Literature DB >> 26055539

ICOSA: A Distance-Dependent, Orientation-Specific Coarse-Grained Contact Potential for Protein Structure Modeling.

Wessam Elhefnawy1, Lin Chen1, Yun Han1, Yaohang Li2.   

Abstract

The relative distance and orientation in contacting residue pairs plays a significant role in protein folding and stabilization. We hereby devise a new knowledge-based, coarse-grained contact potential, so-called ICOSA, by correlating inter-residue contact distance and orientation in evaluating pair-wise inter-residue interactions. The rationale of our approach is to establish icosahedral local coordinates to estimate the statistical residue contact distributions in all spherical triangular shells within a sphere. We extend the theory of finite ideal gas reference state to icosahedral local coordinates. ICOSA incorporates long-range contact interactions, which is critical to ICOSA sensitivity and is justified in statistical rigor. With only backbone atoms information, ICOSA is at least comparable to all-atom, fine-grained potentials such as Rosetta, DFIRE, I-TASSER, and OPUS in discriminating near-natives from misfold protein conformations in the Rosetta and I-TASSER protein decoy sets. ICOSA also outperforms a set of widely used coarse-grained potentials and is comparable to all-atom, fine-grained potentials in identifying CASP10 models.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  contact orientation and distance; finite ideal gas reference state; icosahedrons; inter-residue contact; knowledge-based potential

Mesh:

Substances:

Year:  2015        PMID: 26055539     DOI: 10.1016/j.jmb.2015.05.022

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  2 in total

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Authors:  José Ramón López-Blanco; Alejandro Jesús Canosa-Valls; Yaohang Li; Pablo Chacón
Journal:  Nucleic Acids Res       Date:  2016-05-05       Impact factor: 16.971

2.  Identification of native protein structures captured by principal interactions.

Authors:  Mehdi Mirzaie
Journal:  BMC Bioinformatics       Date:  2019-11-21       Impact factor: 3.169

  2 in total

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