Abstract:
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.
