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dc.contributor.authorBarradas Bautista, Didier
dc.contributor.authorCao, Zhen
dc.contributor.authorCavallo, Luigi
dc.contributor.authorOliva, Romina
dc.date.accessioned2020-09-22T06:36:49Z
dc.date.available2020-09-22T06:36:49Z
dc.date.issued2020-09-16
dc.date.submitted2020-06-07
dc.identifier.citationBarradas-Bautista, D., Cao, Z., Cavallo, L., & Oliva, R. (2020). The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses. BMC Bioinformatics, 21(S8). doi:10.1186/s12859-020-03600-8
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-020-03600-8
dc.identifier.urihttp://hdl.handle.net/10754/665269
dc.description.abstractAbstract Background Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process. In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis. Results As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as case studies, we illustrate here in detail the approach we adopted. Conclusions Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.
dc.description.sponsorshipThe authors wish to thank the CASP13-CAPRI organizers and assessors, as well as the structural biologists who provided the experimental structures to the experiment, for providing an independent benchmark for testing their scoring approach.
dc.publisherSpringer Nature
dc.relation.urlhttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03600-8
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleThe CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses
dc.typeArticle
dc.contributor.departmentKAUST Catalysis Center (KCC)
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentChemical Science Program
dc.identifier.journalBMC Bioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Sciences and Technologies, University of Naples “Parthenope”, Centro Direzionale - Isola C4, 80143, Naples, Italy.
dc.identifier.volume21
dc.identifier.issueS8
kaust.personBarradas Bautista, Didier
kaust.personCao, Zhen
kaust.personCavallo, Luigi
dc.date.accepted2020-06-10
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.c.5124138
refterms.dateFOA2020-09-22T06:37:18Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Dataset]</i> <br/> Barradas-Bautista, D., Cao, Z., Cavallo, L., &amp; Oliva, R. (2020). <i>The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses</i>. figshare. https://doi.org/10.6084/M9.FIGSHARE.C.5124138. DOI: <a href="https://doi.org/10.6084/m9.figshare.c.5124138" >10.6084/m9.figshare.c.5124138</a> Handle: <a href="http://hdl.handle.net/10754/665310" >10754/665310</a></a></li></ul>
dc.date.published-online2020-09-16
dc.date.published-print2020-09


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.