<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">CMB</journal-id><journal-title-group><journal-title>Computational Molecular Bioscience</journal-title></journal-title-group><issn pub-type="epub">2165-3445</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/cmb.2024.141002</article-id><article-id pub-id-type="publisher-id">CMB-131193</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biomedical&amp;Life Sciences</subject></subj-group></article-categories><title-group><article-title>
 
 
  Structural and Functional Annotation of Hypothetical Protein of &lt;i&gt;Fusobacterium nucleatum&lt;/i&gt; Strain MJR7757B: An in Silico Approach
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Md.</surname><given-names>Isrfil Hossen</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fouzia</surname><given-names>Mostafa</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Nusrat</surname><given-names>Jahan</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jannatul</surname><given-names>Ferdaus</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Amgad</surname><given-names>Albahi</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Sayed</surname><given-names>Mashequl Bari</given-names></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff5"><addr-line>National Food Research Centre, Khartoum, Sudan</addr-line></aff><aff id="aff2"><addr-line>Abdul Malek Ukil Medical College, Noakhali, Bangladesh</addr-line></aff><aff id="aff1"><addr-line>College of Food Science and Technology, Huazhong Agricultural University, Wuhan, China</addr-line></aff><aff id="aff6"><addr-line>Department of Aquatic Animal Health Management, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh</addr-line></aff><aff id="aff4"><addr-line>Department of Medicine, IBN Sina Medical College, Dhaka, Bangladesh</addr-line></aff><aff id="aff3"><addr-line>Department of Crop Science and Technology, Rajshahi University, Rajshahi, Bangladesh</addr-line></aff><pub-date pub-type="epub"><day>18</day><month>02</month><year>2024</year></pub-date><volume>14</volume><issue>01</issue><fpage>17</fpage><lpage>33</lpage><history><date date-type="received"><day>7,</day>	<month>November</month>	<year>2023</year></date><date date-type="rev-recd"><day>16,</day>	<month>February</month>	<year>2024</year>	</date><date date-type="accepted"><day>19,</day>	<month>February</month>	<year>2024</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  <em>Fusobacterium nucleatum</em> is an anaerobic, commensal, gram-negative oral bacterium that is carcinogenic and causes a wide range of human diseases. The present study focused on the analysis of the hypothetical protein, HMPREF3221_01179, derived from 
  <em>F. nucleatum</em> strain MJR7757B, employing various computational methods to anticipate both its structure and functional characteristics. NCBI conserved domain analysis, NCBI BLASTp and MEGA Phylogenetic tree study characterize the target protein as an outer membrane efflux protein (ToIC family) which facilitate the bacterial transmembrane transport. With a molecular weight of 52120.02 Da, an isoelectric point (pI) of 8.33, and an instability index of 29.47, the protein is anticipated to exhibit good solubility in the extracellular space and crucial stability for pharmaceutical applications. The protein’s structure meets quality standards during the construction and refinement of its 3D model. The efflux inhibitor Arginine beta-naphthylamide exhibits a significant binding affinity (-7.1 kcal/mol) to the binding site of the target protein. The in-silico analysis improves the understanding of the protein and facilitates future investigations into therapeutic medication.
 
</p></abstract><kwd-group><kwd>&lt;i&gt;Fusobacterium nucleatum&lt;/i&gt;</kwd><kwd> In Silico</kwd><kwd> Bacteria</kwd><kwd> Hypothetical Protein</kwd><kwd> Molecular Docking</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Fusobacterium nucleatum is a prevalent bacterium found in the mouth that has been associated with several human diseases, such as the formation and advancement of colorectal cancer (CRC) [<xref ref-type="bibr" rid="scirp.131193-ref1">1</xref>] . F. nucleatum triggers inflammation, 52 which causes genetic instability and inhibits the body’s immune responses against tumors [<xref ref-type="bibr" rid="scirp.131193-ref2">2</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref3">3</xref>] . This gram-negative anaerobic species also associated with adverse pregnancy outcomes, gastrointestinal disorders, cardiovascular disease, rheumatoid arthritis, respiratory tract infections, Lemierre’s syndrome, and Alzheimer’s disease [<xref ref-type="bibr" rid="scirp.131193-ref4">4</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref5">5</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref6">6</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref7">7</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref8">8</xref>] . F. nucleatum infections commonly respond well to standard antibiotic therapies. Among the effective antibiotics are metronidazole, clindamycin, and beta-lactam antibiotics such as penicillin or amoxicillin [<xref ref-type="bibr" rid="scirp.131193-ref9">9</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref10">10</xref>] . Despite advancements in genomic sequencing, a substantial portion of F. nucleatum’s proteome remains uncharacterized, including the hypothetical protein HMPREF3221_01179.