Local Minima Hopping along the Protein Energy Surface




Olson, Brian

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Modeling of protein molecules in silico for the purpose of elucidating the three-dimensional structure where the protein is biologically active employs the knowledge that the protein conformational space has an underlying funnel-like energy surface. The biologically-active structure, also referred to as the native structure, resides at the basin or global minimum of the energy surface. A common approach among computational methods that seek the protein native structure is to search for local minima in the energy surface, with the hope that one of the local minima corresponds to the global minimum. Typical stochastic search methods, however, fail to explicitly sample local minima. This thesis proposes a novel algorithm to directly sample local minima at a coarse-grained level of detail. The Protein Local Optima Walk (PLOW) algorithm combines a memetic approach from evolutionary computation with cutting-edge structure prediction protocols in computational biophysics. PLOW explores the space of local minima by explicitly projecting each move at the global level to a nearby local minimum. This allows PLOW to jump over local energy barriers and more effectively sample near-native conformations. An additional contribution of this thesis is that the memetic approach in PLOW is applied to FeLTr, a tree-based search framework which ensures geometric diversity of computed conformations through projections of the conformational space. Analysis across a broad range of proteins shows that PLOW and memetic FeLTr outperform the original FeLTr framework and compare favorably against state-of-the-art ab-initio structure prediction algorithms.



Native Structure, Near-Native Conformations, Local Minimum, Iterated Local Search, Fragment-Based Assembly