A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2009

A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation

Abstract

Knowledge-based protein folding potentials have proven successful in the recent years. Based on statistics of observed interatomic distances, they generally encode pairwise contact information. In this study we present a method that derives multi-body contact potentials from measurements of surface areas using coarse-grained protein models. The measurements are made using a newly implemented geometric construction: the arrangement of circles on a sphere. This construction allows the definition of residue covering areas which are used as parameters to build functions able to distinguish native structures from decoys. These functions, encoding up to 5-body contacts are evaluated on a reference set of 66 structures and its 45000 decoys, and also on the often used lattice ssfit set from the decoys'R us database. We show that the most relevant information for discrimination resides in 2- and 3-body contacts. The potentials we have obtained can be used for evaluation of putative structural models; they could also lead to different types of structure refinement techniques that use multi-body interactions.
Fichier principal
Vignette du fichier
2009_JOBIM.pdf (221.86 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00429607 , version 1 (03-11-2009)

Identifiers

  • HAL Id : inria-00429607 , version 1

Cite

Sebastien Loriot, Frédéric Cazals, Michael Levitt, Julie Bernauer. A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Société Française de Bioinformatique, Jul 2009, Nantes, France. ⟨inria-00429607⟩
985 View
105 Download

Share

Gmail Facebook X LinkedIn More