UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes
Motivation Single protein residue mutations may reshape the binding affinity of protein¿protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein¿protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein¿protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.
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Oxford University Press
2021-02-01
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Online Access: | http://hdl.handle.net/10261/262444 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100004316 http://dx.doi.org/10.13039/501100002809 |
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dig-icvv-es-10261-2624442022-03-15T03:02:27Z UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes Amengual-Rigo, Pep Fernández-Recio, Juan Guallar, Victor Ministerio de Economía y Competitividad (España) Generalitat de Catalunya Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) European Commission IBM Centro de Supercomputación de Cataluña Motivation Single protein residue mutations may reshape the binding affinity of protein¿protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein¿protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein¿protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online. This work was supported by a predoctoral fellowship from the Government of Catalonia (2018FI_B_00873 to P.A.-R.) and grants from the Spanish government ‘Programa Estatal I+D+i’ (BIO2016-79930-R), (PID2019-110167RB-I00) and (CTQ2016-79138-R), and by grant PIREPRED from the EU European Regional Development Fund program Interreg V-A Spain-France-Andorra (POCTEFA). This work was also received funding from the IBM-BSC Deep Learning Center (2016). Peer reviewed 2022-03-02T08:00:13Z 2022-03-02T08:00:13Z 2021-02-01 2022-03-02T08:00:13Z artículo http://purl.org/coar/resource_type/c_6501 doi: 10.1093/bioinformatics/btaa708 issn: 1367-4803 e-issn: 1460-2059 Bioinformatics 37(3): 334-341 (2021) http://hdl.handle.net/10261/262444 10.1093/bioinformatics/btaa708 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100004316 http://dx.doi.org/10.13039/501100002809 #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO//BIO2016-79930-R info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110167RB-I00/ES/NUEVA METODOLOGIA DE DOCKING ENTRE PROTEINAS PARA LA INTERPRETACION DE VARIANTES GENETICAS DE RELEVANCIA PARA LA SALUD HUMANA/ info:eu-repo/grantAgreement/MINECO//CTQ2016-79138-R Postprint http://dx.doi.org/10.1093/bioinformatics/btaa708 Sí open Oxford University Press |
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Motivation
Single protein residue mutations may reshape the binding affinity of protein¿protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein¿protein complexes trained on interactome data.
Results
Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein¿protein mutations using UEP, a fast and reliable open-source code.
Availability and implementation
UEP algorithm can be found at: https://github.com/pepamengual/UEP.
Supplementary information
Supplementary data are available at Bioinformatics online. |
author2 |
Ministerio de Economía y Competitividad (España) |
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Ministerio de Economía y Competitividad (España) Amengual-Rigo, Pep Fernández-Recio, Juan Guallar, Victor |
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artículo |
author |
Amengual-Rigo, Pep Fernández-Recio, Juan Guallar, Victor |
spellingShingle |
Amengual-Rigo, Pep Fernández-Recio, Juan Guallar, Victor UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
author_sort |
Amengual-Rigo, Pep |
title |
UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
title_short |
UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
title_full |
UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
title_fullStr |
UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
title_full_unstemmed |
UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
title_sort |
uep: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes |
publisher |
Oxford University Press |
publishDate |
2021-02-01 |
url |
http://hdl.handle.net/10261/262444 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100004316 http://dx.doi.org/10.13039/501100002809 |
work_keys_str_mv |
AT amengualrigopep uepanopensourceandfastclassifierforpredictingtheimpactofmutationsinproteinproteincomplexes AT fernandezreciojuan uepanopensourceandfastclassifierforpredictingtheimpactofmutationsinproteinproteincomplexes AT guallarvictor uepanopensourceandfastclassifierforpredictingtheimpactofmutationsinproteinproteincomplexes |
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1777671023330590720 |