</p><p>Hypothetical proteins (HPs), present in genomes, lack experimental characterization yet are essential for diverse cellular processes and signaling pathways. Their annotation is crucial for comprehending disease mechanisms, aiding drug design, vaccine production, and identifying virulent proteins in bacteria through in-silico studies, offering valuable insights into diseases and pathogenesis [<xref ref-type="bibr" rid="scirp.131193-ref11">11</xref>] . In the field of bioinformatics, researchers are actively unveiling the biological functions and characteristics of millions of uncharacterized proteins from different organisms, which perform a wide range of functions, including structuring cells and organisms and participating in vital in vivo processes through interactions with other molecules [<xref ref-type="bibr" rid="scirp.131193-ref12">12</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref13">13</xref>] . By employing bioinformatics methods, researchers can analyze protein structures in 3D, identify new domains, and uncover the functions of proteins, enhancing our understanding of their biological roles [<xref ref-type="bibr" rid="scirp.131193-ref14">14</xref>] . In cases where experimental determination of a protein’s function is challenging, function inference can be achieved through sequence similarity; if this fails, analysis of protein structure offers valuable functional clues, with recent advancements in combining various structure-based approaches and integrating evidence from multiple sources [<xref ref-type="bibr" rid="scirp.131193-ref15">15</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref16">16</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref17">17</xref>] . Understanding the role of such proteins is pivotal for comprehending the pathogenicity and biology of this bacterium.</p><p>This study focused on the hypothetical protein HMPREF3221_01179 from F. nucleatum, a bacterium associated with diverse human infections. Using in silico methods, we have investigated the structural and functional annotations of the hypothetical protein (accession no. KXA20922.1) from the F. nucleatum strain MJR7757B.</p></sec><sec id="s2"><title>2. Materials and Methods</title><sec id="s2_1"><title>2.1. Hypothetical Protein Sequence Retrieval</title><p>There are over 400 genome sequences of F. nucleatum accessible in the National Center for Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov) [<xref ref-type="bibr" rid="scirp.131193-ref18">18</xref>] . This research select a hypothetical protein HMPREF3221_01179 (accession no. KXA20922.1) from the F. nucleatum strain MJR7757B. This protein consists of 438 amino acid residues, and its primary sequence was retrieved in FASTA format for in-depth analysis [<xref ref-type="bibr" rid="scirp.131193-ref19">19</xref>] .</p></sec><sec id="s2_2"><title>2.2. Analysis of Physicochemical Properties of Hypothetical Protein</title><p>The physical and chemical properties of the target hypothetical protein were analyzed using the ProtParam tool available on the ExPASy website (http://web.expasy.org/protparam/) [<xref ref-type="bibr" rid="scirp.131193-ref20">20</xref>] . These properties included molecular weight, aliphatic index (AI) [<xref ref-type="bibr" rid="scirp.131193-ref21">21</xref>] , extinction coefficients [<xref ref-type="bibr" rid="scirp.131193-ref22">22</xref>] , GRAVY (grand average of hydropathy) [<xref ref-type="bibr" rid="scirp.131193-ref21">21</xref>] , and isoelectric point (pI) [<xref ref-type="bibr" rid="scirp.131193-ref23">23</xref>] .</p></sec><sec id="s2_3"><title>2.3. Hypothetical Protein (Conserved Domains) Function Prediction</title><p>The conserved domain analysis of the hypothetical protein was conducted using NCBI Conserved Domain Search Service (https://www.ncbi.nlm.nih.gov/structure/cdd/wrpsb.cgi) [<xref ref-type="bibr" rid="scirp.131193-ref24">24</xref>] , Pfam (https://pfam.xfam.org) [<xref ref-type="bibr" rid="scirp.131193-ref25">25</xref>] , and InterProScan (https://www.ebi.ac.uk/interpro/search/sequence) [<xref ref-type="bibr" rid="scirp.131193-ref26">26</xref>] . CD Search detects conserved domains within a protein sequence by comparing the query sequence using RPS-BLAST (Reverse Position-Specific BLAST) against position-specific score matrices derived from conserved domain alignments in the Conserved Domain Database (CDD) [<xref ref-type="bibr" rid="scirp.131193-ref27">27</xref>] . Pfam, a protein family database, provides annotations and multiple sequence alignments generated through hidden Markov models (HMMs) [<xref ref-type="bibr" rid="scirp.131193-ref25">25</xref>] .</p></sec><sec id="s2_4"><title>2.4. Multiple Sequence Alignment and Phylogenetic Analysis</title><p>A search for protein homologs was conducted using BLASTp from NCBI (http://www.ncbi.nlm.nih.gov) against the nonredundant database, employing default parameters. Sequence alignment and phylogenetic tree construction were carried out using the MEGA 11 program [<xref ref-type="bibr" rid="scirp.131193-ref28">28</xref>] . Specifically, the ClustalW algorithm and Maximum Likelihood (ML) technique within MEGA 11 were employed for iterative Multiple Sequence Alignment (MSA) and tree-building processes, respectively.</p></sec><sec id="s2_5"><title>2.5. Protein Structure Preparation</title><p>The secondary structure of the protein was predicted using the PSI-blast based secondary structure prediction (PSIPRED) (http://bioinf.cs.ucl.ac.uk/psipred) [<xref ref-type="bibr" rid="scirp.131193-ref29">29</xref>] and Self-Optimized Prediction Method with Alignment (SOPMA) (https://npsaprabi.ibcp.fr/cgibin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) [<xref ref-type="bibr" rid="scirp.131193-ref30">30</xref>] servers. The 3D structure of the target protein was determined using the SWISS-MODEL (https://swissmodel.expasy.org/) server [<xref ref-type="bibr" rid="scirp.131193-ref31">31</xref>] . This server automatically searches BLASTp to identify suitable templates for each protein sequence. The resulting 3D model structure was visualized using BIOVIA Discovery Studio Visualizer (BIOVIA Discovery Studio 2021). The three-dimensional model structure generated by the SWISS-MODEL server was further refined using the software Swiss-PdbViewer [<xref ref-type="bibr" rid="scirp.131193-ref32">32</xref>] .</p></sec><sec id="s2_6"><title>2.6. Protein Quality Assessment</title><p>The quality of the generated model structure was assessed using various evaluation tools, including PROCHECK (https://www.ebi.ac.uk/thornton-srv/software/PROCHECK) [<xref ref-type="bibr" rid="scirp.131193-ref33">33</xref>] , QMEAN (https://swissmodel.expasy.org/qmean) [<xref ref-type="bibr" rid="scirp.131193-ref34">34</xref>] from the ExPASy server of the SWISS-MODEL workspace, and ERRAT (https://saves.mbi.ucla.edu/) [<xref ref-type="bibr" rid="scirp.131193-ref35">35</xref>] . Z-scores for both proteins were estimated using the ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php) server [<xref ref-type="bibr" rid="scirp.131193-ref36">36</xref>] .