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

Journal Article


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.


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.


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.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Hauser, K

Published Date

  • January 2013

Published In

Volume / Issue

  • 13 Suppl 1 /

Start / End Page

  • S9 -

PubMed ID

  • 24565175

Pubmed Central ID

  • 24565175

Electronic International Standard Serial Number (EISSN)

  • 1472-6807

International Standard Serial Number (ISSN)

  • 1472-6807

Digital Object Identifier (DOI)

  • 10.1186/1472-6807-13-s1-s9


  • eng