Populating Local Minima in the Protein Conformational Space

摘要

Protein Modeling conceptualizes the protein energy landscape as a funnel with the native structure at the low-energy minimum. Current protein structure prediction algorithms seek the global minimum by searching for low-energy conformations in the hope that some of these reside in local minima near the native structure. The search techniques employed, however, fail to explicitly model these local minima. This work proposes a memetic algorithm which combines methods from evolutionary computation with cutting-edge structure prediction protocols. The Protein Local Optima Walk (PLOW) algorithm proposed here explores the space of local minima by explicitly projecting each move in the conformation space to a nearby local minimum. This allows PLOW to jump over local energy barriers and more effectively sample near-native conformations. Analysis across a broad range of proteins shows that PLOW outperforms an MMC-based method and compares favorably against other published abini to structure prediction algorithms.