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Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method
Author(s) -
Valentin Jan B.,
Andreetta Christian,
Boomsma Wouter,
Bottaro Sandro,
FerkinghoffBorg Jesper,
Frellsen Jes,
Mardia Kanti V.,
Tian Pengfei,
Hamelryck Thomas
Publication year - 2014
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24386
Subject(s) - probabilistic logic , dihedral angle , generality , protein structure prediction , computer science , force field (fiction) , statistical model , protein structure , protein secondary structure , algorithm , mathematics , statistical physics , artificial intelligence , physics , psychology , hydrogen bond , molecule , psychotherapist , nuclear magnetic resonance , quantum mechanics
We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length scale, which concern the dihedral angles in main chain and side chains, respectively. Conceptually, this constitutes a probabilistic and continuous alternative to the use of discrete fragment and rotamer libraries. The local model is combined with a nonlocal model that involves a small number of energy terms according to a physical force field, and some information on the overall secondary structure content. In this initial study we focus on the formulation of the joint model and the evaluation of the use of an energy vector as a descriptor of a protein's nonlocal structure; hence, we derive the parameters of the nonlocal model from the native structure without loss of generality. The local and nonlocal models are combined using the reference ratio method, which is a well‐justified probabilistic construction. For evaluation, we use the resulting joint models to predict the structure of four proteins. The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. Proteins 2014; 82:288–299. © 2013 Wiley Periodicals, Inc.