</p></sec><sec id="s2_7"><title>2.7. Protein Active Site Prediction</title><p>The Computed Atlas of Surface Topography of Proteins (CASTp) server (http://sts.bioe.uic.edu/castp/calculation.html) was employed to identify the predictive protein’s active site. It is essential for predicting the regions and critical residues involved in protein-ligand interactions. The CASTp results were visualized using BIOVIA Discovery Studio Visualizer software.</p></sec><sec id="s2_8"><title>2.8. Subcellular Localization of Protein</title><p>The CELLO: Subcellular Localization Predictive System (http://cello.life.nctu.edu.tw) [<xref ref-type="bibr" rid="scirp.131193-ref37">37</xref>] , Predicts Subcellular Localization of Prokaryotic Proteins (PSLpred) (https://webs.iiitd.edu.in/raghava/pslpred/) [<xref ref-type="bibr" rid="scirp.131193-ref38">38</xref>] , PSORTb v3.0.2 (https://www.psort.org/psortb/) [<xref ref-type="bibr" rid="scirp.131193-ref39">39</xref>] and SOSUI (http://harrier.nagahama-i-bio.ac.jp/sosui) [<xref ref-type="bibr" rid="scirp.131193-ref40">40</xref>] servers were utilized to predict the subcellular location of the hypothetical protein.</p></sec><sec id="s2_9"><title>2.9. Molecular Docking Analysis</title><p>Docking analysis was conducted using Autodock Vina software (http://vina.scripps.edu/download.html) [<xref ref-type="bibr" rid="scirp.131193-ref41">41</xref>] , which aids in studying and predicting ligand interactions with macromolecules. The ligand utilized for docking was Arginine beta-naphthylamide which is an inhibitor of ToIC family proteins. Autodock Vina determined the binding affinity between the target protein and ligand [<xref ref-type="bibr" rid="scirp.131193-ref42">42</xref>] . Protein-protein docking between the target protein and the hemolysin-coregulated protein1 (Hcp1) of S. Typhimurium was performed using the ClusPro 2.0 server [<xref ref-type="bibr" rid="scirp.131193-ref43">43</xref>] . The docking results were analyzed with Discovery studio visualizer.</p></sec></sec><sec id="s3"><title>3. Results and Discussions</title><sec id="s3_1"><title>3.1. Protein Sequence Retrieval</title><p>The hypothetical protein identified under the accession number KXA20922.1 originates from the F. nucleatum strain MJR7757B. This protein consists of 438 amino acid residues, and its primary sequence was obtained in FASTA format to enable subsequent analysis (<xref ref-type="table" rid="table1">Table 1</xref>).</p></sec><sec id="s3_2"><title>3.2. Protein Physicochemical Properties</title><p>The putative protein consisted of 438 amino acids and had a molecular weight of 52120.02 Da. It is believed that these amino acids have a half-life of more than 10 hours in bacteria. The pH of the protein is 8.33, indicating a slightly alkaline nature. Their aliphatic index (AI) of 85.89 suggests the presence of aliphatic side chains. The hydropathicity (GRAVY) has a grand average of −0.823, showing an average hydrophilic nature. The instability index (II) is 29.47, suggesting a considerable level of stability (<xref ref-type="table" rid="table2">Table 2</xref>).</p></sec><sec id="s3_3"><title>3.3. Protein Functional Prediction</title><p>Domain analysis involves identifying, characterizing, and understanding the roles of individual domains to gain insights into the overall function and organization of proteins. Several annotation techniques were used to identify conserved regions (domains) and predict the functions of the HP protein. According to the NCBI-CD Search, InterProScan, and Pfam databases the target protein belongs to the outer membrane efflux protein (ToIC family). The ToIC superfamily domain, predicted by the NCBI-CDD server, has an E-value of 7.71e−09</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> The properties of hypothetical protein retrieved from NCBI database</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Properties</th><th align="center" valign="middle" >Hypothetical Protein</th></tr></thead><tr><td align="center" valign="middle" >Locus</td><td align="center" valign="middle" >KXA20922</td></tr><tr><td align="center" valign="middle" >Definition</td><td align="center" valign="middle" >Hypothetical protein HMPREF3221_01179 [F, nucleatum]</td></tr><tr><td align="center" valign="middle" >Accession</td><td align="center" valign="middle" >KXA20922</td></tr><tr><td align="center" valign="middle" >Version</td><td align="center" valign="middle" >KXA20922.1</td></tr><tr><td align="center" valign="middle" >Amino acid</td><td align="center" valign="middle" >438</td></tr><tr><td align="center" valign="middle" >Organisms</td><td align="center" valign="middle" >F. nucleatum</td></tr><tr><td align="center" valign="middle" >FASTA sequence</td><td align="center" valign="middle" >&gt;KXA20922.1 hypothetical protein HMPREF3221_01179 [F. nucleatum] MIRERMNMKKILLFFLILTSLNCSAQETLSIDEALNRVGNDRESYEFKKFQNSQEGTNVKIKDNKLGDFN GVTLSSGYNISENNFDNRPRKYDRTFQNKATYGPFFVNYNYVQSDRSYVSFGVEKNLKDVFYSKYNSNLK INNLQLELNKISYDKNIQTKKINLVSLYQDILNTKNELEYRKKAYEHYRVDLDKLKKSYELGASPKINLE SVELEAEDSKLQIDILETKLKSLYDIGKTDYNIDFENYKLLDFVENNESIDFILNSYMKDEVEELRLSLS MAEERKSYSNYDRYMPDLYLGYERVDRNLRGDRYYRDQDLFTIKFSKKLFSTDSEYKLNELEVENLKNDL NEKIRVINAEKIKLKSEYHELLKLTSIGDKKSNIAYKKYLIKEKEYELNKSSYLDVIDEYNKYLSQEIET KKAKNALNAFVYKIKIKR</td></tr></tbody></table></table-wrap><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> The physicochemical properties of hypothetical protein HMPREF3221_01179</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >ProtParam Parameters</th><th align="center" valign="middle" >Values</th></tr></thead><tr><td align="center" valign="middle" >Number of amino acids</td><td align="center" valign="middle" >438</td></tr><tr><td align="center" valign="middle" >Molecular weight (MW)</td><td align="center" valign="middle" >52120.02 Da</td></tr><tr><td align="center" valign="middle" >Theoretical pl (Isoelectric point)</td><td align="center" valign="middle" >8.33</td></tr><tr><td align="center" valign="middle" >Total number of negatively charged (Asp + Glu)</td><td align="center" valign="middle" >72</td></tr><tr><td align="center" valign="middle" >Total number of positively charged (Arg + Lys)</td><td align="center" valign="middle" >75</td></tr><tr><td align="center" valign="middle" >Estimated half-life (hr)</td><td align="center" valign="middle" >&gt;10</td></tr><tr><td align="center" valign="middle" >Instability