GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ
© The Author(s) 2016
Received: 21 January 2016
Accepted: 6 July 2016
Published: 13 July 2016
Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target.
Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface.
The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter’s druggable intrafilament interface as a potential drug target.
Keywordsab initio modelling Homology modelling Docking FtsZ Drug design
Resistance to antibiotics has become a source of great public concern recently, compounded by the slow emergence of new antibiotics . One possible solution may be phage therapy. Bacteriophages (referred to hereafter as phages) are among the simplest and most abundant types of microorganisms , and have been used therapeutically for close to a century. With the advent of antibiotics, the use of phages therapeutically has decreased in the western world. However, as resistance to antibiotics becomes a pressing issue, phages are once again being seen as a way to combat disease [3, 4].
There are several advantages to the therapeutic use of phages; they affect highly specific targets that minimise collateral damage , they can target bacterial strains resistant to antibiotics, and they can be used to supplement the effects of antibiotics . Phages are, however, not without problems. For example, the delivery of phages is a major challenge, with the immune system presenting a large hurdle  and the use of phages as a delivery method requires accurate diagnosis of the disease-causing bacteria and therefore slows down treatment.
Study of phage-host interaction can also inform small molecule drug discovery by revealing new drug targets and pinpointing their weaknesses. Proteins in phages have naturally evolved to find effective methods to disrupt bacteria. Furthermore, in multi-protein complexes involved in processes such as cell regulation, small numbers of amino acids form hotspots which contribute most of the free energy during interactions . By studying how phage proteins disrupt the protein–protein interfaces, we can identify potential hotspots and target them when designing drugs. Such drugs would dispense with the specificity limitation of the original phage since they can be designed to target broadly conserved bacterial mechanisms potentially rendering unnecessary the diagnosis of the specific pathogenic bacterium [8, 9].
Upon infection of bacteria, phages take over the host resources through the actions of proteins expressed early in the infection. One such protein from the Escherichia coli phage T7 is gene protein (GP) 0.4. GP0.4 directly inhibits the filamenting temperature-sensitive mutant Z division protein (FtsZ) of E. coli by preventing its assembly into protofilaments both in vivo and in vitro .
FtsZ is a tubulin homologue that plays a key role in the division of bacteria cells [11–13]. Much like tubulin, purified FtsZ binds and hydrolyses GTP . GTP binding induces the FtsZ to polymerise into one of two polar protofilament conformations; straight or gently curved [11–15]. Between FtsZ monomers, an active site is formed which hydrolyses the GTP, and remains accessible to the GTP-rich cytoplasm [13, 15]. Therefore GTP binding can be rapidly restored allowing protofilaments to consist of mostly FtsZ-GTP subunits resistant to depolymerisation . The FtsZ protofilaments associate laterally to form bundles or sheets [11, 13]. As the protofilaments bundle together they form the Z ring at the site of cytokinesis. Once assembled, the Z ring plays a crucial role in recruiting downstream proteins essential for cell division [12, 13]. Therefore the inhibition of FtsZ polymerisation prevents the division of the bacteria .
The exact mechanism through which GP0.4 interacts with FtsZ is unknown. However, a mutation in FtsZ that introduced a six nucleotide insertion (TCGGCG) overcame the toxicity of GP0.4 . Here, using a suite of structural bioinformatics methods, we predict the structure of GP0.4 ab initio and determine a mode of interaction with FtsZ in accord with available data. The results suggest that the FtsZ protofilament interface is targeted in different ways by phage proteins for antimicrobial purposes or by the bacteria’s own proteins for regulatory purposes. These results add weight to the notion that this pocket is a druggable interface.
BLAST  was used with the GP0.4 and FtsZ sequences to identify homologous proteins in the UniProt database  and to search for any homologues already structurally characterised in the Protein Data Bank (PDB) . The HHPred server was used to confirm that the structure of GP0.4 could not be inferred by distant homology to any known structure.
GP0.4 ab initio modelling
To elucidate a structure for GP0.4, ROSETTA 3.5 AbinitioRelax modelling [19–24] was used. The AbinitioRelax application consists of two steps; the first step is a coarse-grained fragment-based search for conformation that uses a score function which favours protein-like features (Abinitio). The second step is an all atom refinement using the Rosetta full-atom force field (Relax). The Robetta server [22, 25] was used to generate the required fragments and the PSIPRED  secondary structure predictions used by ROSETTA.
