Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

Jan Valentin; Christian Andreetta; Wouter Krogh Boomsma; Sandro Bottaro; Jesper Ferkinghoff-Borg; Jes Frellsen; Kanti V. Mardia; Pengfei Tian; Thomas Wim Hamelryck

doi:10.1002/prot.24386

Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

Jan Valentin, Christian Andreetta, Wouter Krogh Boomsma, Sandro Bottaro, Jesper Ferkinghoff-Borg, Jes Frellsen, Kanti V. Mardia, Pengfei Tian, Thomas Wim Hamelryck

7 Citations (Scopus)

Abstract

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.

Original language	English
Journal	Proteins: Structure, Function, and Bioinformatics
Volume	82
Issue number	2
Pages (from-to)	288-299
Number of pages	12
ISSN	0887-3585
DOIs	https://doi.org/10.1002/prot.24386
Publication status	Published - Feb 2014

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@article{994c694a422647b582fdffedfbe9a48e,

title = "Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method",

abstract = "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.",

author = "Jan Valentin and Christian Andreetta and Boomsma, {Wouter Krogh} and Sandro Bottaro and Jesper Ferkinghoff-Borg and Jes Frellsen and Mardia, {Kanti V.} and Pengfei Tian and Hamelryck, {Thomas Wim}",

note = "Copyright {\textcopyright} 2013 Wiley Periodicals, Inc., a Wiley company.",

year = "2014",

month = feb,

doi = "10.1002/prot.24386",

language = "English",

volume = "82",

pages = "288--299",

journal = "Proteins: Structure, Function, and Bioinformatics",

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publisher = "JohnWiley & Sons, Inc.",

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TY - JOUR

T1 - Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

AU - Valentin, Jan

AU - Andreetta, Christian

AU - Boomsma, Wouter Krogh

AU - Bottaro, Sandro

AU - Ferkinghoff-Borg, Jesper

AU - Frellsen, Jes

AU - Mardia, Kanti V.

AU - Tian, Pengfei

AU - Hamelryck, Thomas Wim

PY - 2014/2

Y1 - 2014/2

N2 - 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.

AB - 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.

U2 - 10.1002/prot.24386

DO - 10.1002/prot.24386

M3 - Journal article

C2 - 23934827

SN - 0887-3585

VL - 82

SP - 288

EP - 299

JO - Proteins: Structure, Function, and Bioinformatics

JF - Proteins: Structure, Function, and Bioinformatics

IS - 2

ER -

Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

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