Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches

Abstract The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual infor...

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Autores principales:	Camila Pontes, Miguel Andrade, José Fiorote, Werner Treptow
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b166faf4fe6a45e08cccaab513bd19d5
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spelling	oai:doaj.org-article:b166faf4fe6a45e08cccaab513bd19d52021-12-02T17:04:36ZTrivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches10.1038/s41598-021-86455-02045-2322https://doaj.org/article/b166faf4fe6a45e08cccaab513bd19d52021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86455-0https://doaj.org/toc/2045-2322Abstract The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%—far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200).Camila PontesMiguel AndradeJosé FioroteWerner TreptowNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Camila Pontes Miguel Andrade José Fiorote Werner Treptow Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
description	Abstract The problem of finding the correct set of partners for a given pair of interacting protein families based on multi-sequence alignments (MSAs) has received great attention over the years. Recently, the native contacts of two interacting proteins were shown to store the strongest mutual information (MI) signal to discriminate MSA concatenations with the largest fraction of correct pairings. Although that signal might be of practical relevance in the search for an effective heuristic to solve the problem, the number of MSA concatenations with near-native MI is large, imposing severe limitations. Here, a Genetic Algorithm that explores possible MSA concatenations according to a MI maximization criteria is shown to find degenerate solutions with two error sources, arising from mismatches among (i) similar and (ii) non-similar sequences. If mistakes made among similar sequences are disregarded, type-(i) solutions are found to resolve correct pairings at best true positive (TP) rates of 70%—far above the very same estimates in type-(ii) solutions. A machine learning classification algorithm helps to show further that differences between optimized solutions based on TP rates are not artificial and may have biological meaning associated with the three-dimensional distribution of the MI signal. Type-(i) solutions may therefore correspond to reliable results for predictive purposes, found here to be more likely obtained via MI maximization across protein systems having a minimum critical number of amino acid contacts on their interaction surfaces (N > 200).
format	article
author	Camila Pontes Miguel Andrade José Fiorote Werner Treptow
author_facet	Camila Pontes Miguel Andrade José Fiorote Werner Treptow
author_sort	Camila Pontes
title	Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
title_short	Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
title_full	Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
title_fullStr	Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
title_full_unstemmed	Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
title_sort	trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b166faf4fe6a45e08cccaab513bd19d5
work_keys_str_mv	AT camilapontes trivialandnontrivialerrorsourcesaccountformisidentificationofproteinpartnersinmutualinformationapproaches AT miguelandrade trivialandnontrivialerrorsourcesaccountformisidentificationofproteinpartnersinmutualinformationapproaches AT josefiorote trivialandnontrivialerrorsourcesaccountformisidentificationofproteinpartnersinmutualinformationapproaches AT wernertreptow trivialandnontrivialerrorsourcesaccountformisidentificationofproteinpartnersinmutualinformationapproaches
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Trivial and nontrivial error sources account for misidentification of protein partners in mutual information approaches

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