Res. Int. 2023;2023:1–28. doi: 10.1155/2023/1787485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Pranavathiyani G., Prava J., Rajeev A. C., Pan A.. Novel Target Exploration from Hypothetical Proteins of Klebsiella Pneumoniae MGH 78578 Reveals a Protein Involved in Host-Pathogen Interaction. Front. Cell. Infect. Microbiol. 2020;10:109. doi: 10.3389/fcimb.2020.00109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Samsonov G. V., Straumanis M. E.. Handbook of the Physicochemical Properties of the Elements. Phys. Today. 1968;21(9):97. doi: 10.1063/1.3035172. [DOI] [Google Scholar]
  64. Islam M. S., Shahik S. M., Sohel M., Patwary N. I. A., Hasan M. A.. In Silico Structural and Functional Annotation of Hypothetical Proteins of Vibrio Cholerae O139. Genomics Inform. 2015;13(2):53–59. doi: 10.5808/GI.2015.13.2.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Brinkworth A. J., Hammer C. H., Olano L. R., Kobayashi S. D., Chen L., Kreiswirth B. N., DeLeo F. R.. Identification of Outer Membrane and Exoproteins of Carbapenem-Resistant Multilocus Sequence Type 258 Klebsiella Pneumoniae. PLoS One. 2015;10(4):e0123219. doi: 10.1371/journal.pone.0123219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Motta S., Vecchietti D., Martorana A. M., Brunetti P., Bertoni G., Polissi A., Mauri P., Di Silvestre D.. The Landscape of Pseudomonas Aeruginosa Membrane-Associated Proteins. Cells. 2020;9(11):2421. doi: 10.3390/cells9112421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Anis Ahamed N., Panneerselvam A., Arif I. A., Syed Abuthakir M. H., Jeyam M., Ambikapathy V., Mostafa A. A.. Identification of Potential Drug Targets in Human Pathogen Bacillus Cereus and Insight for Finding Inhibitor through Subtractive Proteome and Molecular Docking Studies. J. Infect. Public Health. 2021;14(1):160–168. doi: 10.1016/j.jiph.2020.12.005. [DOI] [PubMed] [Google Scholar]
  68. Baliga P., Shekar M., Venugopal M. N.. Potential Outer Membrane Protein Candidates for Vaccine Development Against the Pathogen Vibrio Anguillarum: A Reverse Vaccinology Based Identification. Curr. Microbiol. 2018;75(3):368–377. doi: 10.1007/s00284-017-1390-z. [DOI] [PubMed] [Google Scholar]
  69. Chung Y. J., Saier J.. SMR-Type Multidrug Resistance Pumps. Curr. Opin. Drug Discov. Dev. 2001;4:234–237. [PubMed] [Google Scholar]
  70. Qin S., Xiao W., Zhou C., Pu Q., Deng X., Lan L., Liang H., Song X., Wu M.. Pseudomonas Aeruginosa: Pathogenesis, Virulence Factors, Antibiotic Resistance, Interaction with Host, Technology Advances and Emerging Therapeutics. Signal Transduct. Target. Ther. 2022;7(1):199. doi: 10.1038/s41392-022-01056-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Huber P., Basso P., Reboud E., Attree I.. Pseudomonas Aeruginosa Renews Its Virulence Factors. Environ. Microbiol. Rep. 2016;8:564. doi: 10.1111/1758-2229.12443. [DOI] [PubMed] [Google Scholar]
  72. Rocha A. J., DeOliveira Barsottini M. R., Rocha R. R., Laurindo M. V., DeMoraes F. L. L., DaRocha S. L.. Pseudomonas Aeruginosa: Virulence Factors and Antibiotic Resistance Genes. Braz. Arch. Biol. Technol. 2019;62:19180503. doi: 10.1590/1678-4324-2019180503. [DOI] [Google Scholar]
  73. Solanki V., Tiwari M., Tiwari V.. Prioritization of Potential Vaccine Targets Using Comparative Proteomics and Designing of the Chimeric Multi-Epitope Vaccine against Pseudomonas Aeruginosa. Sci. Rep. 2019;9(1):5240. doi: 10.1038/s41598-019-41496-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Crouzet J., Cauchois L., Blanche F., Debussche L., Thibaut D., Rouyez M. C., Rigault S., Mayaux J. F., Cameron B.. Nucleotide Sequence of a Pseudomonas Denitrificans 5.4-Kilobase DNA Fragment Containing Five Cob Genes and Identification of Structural Genes Encoding S-Adenosyl-L-Methionine: Uroporphyrinogen III Methyltransferase and Cobyrinic Acid a, c-Diamide Synthase. J. Bacteriol. 1990;172(10):5968–5979. doi: 10.1128/jb.172.10.5968-5979.1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Crespo A., Blanco-Cabra N., Torrents E.. Aerobic Vitamin B(12) Biosynthesis Is Essential for Pseudomonas Aeruginosa Class II Ribonucleotide Reductase Activity During Planktonic and Biofilm Growth. Front. Microbiol. 2018;9:986. doi: 10.3389/fmicb.2018.00986. [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

ao5c02268_si_001.pdf (4MB, pdf)

Data Availability Statement

The data used in this study is available from the corresponding author upon reasonable request.

Articles from ACS Omega are provided here courtesy of American Chemical Society

RESOURCES