Using ROSETTA, 10,000 ab initio models (or decoys) were produced for GP0.4 and clustered using default protocols on a Linux workstation. Representatives of the largest ten resulting clusters were considered as candidate fold predictions. This process was repeated for four homologues identified in the BLAST search. In addition to ROSETTA, GP0.4 and each homologous sequence was submitted to the QUARK ab initio server . QUARK is an ab initio modelling method conceptually similar to ROSETTA. The server yielded a further ten models (representatives of the ten largest clusters) for each of the proteins.
Validation and comparison of GP0.4 models
GROMACS 5.0.1 molecular dynamics [28–32] was used to test the stability of the models over a period of 5 ns using a cubic box filled with water as the solvent, chloride as the counterions, and the AMBER99SB-ILDN force field . ProSA [34, 35] and QMEAN [36–39] were used to obtain protein structure quality measurements.
The top ten models produced for each protein by ROSETTA and QUARK were submitted to the ProCKSI comparative server  to assess any structural similarity between them by producing a matrix of template modelling (TM) scores . This matrix was visualised using CLuster ANalysis of Sequences (CLANS)  to cluster the models with an attraction value of >0.6.
FtsZ comparative modelling
ROSETTA comparative modelling (RosettaCM)  was used to make ten models of E. coli FtsZ based on ten homologues (2VAW, 2VXY, 4M8I, 1RQ2, 1OFU, 1W5F, 2VAP, 2R75, 3J4S and 4B45) obtained from HHPred [43–45]. RosettaCM optimises an all-atom energy function over the conformational space defined by homologue structures to produce models with more accurate side chain and backbone conformations than previously available. In order to select the best model produced, the inbuilt RosettaCM scoring system was used in combination with the model quality assessment program ProSA [34, 35].
Four servers that performed well in the most recent CAPRI (Critical Assessment of PRediction of Interactions) competition  were used to predict how models of FtsZ and GP0.4 might interact—ClusPro [47–50], Swarmdock [51–53], Dock/PIERR  and GRAMM-X .
The final putative GP0.4 binding site was assessed for drugability with two complementary servers (DoGsiteScorer  and FTMap [57–59]) and its conservation between FtsZ sequences quantified with ConSurf [60–63].
Results and discussion
ab initio modelling of GP0.4
RosettaCM  was used to produce ten models of E.coli FtsZ using homologous structures from P. aeruginosa (1ofu, 2vaw), M. tuberculosis (1rq2), T. maritima (1w5f), A. aeolicus (2r75), M. jannaschii (2vap), B.subtilis (2vxy), B. thuringiensis (3j4 s), H. volcanii (4b45) and S. epidermidis (4m8i) as templates. FtsZ has a well conserved core domain followed by a variable C-terminal domain (Additional file 1: Figure S1). Indeed the ten models produced were very similar apart from that variable C-terminal domain which adopted a wide variety of poorly packed conformations. Previous studies of FtsZ–FtsZ interaction found that the C-terminal domain was not required for the assembly of protofilaments, but was essential for interaction with other membrane associated cell division proteins . Since our focus here was on the inter-subunit interface of the protofilament targeted by GP0.4, the C-terminal region was eliminated and RosettaCM energy scores and ProSA [34, 35] scores were used to identify the most favoured model of the core domain.
Mutant Ftsz modelling
Interestingly, MciZ, a developmental regulator of bacterial cell division was found to inhibit FtsZ polymerisation by targeting the same region of the intrafilament FtsZ interface as GP0.4 whilst binding to the opposite face of the FtsZ monomer , as shown in Fig. 6.
Characteristics of the predicted GP0.4 binding site
In order to assess whether the druggable predicted GP0.4 binding site is conserved among bacteria, we mapped sequence conservation of 150 FtsZ sequences onto the FtsZ structure using the ConSurf server. Figure 8 shows that the area the ‘U’ model bound to was well conserved. This provides evidence that a GP0.4 like protein or small molecule targeted to its binding site might be effective against FtsZ in other bacteria including pathogenic species: indeed, GP0.4 homologues were identified in phages for Yersinia and Salmonella supporting this idea.
