title: Sampling-based exploration of folded state of a protein under kinematic and geometric constraints.
abstract: 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.
authors.affiliation: Biomedical Informatics Program, Department of Computer Science, Stanford University, Stanford, California 94305-9010, USA. peggyyao@ai.stanford.edu
authors.forename: Peggy
authors.initials: P
authors.lastname: Yao
authors.forename: Liangjun
authors.initials: L
authors.lastname: Zhang
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Latombe
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry; D002352:Carrier Proteins / Q000737:chemistry; D003198:Computer Simulation; D002373:Cyclic AMP Receptor Protein / Q000737:chemistry; D029968:Escherichia coli Proteins / Q000737:chemistry; D006860:Hydrogen Bonding; D008565:Membrane Proteins / Q000737:chemistry; D008958:Models, Molecular; D010675:Pheromones / Q000737:chemistry; D011485:Protein Binding; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D015800:Protozoan Proteins / Q000737:chemistry; D012984:Software; D013816:Thermodynamics
chemical_list: D001426:Bacterial Proteins; D002352:Carrier Proteins; D002373:Cyclic AMP Receptor Protein; D029968:Escherichia coli Proteins; D008565:Membrane Proteins; D010675:Pheromones; D015800:Protozoan Proteins; C033849:lysyl-arginyl-ornithine-binding protein, bacteria; C057783:mating pheromone Er-1, Euplotes raikovi; C107733:cyanovirin N