Protein conformational landscapes: energy minimization and clustering of a long molecular dynamics trajectory.
Using energy minimization and cluster analysis, we have analyzed a 1020 ps molecular dynamics trajectory of solvated bovine pancreatic trypsin inhibitor. Elucidation of conformational substates in this way both illustrates the degree of conformational convergence in the simulation and reduces the structural data to a tractable subset. The relative movement of structures upon energy minimization was used to estimate the sizes of features on the protein potential energy surface. The structures were analyzed using their pairwise root-mean-square C alpha deviations, which gave a global measure of conformational changes that would not be apparent by monitoring single degrees of freedom. At time scales of 0.1 ps, energy minimization detected sharp transitions between energy minima separated by 0.1 A rms deviation. Larger conformational clusters containing these smaller minima and separated by 0.25 A were seen at 1 ps time scales. Both of these small features of the conformational landscape were characterized by movements in loop regions associated with small, correlated backbone dihedral angle shifts. On a nanosecond time scale, the main features of the protein energy landscape were clusters separated by over 0.7 A rms deviation, with only seven of these substates visited over the 1 ns trajectory. These substrates, discernible both before and after energy minimization, differ mainly in a monotonic pivot of the loop residues 11-18 over the course of the simulation. This loop contains lysine 17, which specifically binds to trypsin in the active site. The trajectory did not return to previously visited clusters, indicating that this trajectory has not been shown to have completely sampled the conformational substates available to it. Because the apparent convergence to a single region of conformation space depends on both the time scale of observation and the size of the conformational features examined, convergence must be operationally defined within the context of the simulation.
Troyer
J M
JM
Department of Pharmaceutical Chemistry, University of California, San Francisco 94143-0450, USA.
Cohen
F E
FE
D000818:Animals; D007611:Aprotinin / Q000737:chemistry* / Q000378:metabolism; D002417:Cattle; D016000:Cluster Analysis; D004735:Energy Transfer; D008958:Models, Molecular; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D013379:Substrate Specificity
D007611:Aprotinin