The FtsZ filament interface targeted by GP0.4 has previously been highlighted as a possible target for small molecule targeting. However, there are concerns over targeting the GTP-binding site due to a risk of poisoning the eukaryotic homologue, tubulin . Nevertheless, our results emphasise the druggable nature of a larger interface including the pocket targeted by GP0.4 They therefore encourage further efforts at exploiting the interface for small molecule drug design as well as offering possible routes forward for peptidomimetic inhibition.
Characterising the targets and inhibitory mechanisms of phage proteins is a valuable route to the discovery and validation of new potential drug targets. Here we bring a range of structural bioinformatics methods to bear on the interaction between phage GP0.4 and its bacterial target FtsZ. We provide evidence that GP0.4 adopts a ‘U’ shaped conformation that inserts into a cleft between helices 1, 5 and 7 on FtsZ. The hypothesis is strongly supported by data obtained for a GP0.4-resistant FtsZ mutation . The presence of GP0.4 bound to this region, as shown in Fig. 6, would physically interfere with assembly of the FtsZ filament. The importance of this FtsZ–FtsZ interface was further demonstrated by MciZ, a regulatory protein that binds to the opposite side of the interface to inhibit bacterial cell division. The druggable nature of the broad intrafilament FtsZ interface should encourage future drug design.
AJS carried out the experiments. DJR conceived and supervised the work. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Availability of data and material
Modelled structures are available on request from the authors.
Consent for publication
Ethics approval and consent to participate
None to report.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Oyston PCF, Fox MA, Richards SJ, Clark GC. Novel peptide therapeutics for treatment of infections. J Med Microbiol. 2009;58(Pt 8):977–87.View ArticlePubMedGoogle Scholar
- Labrie SJ, Samson JE, Moineau S. Bacteriophage resistance mechanisms. Nat Rev Microbiol. 2010;8:317–27.View ArticlePubMedGoogle Scholar
- Lu TK, Koeris MS. The next generation of bacteriophage therapy. Curr Opin Microbiol. 2011;14:524–31.View ArticlePubMedGoogle Scholar
- Pirnay J-P, De Vos D, Verbeken G, Merabishvili M, Chanishvili N, Vaneechoutte M, Zizi M, Laire G, Lavigne R, Huys I, Van den Mooter G, Buckling A, Debarbieux L, Pouillot F, Azeredo J, Kutter E, Dublanchet A, Górski A, Adamia R. The phage therapy paradigm: prêt-à-porter or sur-mesure? Pharm Res. 2011;28:934–7.View ArticlePubMedGoogle Scholar
- Keen EC. Phage therapy: concept to cure. Front Microbiol. 2012;3:238.View ArticlePubMedPubMed CentralGoogle Scholar
- Kutateladze M, Adamia R. Bacteriophages as potential new therapeutics to replace or supplement antibiotics. Trends Biotechnol. 2010;28:591–5.View ArticlePubMedGoogle Scholar
- Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces. J Mol Biol. 1998;280:1–9.View ArticlePubMedGoogle Scholar
- Fischetti VA, Nelson D, Schuch R. Reinventing phage therapy: are the parts greater than the sum? Nat Biotechnol. 2006;24:1508–11.View ArticlePubMedGoogle Scholar
- Projan S. Phage-inspired antibiotics? Nat Biotechnol. 2004;22:167–8.View ArticlePubMedGoogle Scholar
- Kiro R, Molshanski-Mor S, Yosef I, Milam SL, Erickson HP, Qimron U. Gene product 0.4 increases bacteriophage T7 competitiveness by inhibiting host cell division. Proc Natl Acad Sci USA. 2013;110:19549–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Erickson HP, Taylor DW, Taylor KA, Bramhill D. Bacterial cell division protein FtsZ assembles into protofilament sheets and minirings, structural homologs of tubulin polymers. Proc Natl Acad Sci. 1996;93:519–23.View ArticlePubMedPubMed CentralGoogle Scholar
- Loose M, Mitchison TJ. The bacterial cell division proteins FtsA and FtsZ self-organize into dynamic cytoskeletal patterns. Nat Cell Biol. 2014;16:38–46.View ArticlePubMedGoogle Scholar
- Margolin W. FtsZ and the division of prokaryotic cells and organelles. Nat Rev Mol Cell Biol. 2005;6:862–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Lu C, Reedy M, Erickson HP. Straight and curved conformations of FtsZ are regulated by GTP hydrolysis. J Bacteriol. 2000;182:164–70.View ArticlePubMedPubMed CentralGoogle Scholar
