Scaling Up Robotics-Inspired Conformational Sampling Algorithms

[摘要] The ability to efficiently sample a protein’s conformational space allows one to understand how a protein may interact with different partners. Algorithms from sampling-based robot motion planning have been used for conformational sampling of small-sized systems. These algorithms keep track of ;;coverage” in conformational space based on what has been sampled and aim to intelligently perturb the protein’s degrees of freedom to bias search in less densely explored areas of conformational space. However, these algorithms were not designed for large proteins or complexes. These algorithms depend heavily on defining useful perturbation strategies, which is a very difficult task for large proteins because such systems are typically more constrained and exhibit complex motions. Additionally, conformational sampling generally becomes a harder problem as the size of the considered system increases, so these algorithms need to take advantage of significant computational resources when needed.This thesis describes SIMS 2.0, a new framework for conformational sampling built from prior work called the Structured Intuitive Move Selector (SIMS). We introduce an automated construction of perturbation strategies derived from B-factors, secondary structure, and rigidity analysis. We also introduce a new algorithm for conformational sampling that can take advantage of large-scale computational resources while still keeping the geometric reasoning that robotics-inspired algorithms excel at. This work pushes the limits of the size of systems that can be studied by robotics-inspired conformational sampling.

[发布机构] Rice University

[效力级别] Conformational