A PCA-guided Search Algorithm to Probe the Conformational Space of the Ras Protein
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Exploring the Structure Space of Wildtype Ras Guided by Experimental Data
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Multi-Resolution Rigidity-Based Sampling of Protein Conformational Paths
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Characterization of transition trajectories that take a protein between different functional states is an important yet challenging problem in computational biology. Approaches based on Molecular Dynamics can obtain the most detailed and accurate information but at considerable computational cost. To address the cost, sampling-based path planning methods adapted from robotics forego protein dynamics and seek instead conformational paths, operating under the assumption that dynamics can be incorporated later to transform paths to transition trajectories. Existing methods focus either on short peptides or large proteins; on the latter, coarse representations simplify the search space. Here we present a robotics-inspired tree-based method to sample conformational paths that connect known structural states of small- to medium- size proteins. We address the dimensionality of the search space using molecular fragment replacement to efficiently obtain physically-realistic conformations. The method grows a tree in conformational space rooted at a given conformation and biases the growth of the tree to steer it to a given goal conformation. Different bias schemes are investigated for their efficacy. Experiments on proteins up to 214 amino acids long with known functionally-relevant states more than 13A apart show that the method effectively obtains conformational paths connecting significantly different structural states.