Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures

Type
Article

Authors
Iacoangeli, Alfredo
Marcatili, Paolo
Tramontano, Anna

KAUST Grant Number
KUK-I1-012-43
PRIN 20108XYHJS

Date
2015

Abstract
In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category.

Citation
Iacoangeli, A., Marcatili, P., & Tramontano, A. (2015). Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures. Journal of Chemical Theory and Computation, 11(10), 5045–5051. doi:10.1021/acs.jctc.5b00371

Acknowledgements
This work was supported by the King Abdullah University of Science and Technology (KAUST), Award Number KUK-I1-012-43 and PRIN 20108XYHJS.

Publisher
AMER CHEMICAL SOC

Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION

DOI
10.1021/acs.jctc.5b00371

Additional Links
https://pubs.acs.org/doi/10.1021/acs.jctc.5b00371

Permanent link to this record