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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-8058773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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