Adaptive local learning in sampling based motion planning for protein folding

Motivation: Simulating protein foldingmotions is an important problem in computational biology. Motion planningalgorithms such as Probabilistic RoadmapMethods (PRMs) have been successful in modeling the protein folding landscape. PRMs and variants contain several phases (i.e., sampling, connection, and path extraction). Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. Results: We present a local learning algorithm that considers the past performance near the current connection attempt as a basis for learning. It is sensitive not only to different types of landscapesbut also to differing regions in the landscapeitself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52–114 residues. Our method models the landscapewith better quality and comparable time to the best performing individual method and to global learning.