index</td><td align="center" valign="middle" >29.47</td></tr><tr><td align="center" valign="middle" >Aliphatic index (AI)</td><td align="center" valign="middle" >85.89</td></tr><tr><td align="center" valign="middle" >Grade average of hydropathicity (GRAVY)</td><td align="center" valign="middle" >−0.823</td></tr><tr><td align="center" valign="middle" >Number of atoms</td><td align="center" valign="middle" >7357</td></tr></tbody></table></table-wrap><p>and is located at amino acid residues 92 - 428. Outer membrane efflux protein (ToIC protein family) has a variety of important functions in bacterial physiology. They aggressively eliminate a range of compounds, such as antibiotics and poisons, serving as a barrier against dangerous chemicals and maintaining cellular homeostasis [<xref ref-type="bibr" rid="scirp.131193-ref44">44</xref>] [<xref ref-type="bibr" rid="scirp.131193-ref45">45</xref>] . Their main documented role is in drug resistance, where they force antibiotics out of cells and so promote multidrug resistance. They engage in interbacterial interactions with certain bacteria (Escherichia coli, Pseudomonas aeruginosa, and Salmonella enterica) by exporting virulence factors or poisons to rival bacteria. They may also contribute to biofilms’ production and increase pathogenicity by exporting toxins. Certain efflux proteins move quorum-sensing signalling chemicals [<xref ref-type="bibr" rid="scirp.131193-ref46">46</xref>] .</p></sec><sec id="s3_4"><title>3.4. Sequence Alignment Assessment and Phylogenetic Analysis</title><p>According to the NCBI BLASTp search of the target protein in compared to the nonredundant database, the protein shares 98% - 100% sequence similarity with other known ToIC superfamily proteins from different organisms (<xref ref-type="table" rid="table3">Table 3</xref>). A phylogenetic tree was constructed to depict the relationship between target hypothetical protein and other ToIC family proteins. The BLASTp results were utilized in the construction of the tree by using Mega11 software. The results suggest that most of the proteins are closely related to each other and found a common ancestor (<xref ref-type="fig" rid="fig1">Figure 1</xref>).</p></sec><sec id="s3_5"><title>3.5. Protein Structure Analysis</title><p>The results obtained from the SOPMA analysis revealed three conformational states: extended strand (11.64%), alpha helix (60.05%), and random coil (25.11%). The results obtained using PSIPRED showed that the random coil accounted for 25.38% of the structure, the alpha helix accounted for 60%, and the extended strand accounted for 11.77%. The PSIPRED utilized for the prediction of the secondary structure of the protein is shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>.</p><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> NCBI BLASTp result shows thesequence similarity with the target hypotheticalprotein sequence</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Accession</th><th align="center" valign="middle" >Organism Name</th><th align="center" valign="middle" >Protein Name</th><th align="center" valign="middle" >Scores</th><th align="center" valign="middle" >Per. Identity</th><th align="center" valign="middle" >E-value</th></tr></thead><tr><td align="center" valign="middle" >KXA20922.1</td><td align="center" valign="middle" >Fusobacterium nucleatum</td><td align="center" valign="middle" >hypothetical protein HMPREF3221_01179</td><td align="center" valign="middle" >855</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >OFQ57685.1</td><td align="center" valign="middle" >Fusobacterium sp. HMSC065F01</td><td align="center" valign="middle" >hypothetical protein HMPREF2931_08605</td><td align="center" valign="middle" >840</td><td align="center" valign="middle" >99.54</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_022070077.1</td><td align="center" valign="middle" >Fusobacterium</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >839</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >ALF18214.1</td><td align="center" valign="middle" >Fusobacterium animalis</td><td align="center" valign="middle" >hypothetical protein RN98_08525</td><td align="center" valign="middle" >839</td><td align="center" valign="middle" >99.08</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_249527044.1</td><td align="center" valign="middle" >Fusobacterium nucleatum</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >838</td><td align="center" valign="middle" >99.77</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_199488823.1</td><td align="center" valign="middle" >Fusobacterium sp. CM1</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >838</td><td align="center" valign="middle" >99.77</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_023040053.1</td><td align="center" valign="middle" >Fusobacterium nucleatum</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >837</td><td align="center" valign="middle" >99.3</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >ALF21854.1</td><td align="center" valign="middle" >Fusobacterium animalis</td><td align="center" valign="middle" >hypothetical protein RO08_05905</td><td align="center" valign="middle" >836</td><td align="center" valign="middle" >98.61</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_210388568.1</td><td align="center" valign="middle" >Fusobacterium sp. HMSC065F01</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >836</td><td align="center" valign="middle" >99.54</td><td align="center" valign="middle" >0</td></tr><tr><td align="center" valign="middle" >WP_187152472.1</td><td align="center" valign="middle" >Fusobacterium</td><td align="center" valign="middle" >TolC family protein</td><td align="center" valign="middle" >835</td><td align="center" valign="middle" >99.3</td><td align="center" valign="middle" >0</td></tr></tbody></table></table-wrap><p>The tertiary structure of the target protein was prepared through SWISS-MODEL service by utilizing a template demonstrating a sequence identity of 93.10% with the hypothetical protein. The Swiss-PdbViewer energy minimization server was utilized for the model protein structure’s energy reduction. The 3D structure after energy minimization is shown in Discover studio visualizer (<xref ref-type="fig" rid="fig3">Figure 3</xref>).