Title: Probabilistic Search Frameworks for Protein Modeling

Abstract:

Protein modeling is central to improve our understanding of biology
and disease. Diverse search and optimization algorithms in robotics
motion planning and evolutionary computation bear useful analogies and
ideas that can be exploited to advance the state of protein
modeling. Building on these analogies, we present novel probabilistic
search and optimization algorithms to compute the biologically-active
structure of a protein from its amino acid sequence, model the bound
structure of a protein dimer from the unbound rigid monomeric
structures, or map conformational transitions between two
functionally-relevant structural states of a protein system. In
particular, a novel robotics-inspired probabilistic search framework
is shown versatile and effective both in modeling structures and
transitions between them. The framework employs projections of the
search space in order to adaptively guide its exploration and further
computational resources to relevant regions of the search
space. Applications show enhanced sampling of the protein
conformational space and effective modeling of biologically-active
structures. Conformational transitions are obtained that connect
functional states of significant structural dissimilarity in
multimodal protein systems. A novel evolutionary-inspired
probabilistic optimization framework is shown effective in sampling
minima of an energy surface. Moreover, ongoing work is showing that
utilization of both the evolutionary and robotics-inspired framework
allows obtaining a broad view of the energy surface of small peptides
and mapping out transitions between stable and semi-stable states.