CCF: AF: Small: Novel Stochastic Optimization Algorithms to Advance the Treatment of Dynamic Molecular Systems

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This project aims to advance algorithmic research in computational biology. The focus is on problems that demand characterizations of molecules, such as proteins and peptides that are central to the inner workings of cell. These molecules often switch between different structural configurations to be compatible and interact with different molecular partners in the cell. Computing the different configurations a protein or peptide takes on for biological activity is central to understanding how these molecules operate in the healthy and diseased cell. Doing so, however, poses many computational challenges, and the research in this project aims to advance our ability to meet these challenges.The project generalizes the problem of obtaining a comprehensive view of possibly diverse functional states as that of obtaining a diverse ensemble of constraint-satisfying structures. Novel stochastic optimization techniques are put forth to deal with the high-dimensional variable spaces arising in these molecules, as well as the nonlinearity and multimodality of the energy surface associated with these spaces. The techniques include hybridization and structurization to effectively balance computational resources between exploration and exploitation. The focus is on blending the two in parallel architectures. Novel multi-objective analysis addresses the inherent noise in energy functions. Accumulated biophysical knowledge drives the design of novel variation operators for effective global and local search. The research is made available as software to facilitate advancement of stochastic optimization while allowing computational biologists to model specific systems of interest with cutting-edge techniques. The research supports a number of coordinated teaching and outreach activities across computer science, bioengineering, and neuroscience, integrating postdoctoral, college, and pre-college students.