</p></sec><sec id="s3_6"><title>3.6. Quality Assessment of Predicted Structure</title><p>Utilizing the SWISS-MODEL service, the protein’s three-dimensional (3D) structure was obtained, and it passed all model quality evaluation tools, such as PROCHECK, QMEAN, and ERRAT. As per the PROCHECK results, the ideal area in the Ramachandran plot included 96.6% of the amino acid residues (<xref ref-type="table" rid="table4">Table 4</xref>) (<xref ref-type="fig" rid="fig4">Figure 4</xref>). The overall residues with a QMEAN4 score of 0.54, regarded as satisfactory (<xref ref-type="fig" rid="fig5">Figure 5</xref>). Additionally, ERRAT projected that the protein structure had a quality factor of 97.6923, indicating high quality.</p><p>The Z-sore obtained from the ProSA server showed the model’s overall quality. It indicated whether the input structure fell within the range of scores normally found for novel proteins of similar size. The Z score for the model obtained from ProSA was −5.89 (<xref ref-type="fig" rid="fig6">Figure 6</xref>).</p><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> Ramachandran plots calculations of the target protein</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Plot statistics</th><th align="center" valign="middle" >Number of AA</th><th align="center" valign="middle" >Percentage (%)</th></tr></thead><tr><td align="center" valign="middle" >Residues in most favored regions [A, B, L]</td><td align="center" valign="middle" >403</td><td align="center" valign="middle" >96.6</td></tr><tr><td align="center" valign="middle" >Residues in additional allowed regions [a, b, l, p]</td><td align="center" valign="middle" >14</td><td align="center" valign="middle" >3.4</td></tr><tr><td align="center" valign="middle" >Residues in generously allowed regions [~a, ~b, ~l, ~p]</td><td align="center" valign="middle" >0</td><td align="center" valign="middle" >0.0</td></tr><tr><td align="center" valign="middle" >Residues in disallowed regions</td><td align="center" valign="middle" >0</td><td align="center" valign="middle" >0.0</td></tr><tr><td align="center" valign="middle" >Number of non-glycine and non-proline residues</td><td align="center" valign="middle" >417</td><td align="center" valign="middle" >100.00</td></tr><tr><td align="center" valign="middle" >Number of end-residues (excl. Gly and Pro)</td><td align="center" valign="middle" >2</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Number of glycine residues (shown as triangles)</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Number of proline residues</td><td align="center" valign="middle" >4</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Total number of residues</td><td align="center" valign="middle" >435</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap></sec><sec id="s3_7"><title>3.7. Active Site Detection</title><p>CASTp provides a detailed, comprehensive, and quantitative analysis of a protein’s topographical features. It can precisely locate and measure functional pockets on protein surfaces and within the 3D structure’s interior. Using the CASTp server, the active site of model structures was examined, and its amino acid residues were ascertained. Then Discover studio was utilized to visualize the results. The major pocket regions were found between 32 - 36, 389 - 396, and 432 - 438, respectively. The model protein’s active residues predicted by CASTp are ASP<sup>32</sup>, LEU<sup>35</sup>, ASN<sup>36</sup>, ASP<sup>154</sup>, ILE<sup>157</sup>, GLN<sup>158</sup>, LYS<sup>161</sup>, ASP<sup>270</sup>, TYR<sup>432</sup>, LYS<sup>435</sup>, ILE<sup>436</sup>, ARG<sup>438</sup> (<xref ref-type="fig" rid="fig7">Figure 7</xref>).</p></sec><sec id="s3_8"><title>3.8. Subcellular Localization of Hypothetical Protein</title><p>The CELLO program identified the location of the target protein at outer membrane with a 3.417 reliability score. The findings from PSORTb and PSLpred were also outer membrane and extracellular protein. A putative protein’s subcellular location is important since it indicates the function and role that the protein plays within a cell. It provides information on the protein’s regulation, interactions with other molecules, and possible role in illness. This knowledge is essential for basic research as well as the creation of new therapeutics [<xref ref-type="bibr" rid="scirp.131193-ref38">38</xref>] .</p></sec><sec id="s3_9"><title>3.9. Molecular Docking Analysis</title><p>Autodock Vina program was utilized to run a docking study between the ligand and the target protein, and the interaction was visualized by Discovery Studio (<xref ref-type="fig" rid="fig8">Figure 8</xref>). The hypothetical protein belongs to the ToIC protein family which are the efflux proteins that help in pumping the materials across the cell membrane. The compound Arginine beta-naphthylamide is known as an inhibitor of efflux proteins. Therefore, it is employed as a ligand in this work. The ligand demonstrated a substantial affinity for binding to the target hypothetical protein. The ligand’s binding affinity for the model was −7.1 kcal/mol (<xref ref-type="table" rid="table5">Table 5</xref>). It was discovered that several of the interaction residues in the proteins’ active sites were identical, as predicted by the CASTp server. The discovery of a significant binding affinity of the ligand with the protein of interest further supported our results.</p><p>Then the protein-protein interaction of the Hemolysis-coregulated protein 1 (Hcp1) protein of S. Typhimurium and the target protein was done by using Cluspro2.0. Hcp1 played an important role in the proper delivery of antibacterial toxins by interacting with efflux proteins. Hence, Hcp1 was utilized in protein-protein interactions. The docking outcomes are mentioned in (<xref ref-type="table" rid="table6">Table 6</xref>). It is noted that maximum residues have taken part in exchange from both proteins. The reason might be the selection of higher cluster members protein-ligand complex from the Cluspro 2.0 server. Experimental research has not yet revealed the precise nature of the interaction between the hcp1 and ToIC proteins. Belonging to the ToIC protein family, renowned for its efflux functions, the protein’s interactions with Hcp1 underscore its crucial involvement in the precise delivery of antibacterial toxins.