Unbiased, scalable sampling of protein loop conformations from probabilistic priors.

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 re

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC3953323 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature