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ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques

MOTIVATION: Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure...

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Autores principales: Toth, Joshua M, DePietro, Paul J, Haas, Juergen, McLaughlin, William A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058773/
https://www.ncbi.nlm.nih.gov/pubmed/32780798
http://dx.doi.org/10.1093/bioinformatics/btaa712
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author Toth, Joshua M
DePietro, Paul J
Haas, Juergen
McLaughlin, William A
author_facet Toth, Joshua M
DePietro, Paul J
Haas, Juergen
McLaughlin, William A
author_sort Toth, Joshua M
collection PubMed
description MOTIVATION: Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. RESULTS: Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. AVAILABILITYAND IMPLEMENTATION: Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-80587732021-04-28 ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques Toth, Joshua M DePietro, Paul J Haas, Juergen McLaughlin, William A Bioinformatics Original Papers MOTIVATION: Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. RESULTS: Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. AVAILABILITYAND IMPLEMENTATION: Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-08-11 /pmc/articles/PMC8058773/ /pubmed/32780798 http://dx.doi.org/10.1093/bioinformatics/btaa712 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Toth, Joshua M
DePietro, Paul J
Haas, Juergen
McLaughlin, William A
ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title_full ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title_fullStr ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title_full_unstemmed ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title_short ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
title_sort resirole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058773/
https://www.ncbi.nlm.nih.gov/pubmed/32780798
http://dx.doi.org/10.1093/bioinformatics/btaa712
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