</p><p>Overall, the retrieved target protein conserved sequence similar with many F. nucleatum species, which supports the efflux protein’s potential usage as a therapeutic target. The outer membrane efflux proteins are essential for bacterial major functions. In recent years, progress in understanding these proteins has been increased. To the best of our knowledge, this is the first investigation to describe the structural and functional properties of F. nucleatum efflux protein HMPREF3221_01179. We believe this research helps in understating the mechanism of bacterial functions and might help design new drugs in the future. However, more studies are needed to confirm its function at the experimental level.</p><table-wrap id="table5" ><label><xref ref-type="table" rid="table5">Table 5</xref></label><caption><title> Details of protein-ligand docking analysis</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Protein</th><th align="center" valign="middle" >Ligand</th><th align="center" valign="middle" >Binding Affinity (kcal/mol)</th><th align="center" valign="middle" >Category</th><th align="center" valign="middle" >Type of Interaction</th><th align="center" valign="middle" >Key Interacting Residues</th></tr></thead><tr><td align="center" valign="middle" >Hypothetical protein HMPREF3221_01179</td><td align="center" valign="middle" >Arginine beta-naphthylamide</td><td align="center" valign="middle" >−7.1</td><td align="center" valign="middle" >Hydrogen Bond</td><td align="center" valign="middle" >Conventional H bond, Pi-alkyl</td><td align="center" valign="middle" >Ile162, Ser166, Gln169, Asp170, Glu367, Lys433, Lys437</td></tr></tbody></table></table-wrap><table-wrap id="table6" ><label><xref ref-type="table" rid="table6">Table 6</xref></label><caption><title> Protein-protein interaction analysis</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Receptor</th><th align="center" valign="middle" >Ligand</th><th align="center" valign="middle" >Cluster Members</th><th align="center" valign="middle" >Weighted Energy Score of The Centre</th></tr></thead><tr><td align="center" valign="middle" >Hypothetical protein KXA20922.1</td><td align="center" valign="middle" >Hypothetical protein PA0085</td><td align="center" valign="middle" >75</td><td align="center" valign="middle" >−1071</td></tr></tbody></table></table-wrap></sec></sec><sec id="s4"><title>4. Conclusion</title><p>Microbial genome hypothetical proteins study is crucial for unravelling their unknown functions, leading to insights into microbial biology, potential drug targets, and applications in biotechnology. This in-depth analysis of the hypothetical protein HMPREF3221_01179 from F. nucleatum strain MJR7757B provides valuable insights into its structural, functional, and interaction properties, suggesting its potential as a therapeutic target. Additionally, these findings unveil opportunities for further exploration of this bacterium in the realm of biotechnological applications.</p></sec><sec id="s5"><title>Authors Contribution</title><p>Conceptualization: Md. Isrfil Hossen, Sayed Mashequl Bari. Methodology: Sayed Mashequl Bari, Md. Isrfil Hossen, Nusrat Jahan. Formal analysis: Sayed Mashequl Bari, Md. Isrfil Hossen, Nusrat Jahan, Fouzia Mostafa. Writing original draft: Sayed Mashequl Bari, Md. Isrfil Hossen, Fouzia Mostafa. Writing review &amp; editing: Amgad Albahi, Jannatul Ferdaus.</p></sec><sec id="s6"><title>Conflicts of Interest</title><p>No potential conflict of interest relevant to this article was reported.</p></sec><sec id="s7"><title>Cite this paper</title><p>Hossen, Md.I., Mostafa, F., Jahan, N., Ferdaus, J., Albahi, A. and Bari, S.M. (2024) Structural and Functional Annotation of Hypothetical Protein of Fusobacterium nucleatum Strain MJR7757B: An in Silico Approach. Computational Molecular Bioscience, 14, 17-33. https://doi.org/10.4236/cmb.2024.141002</p></sec></body><back><ref-list><title>References</title><ref id="scirp.131193-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Shang, F.-M. and Liu, H.-L. (2018) Fusobacterium nucleatum and Colorectal Cancer: A Review. WJGO, 10, 71-81. https://doi.org/10.4251/wjgo.v10.i3.71</mixed-citation></ref><ref id="scirp.131193-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Alon-Maimon, T., Mandelboim, O. and Bachrach, G. (2022) Fusobacterium nucleatum and Cancer. Periodontology, 89, 166-180. https://doi.org/10.1111/prd.12426</mixed-citation></ref><ref id="scirp.131193-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Chen, Y., Shi, T., Li, Y., Huang, L. and Yin, D. (2022) Fusobacterium nucleatum: The Opportunistic Pathogen of Periodontal and Peri-Implant Diseases. Frontiers in Microbiology, 13, Article 860149. https://doi.org/10.3389/fmicb.2022.860149</mixed-citation></ref><ref id="scirp.131193-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Han, Y.W. (2015) Fusobacterium nucleatum: A Commensal-Turned Pathogen. Current Opinion in Microbiology, 23, 141-147. https://doi.org/10.1016/j.mib.2014.11.013</mixed-citation></ref><ref id="scirp.131193-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Allen-Vercoe, E., Strauss, J. and Chadee, K. (2011) Fusobacterium nucleatum: An Emerging Gut Pathogen? Gut Microbes, 2, 294-298. https://doi.org/10.4161/gmic.2.5.18603</mixed-citation></ref><ref id="scirp.131193-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Bashir, A., Miskeen, A.Y., Bhat, A., Fazili, K.M. and Ganai, B.A. (2015) Fusobacterium nucleatum: An Emerging Bug in Colorectal Tumorigenesis. European Journal of Cancer Prevention, 24, 373-385. https://doi.org/10.1097/CEJ.0000000000000116</mixed-citation></ref><ref id="scirp.131193-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Storm, J.C., Ford, B.A. and Streit, J.A. (2013) Myocardial Infection Due to Fusobacterium nucleatum. Diagnostic Microbiology and Infectious Disease, 77, 373-375. https://doi.org/10.1016/j.diagmicrobio.2013.08.022</mixed-citation></ref><ref id="scirp.131193-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Nwaokorie, F.O., Coker, A.O., Ogunsola, F.T., Avika-Campos, M.J., Gaetti-Jardim, E., Ayanbadejo, P.O., Umeizudike, K.A. and Abdurrazaq, O.T. (2011) Isolation and Molecular Identification of Fusobacterium nucleatum from Nigerian Patients with Oro-Facial Infections. West African Journal of Medicine, 30, 125-129.