- Mingorance J, Rivas G, Vélez M, Gómez-Puertas P, Vicente M. Strong FtsZ is with the force: mechanisms to constrict bacteria. Trends Microbiol. 2010;18:348–56.View ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.View ArticlePubMedPubMed CentralGoogle Scholar
- UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:204–12.View ArticleGoogle Scholar
- Berman HM. The protein data bank. Nucleic Acids Res. 2000;28:235–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Bonneau R, Tsai J, Ruczinski I, Chivian D, Rohl C, Strauss CE, Baker D. Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins. 2001;5:119–26.View ArticlePubMedGoogle Scholar
- Bonneau R, Strauss CEM, Rohl CA, Chivian D, Bradley P, Malmström L, Robertson T, Baker D. De novo prediction of three-dimensional structures for major protein families. J Mol Biol. 2002;322:65–78.View ArticlePubMedGoogle Scholar
- Bradley P, Misura KMS, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science. 2005;309:1868–71.View ArticlePubMedGoogle Scholar
- Raman S, Vernon R, Thompson J, Tyka M, Sadreyev R, Pei J, Kim D, Kellogg E, DiMaio F, Lange O, Kinch L, Sheffler W, Kim B-H, Das R, Grishin NV, Baker D. Structure prediction for CASP8 with all-atom refinement using Rosetta. Proteins. 2009;77(Suppl 9):89–99.View ArticlePubMedPubMed CentralGoogle Scholar
- Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol. 1997;268:209–25.View ArticlePubMedGoogle Scholar
- Simons KT, Ruczinski I, Kooperberg C, Fox BA, Bystroff C, Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins. 1999;34:82–95.View ArticlePubMedGoogle Scholar
- Song Y, DiMaio F, Wang RYR, Kim D, Miles C, Brunette T, Thompson J, Baker D. High-resolution comparative modeling with RosettaCM. Structure. 2013;21:1735–42.View ArticlePubMedGoogle Scholar
- Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol. 1999;292:195–202.View ArticlePubMedGoogle Scholar
- Xu D, Zhang Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins. 2012;80:1715–35.View ArticlePubMedPubMed CentralGoogle Scholar
- Berendsen HJC, van der Spoel D, van Drunen R. GROMACS: a message-passing parallel molecular dynamics implementation. Comp Phys Comm. 1995;91:43–56.View ArticleGoogle Scholar
- Hess B, Kutzner C, van der Spoel D, Lindahl E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008;4:435–47.View ArticlePubMedGoogle Scholar
- Lindahl E, Hess B, van der Spoel D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model. 2001;7(8):306–17.Google Scholar
- Pandini A, Fornili A, Fraternali F, Kleinjung J. GSATools: analysis of allosteric communication and functional local motions using a structural alphabet. Bioinformatics. 2013;29:2053–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: fast, flexible, and free. J Comput Chem. 2005;26:1701–18.View ArticleGoogle Scholar
- Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins. 2010;78:1950–8.PubMedPubMed CentralGoogle Scholar
- Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:407–10.View ArticleGoogle Scholar
- Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins. 1993;17:355–62.View ArticlePubMedGoogle Scholar
- Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011;27:343–50.View ArticlePubMedGoogle Scholar
- Benkert P, Tosatto SCE, Schomburg D. QMEAN: a comprehensive scoring function for model quality assessment. Proteins. 2008;71:261–77.View ArticlePubMedGoogle Scholar
- Benkert P, Künzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009;37:510–4.View ArticleGoogle Scholar
- Benkert P, Schwede T, Tosatto SC. QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information. BMC Struct Biol. 2009;9:35.View ArticlePubMedPubMed CentralGoogle Scholar
- Barthel D, Hirst JD, Błazewicz J, Burke EK, Krasnogor N. ProCKSI: a decision support system for protein (structure) comparison, knowledge, similarity and information. BMC Bioinformatics. 2007;8:416.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005;33:2302–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Frickey T, Lupas A. CLANS: a Java application for visualizing protein families based on pairwise similarity. Bioinformatics. 2004;20:3702–4.View ArticlePubMedGoogle Scholar
- Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods. 2012;9:173–5.View ArticleGoogle Scholar
- Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2005;33:244–8.View ArticleGoogle Scholar
- Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005;21:951–60.View ArticlePubMedGoogle Scholar
- Lensink MF, Wodak SJ. Docking, scoring, and affinity prediction in CAPRI. Proteins. 2013;81:2082–95.View ArticlePubMedGoogle Scholar
- Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics. 2004;20:45–50.View ArticlePubMedGoogle Scholar
- Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res. 2004;32:96–9.View ArticleGoogle Scholar
- Kozakov D, Brenke R, Comeau SR, Vajda S. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins. 2006;65:392–406.View ArticlePubMedGoogle Scholar
- Kozakov D, Beglov D, Bohnuud T, Mottarella SE, Xia B, Hall DR, Vajda S. How good is automated protein docking? Proteins. 2013;81:2159–66.View ArticlePubMedPubMed CentralGoogle Scholar
- Torchala M, Moal IH, Chaleil RAG, Agius R, Bates PA. A Markov-chain model description of binding funnels to enhance the ranking of docked solutions. Proteins. 2013;81:2143–9.View ArticlePubMedGoogle Scholar
- Torchala M, Bates PA. Predicting the structure of protein-protein complexes using the SwarmDock Web Server. Methods Mol Biol. 2014;1137:181–97.View ArticlePubMedGoogle Scholar
- Torchala M, Moal IH, Chaleil RAG, Fernandez-Recio J, Bates PA. SwarmDock: a server for flexible protein-protein docking. Bioinformatics. 2013;29:807–9.View ArticlePubMedGoogle Scholar
- Viswanath C, Ravikant DVS, Elber R. DOCK/PIERR: web server for structure prediction of protein-protein complexes. Methods Mol Biol. 2014;1137:199–207.View ArticlePubMedGoogle Scholar
- Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein–protein docking. Nucleic Acids Res. 2006;34(2):W310–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Volkamer A, Kuhn D, Grombacher T, Rippmann F, Rarey M. Combining global and local measures for structure-based druggability predictions. J Chem Inf Model. 2012;52:360–72.View ArticlePubMedGoogle Scholar
- Brenke R, Kozakov D, Chuang G-Y, Beglov D, Hall D, Landon MR, Mattos C, Vajda S. Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics. 2009;25:621–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Kozakov D, Hall DR, Chuang G-Y, Cencic R, Brenke R, Grove LE, Beglov D, Pelletier J, Whitty A, Vajda S. Structural conservation of druggable hot spots in protein-protein interfaces. Proc Natl Acad Sci USA. 2011;108:13528–33.View ArticlePubMedPubMed CentralGoogle Scholar
- Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE, Luo L, Xia B, Beglov D, Vajda S. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc. 2015;10:733–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res. 2010;38:529–33.View ArticleGoogle Scholar
- Celniker G, Nimrod G, Ashkenazy H, Glaser F, Martz E, Mayrose I, Pupko T, Ben-Tal N. ConSurf: using evolutionary data to raise testable hypotheses about protein function. Isr J Chem. 2013;53:199–206.View ArticleGoogle Scholar
- Glaser F, Pupko T, Paz I, Bell RE, Bechor-Shental D, Martz E, Ben-Tal N. ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics. 2003;19:163–4.View ArticlePubMedGoogle Scholar
- Landau M, Mayrose I, Rosenberg Y, Glaser F, Martz E, Pupko T, Ben-Tal N. ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res. 2005;33:299–302.View ArticleGoogle Scholar
- Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113.View ArticleGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. Jalview version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25:1189–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferrè F, Clote P. DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res. 2005;33:230–2.View ArticleGoogle Scholar
- Krissinel E, Henrick K. Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr. 2004;60(12):2256–68.View ArticlePubMedGoogle Scholar
- Holm L, Rosenström P. Dali server: conservation mapping in 3D. Nucleic Acids Res. 2010;38:545–9.View ArticleGoogle Scholar
- Bisson-Filho AW, Discola KF, Castellen P, Blasios V, Martins A, Sforça ML, Garcia W, Zeri ACM, Erickson HP, Dessen A, Gueiros-Filho FJ. FtsZ filament capping by MciZ, a developmental regulator of bacterial division. Proc Natl Acad Sci USA. 2015;112:E2130–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Schaffner-Barbero C, Martín-Fontecha M, Chacón P, Andreu JM. Targeting the assembly of bacterial cell division protein FtsZ with small molecules. ACS Chem Biol. 2012;7:269–77.View ArticlePubMedGoogle Scholar