About: An information gain-based approach for evaluating protein structure models

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	An information gain-based approach for evaluating protein structure models
Creator	Janel, Nathalie Moroy, Gautier Postic, Guillaume Tufféry, Pierre
source	Elsevier; Medline; PMC
abstract	For three decades now, knowledge-based scoring functions that operate through the “potential of mean force” (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name—i.e. PMFs obtained by molecular dynamics simulations—their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes’ theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.
has issue date	2020-08-18(xsd:dateTime)
bibo:doi	10.1016/j.csbj.2020.08.013
bibo:pmid	32837711
has license	no-cc
sha1sum (hex)	67a55a420f2238d2b0a0e532eda3236c3c60b2d5
schema:url	https://doi.org/10.1016/j.csbj.2020.08.013
resource representing a document's title	An information gain-based approach for evaluating protein structure models
has PubMed Central identifier	PMC7431362
has PubMed identifier	32837711
schema:publication	Comput Struct Biotechnol J
resource representing a document's body	covid:67a55a420f2238d2b0a0e532eda3236c3c60b2d5#body_text
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