</mixed-citation></ref><ref id="scirp.131193-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Le Monnier, A., Jamet, A., Carbonnelle, E., Barthod, G., Moumile, K., Lesage, F., Zahar, J.-R., Mannach, Y., Berche, P. and Couloigner, V. (2008) Fusobacterium Necrophorum Middle Ear Infections in Children and Related Complications: Report of 25 Cases and Literature Review. The Pediatric Infectious Disease Journal, 27, 613-617. https://doi.org/10.1097/INF.0b013e318169035e</mixed-citation></ref><ref id="scirp.131193-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Stergiopoulou, T. and Walsh, T.J. (2016) Fusobacterium necrophorum Otitis and Mastoiditis in Infants and Young Toddlers. European Journal of Clinical Microbiology &amp; Infectious Diseases, 35, 735-740. https://doi.org/10.1007/s10096-016-2612-1</mixed-citation></ref><ref id="scirp.131193-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Naveed, M., Makhdoom, S.I., Abbas, G., Safdari, M., Farhadi, A., Habtemariam, S., Shabbir, M.A., Jabeen, K., Asif, M.F. and Tehreem, S. (2022) The Virulent Hypothetical Proteins: The Potential Drug Target Involved in Bacterial Pathogenesis. Mini-Reviews in Medicinal Chemistry, 22, 2608-2623. https://doi.org/10.2174/1389557522666220413102107</mixed-citation></ref><ref id="scirp.131193-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Zhao, J., Cao, Y. and Zhang, L. (2020) Exploring the Computational Methods for Protein-Ligand Binding Site Prediction. Computational and Structural Biotechnology Journal, 18, 417-426. https://doi.org/10.1016/j.csbj.2020.02.008</mixed-citation></ref><ref id="scirp.131193-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Dukka, B.K. (2013) Structure-Based Methods for Computational Protein Functional Site Prediction. Computational and Structural Biotechnology Journal, 8, e201308005. https://doi.org/10.5936/csbj.201308005</mixed-citation></ref><ref id="scirp.131193-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Mills, C.L., Beuning, P.J. and Ondrechen, M.J. (2015) Biochemical Functional Predictions for Protein Structures of Unknown or Uncertain Function. Computational and Structural Biotechnology Journal, 13, 182-191. https://doi.org/10.1016/j.csbj.2015.02.003</mixed-citation></ref><ref id="scirp.131193-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Watson, J.D., Laskowski, R.A. and Thornton, J.M. (2005) Predicting Protein Function from Sequence and Structural Data. Current Opinion in Structural Biology, 15, 275-284. https://doi.org/10.1016/j.sbi.2005.04.003</mixed-citation></ref><ref id="scirp.131193-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Valencia, A. (2005) Automatic Annotation of Protein Function. Current Opinion in Structural Biology, 15, 267-274. https://doi.org/10.1016/j.sbi.2005.05.010</mixed-citation></ref><ref id="scirp.131193-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Espadaler, J., Querol, E., Aviles, F.X. and Oliva, B. (2006) Identification of Function-Associated Loop Motifs and Application to Protein Function Prediction. Bioinformatics, 22, 2237-2243. https://doi.org/10.1093/bioinformatics/btl382</mixed-citation></ref><ref id="scirp.131193-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Rapp, B.A. and Wheeler, D.L. (2002) GenBank. Nucleic Acids Research, 30, 17-20. https://doi.org/10.1093/nar/30.1.17</mixed-citation></ref><ref id="scirp.131193-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">The UniProt Consortium (2023) UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51, D523-D531.</mixed-citation></ref><ref id="scirp.131193-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D. and Bairoch, A. (2003) ExPASy: The Proteomics Server for In-Depth Protein Knowledge and Analysis. Nucleic Acids Research, 31, 3784-3788. https://doi.org/10.1093/nar/gkg563</mixed-citation></ref><ref id="scirp.131193-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">Kyte, J. and Doolittle, R.F. (1982) A Simple Method for Displaying the Hydropathic Character of a Protein. Journal of Molecular Biology, 157, 105-132. https://doi.org/10.1016/0022-2836(82)90515-0</mixed-citation></ref><ref id="scirp.131193-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">Gill, S.C. and von Hippel, P.H. (1989) Calculation of Protein Extinction Coefficients from Amino Acid Sequence Data. Analytical Biochemistry, 182, 319-326. https://doi.org/10.1016/0003-2697(89)90602-7</mixed-citation></ref><ref id="scirp.131193-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">Henriksson, G., Englund, A.K., Johansson, G. and Lundahl, P. (1995) Calculation of the Isoelectric Points of Native Proteins with Spreading of pKa Values. Electrophoresis, 16, 1377-1380. https://doi.org/10.1002/elps.11501601227</mixed-citation></ref><ref id="scirp.131193-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Marchler-Bauer, A., Bo, Y., Han, L., He, J., Lanczycki, C.J., Lu, S., Chitsaz, F., Derbyshire, M.K., Geer, R.C., Gonzales, N.R., et al. (2017) CDD/SPARCLE: Functional Classification of Proteins via Subfamily Domain Architectures. Nucleic Acids Research, 45, D200-D203. https://doi.org/10.1093/nar/gkw1129</mixed-citation></ref><ref id="scirp.131193-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">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., et al. (2021) Pfam: The Protein Families Database in 2021. Nucleic Acids Research, 49, D412-D419. https://doi.org/10.1093/nar/gkaa913</mixed-citation></ref><ref id="scirp.131193-ref26"><label>26</label><mixed-citation publication-type="other" xlink:type="simple">Quevillon, E., Silventoinen, V., Pillai, S., Harte, N., Mulder, N., Apweiler, R. and Lopez, R. (2005) InterProScan: Protein Domains Identifier. Nucleic Acids Research, 33, W116-W120. https://doi.org/10.1093/nar/gki442</mixed-citation></ref><ref id="scirp.131193-ref27"><label>27</label><mixed-citation publication-type="other" xlink:type="simple">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., et al. (2015) CDD: NCBI’s Conserved Domain Database. Nucleic Acids Research, 43, D222-D226. https://doi.org/10.1093/nar/gku1221</mixed-citation></ref><ref id="scirp.131193-ref28"><label>28</label><mixed-citation publication-type="other" xlink:type="simple">Kumar, S., Nei, M., Dudley, J. and Tamura, K. (2008) MEGA: A Biologist-Centric Software for Evolutionary Analysis of DNA and Protein Sequences. Briefings in Bioinformatics, 9, 299-306. https://doi.org/10.1093/bib/bbn017</mixed-citation></ref><ref id="scirp.131193-ref29"><label>29</label><mixed-citation publication-type="other" xlink:type="simple">Buchan, D.W.A. and Jones, D.T. (2019) The PSIPRED Protein Analysis Workbench: 20 Years On. Nucleic Acids Research, 47, W402-W407. https://doi.org/10.1093/nar/gkz297</mixed-citation></ref><ref id="scirp.131193-ref30"><label>30</label><mixed-citation publication-type="other" xlink:type="simple">Combet, C., Blanchet, C., Geourjon, C. and Deléage, G. (2000) NPS@: Network Protein Sequence Analysis. Trends in Biochemical Sciences, 25, 147-150. https://doi.org/10.1016/S0968-0004(99)01540-6</mixed-citation></ref><ref id="scirp.131193-ref31"><label>31</label><mixed-citation publication-type="other" xlink:type="simple">Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., et al. (2018) SWISS-MODEL: Homology Modelling of Protein Structures and Complexes. Nucleic Acids Research, 46, W296-W303. https://doi.org/10.1093/nar/gky427</mixed-citation></ref><ref id="scirp.131193-ref32"><label>32</label><mixed-citation publication-type="other" xlink:type="simple">Kaplan, W. and Littlejohn, T.G. (2001) Swiss-PDB Viewer (Deep View). Briefings in Bioinformatics, 2, 195-197. https://doi.org/10.1093/bib/2.2.195</mixed-citation></ref><ref id="scirp.131193-ref33"><label>33</label><mixed-citation publication-type="other" xlink:type="simple">Laskowski, R.A., MacArthur, M.W., Moss, D.S. and Thornton, J.M. (1993) PROCHECK: A Program to Check the Stereochemical Quality of Protein Structures. Journal of Applied Crystallography, 26, 283-291. https://doi.org/10.1107/S0021889892009944</mixed-citation></ref><ref id="scirp.131193-ref34"><label>34</label><mixed-citation publication-type="other" xlink:type="simple">Benkert, P., Biasini, M., and Schwede, T. (2011) Toward the Estimation of the Absolute Quality of Individual Protein Structure Models. Bioinformatics, 27, 343-350. https://doi.org/10.1093/bioinformatics/btq662</mixed-citation></ref><ref id="scirp.131193-ref35"><label>35</label><mixed-citation publication-type="other" xlink:type="simple">Colovos, C. and Yeates, T.O. (1993) Verification of Protein Structures: Patterns of Nonbonded Atomic Interactions. Protein Science, 2, 1511-1519. https://doi.org/10.1002/pro.5560020916</mixed-citation></ref><ref id="scirp.131193-ref36"><label>36</label><mixed-citation publication-type="other" xlink:type="simple">Wiederstein, M. and Sippl, M.J. (2007) ProSA-Web: Interactive Web Service for the Recognition of Errors in Three-Dimensional Structures of Proteins. Nucleic Acids Research, 35, W407-W410. https://doi.org/10.1093/nar/gkm290</mixed-citation></ref><ref id="scirp.131193-ref37"><label>37</label><mixed-citation publication-type="other" xlink:type="simple">Yu, C.-S., Chen, Y.-C., Lu, C.-H. and Hwang, J.-K. (2006) Prediction of Protein Subcellular Localization. Proteins, 64, 643-651. https://doi.org/10.1002/prot.21018</mixed-citation></ref><ref id="scirp.131193-ref38"><label>38</label><mixed-citation publication-type="other" xlink:type="simple">Bhasin, M., Garg, A. and Raghava, G.P.S. (2005) PSLpred: Prediction of Subcellular Localization of Bacterial Proteins. Bioinformatics, 21, 2522-2524.</mixed-citation></ref><ref id="scirp.131193-ref39"><label>39</label><mixed-citation publication-type="other" xlink:type="simple">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., et al. (2010) PSORTb 3.0: Improved Protein Subcellular Localization Prediction with Refined Localization Subcategories and Predictive Capabilities for All Prokaryotes. Bioinformatics, 26, 1608-1615. https://doi.org/10.1093/bioinformatics/btq249</mixed-citation></ref><ref id="scirp.131193-ref40"><label>40</label><mixed-citation publication-type="other" xlink:type="simple">Imai, K., Asakawa, N., Tsuji, T., Akazawa, F., Ino, A., Sonoyama, M. and Mitaku, S. (2008) SOSUI-GramN: High Performance Prediction for Sub-Cellular Localization of Proteins in Gram-Negative Bacteria. Bioinformation, 2, 417-421. https://doi.org/10.6026/97320630002417</mixed-citation></ref><ref id="scirp.131193-ref41"><label>41</label><mixed-citation publication-type="other" xlink:type="simple">Trott, O. and Olson, A.J. (2010) AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461. https://doi.org/10.1002/jcc.21334</mixed-citation></ref><ref id="scirp.131193-ref42"><label>42</label><mixed-citation publication-type="other" xlink:type="simple">Zimmermann, L., Stephens, A., Nam, S.-Z., Rau, D., Kübler, J., Lozajic, M., Gabler, F., S&amp;#246;ding, J., Lupas, A.N. and Alva, V. (2018) A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at Its Core. Journal of Molecular Biology, 430, 2237-2243. https://doi.org/10.1016/j.jmb.2017.12.007</mixed-citation></ref><ref id="scirp.131193-ref43"><label>43</label><mixed-citation publication-type="other" xlink:type="simple">Kozakov, D., Hall, D.R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D. and Vajda, S. (2017) The ClusPro Web Server for Protein-Protein Docking. Nature Protocols, 12, 255-278. https://doi.org/10.1038/nprot.2016.169</mixed-citation></ref><ref id="scirp.131193-ref44"><label>44</label><mixed-citation publication-type="other" xlink:type="simple">Zgurskaya, H.I., Krishnamoorthy, G., Ntreh, A. and Lu, S. (2011) Mechanism and Function of the Outer Membrane Channel TolC in Multidrug Resistance and Physiology of Enterobacteria. Frontiers in Microbiology, 2, Article 189. https://doi.org/10.3389/fmicb.2011.00189</mixed-citation></ref><ref id="scirp.131193-ref45"><label>45</label><mixed-citation publication-type="other" xlink:type="simple">Masi, M. and Pagès, J.-M. (2013) Structure, Function and Regulation of Outer Membrane Proteins Involved in Drug Transport in Enterobactericeae: The OmpF/C-TolC Case. TOMICROJ, 7, 22-33. https://doi.org/10.2174/1874285801307010022</mixed-citation></ref><ref id="scirp.131193-ref46"><label>46</label><mixed-citation publication-type="other" xlink:type="simple">Kumar, S. and Varela, M.F. (2012) Biochemistry of Bacterial Multidrug Efflux Pumps. International Journal of Molecular Sciences, 13, 4484-4495. https://doi.org/10.3390/ijms13044484</mixed-citation></ref></ref-list></back></article>