Flexibility is critical for a folded protein to bind to other molecules (ligands) and achieve its functions. The conformational selection theory suggests that a folded protein deforms continuously and its ligand selects the most favorable conformations to bind to. Therefore, one of the best options to study protein-ligand binding is to sample conformations broadly distributed over the protein-folded state. This article presents a new sampler, called kino-geometric sampler (KGS). This sampler encodes dominant energy terms implicitly by simple kinematic and geometric constraints. Two key technical contributions of KGS are (1) a robotics-inspired Jacobian-based method to simultaneously deform a large number of interdependent kinematic cycles without any significant break-up of the closure constraints, and (2) a diffusive strategy to generate conformation distributions that diffuse quickly throughout the protein folded state. Experiments on four very different test proteins demonstrate that KGS can efficiently compute distributions containing conformations close to target (e.g., functional) conformations. These targets are not given to KGS, hence are not used to bias the sampling process. In particular, for a lysine-binding protein, KGS was able to sample conformations in both the intermediate and functional states without the ligand, while previous work using molecular dynamics simulation had required the ligand to be taken into account in the potential function. Overall, KGS demonstrates that kino-geometric constraints characterize the folded subset of a protein conformation space and that this subset is small enough to be approximated by a relatively small distribution of conformations. © 2011 Wiley Periodicals, Inc.
|Original language||English (US)|
|Number of pages||19|
|Journal||Proteins: Structure, Function, and Bioinformatics|
|State||Published - Oct 4 2011|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Grant sponsor: NSF; Grant number: DMS-0443939; Grant sponsor: NSF Postdoctoral CIFellowship ( Computing Research Association); Grant number: 0937060; Grant sponsor: Academic Excellence Alliance Program ( King Abdullah University of Science & Technology); Grant number: Stanford University, Bio-X fellowship, BMI program ( Stanford University)
This publication acknowledges KAUST support, but has no KAUST affiliated authors.