Email updates

Keep up to date with the latest news and content from BMC Structural Biology and BioMed Central.

This article is part of the supplement: Selected articles from the Computational Structural Bioinformatics Workshop 2012

Open Access Research

Unbiased, scalable sampling of protein loop conformations from probabilistic priors

Yajia Zhang* and Kris Hauser

Author Affiliations

School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA

For all author emails, please log on.

BMC Structural Biology 2013, 13(Suppl 1):S9  doi:10.1186/1472-6807-13-S1-S9

Published: 8 November 2013

Abstract

Background

Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences.

Results

Our simulation experiments demonstrate that the method computes high-scoring conformations of large loops (>10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints.

Conclusion

Numerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.

Keywords:
Conformation sampling; Monte Carlo methods; protein loops; ensemble generation; graphical models