AF: Small: Motion Planning Techniques for Protein Motion

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Protein motions play an essential role in many biochemical processes. For example, as proteins fold to their native, functional state, they sometimes undergo critical conformational changes that affect their functionality, e.g., diseases such as Mad Cow disease or Alzheimer's disease are associated with protein misfolding and aggregation. Proteins also undergo conformational change when interacting with other molecules as they transition between bound and unbound states. Knowledge of the mechanics of these motion processes may help provide insight into how and why proteins misfold, how binding regulation is communicated through the protein structure, and how to design more effective drugs. For example, a better understanding of protein misfolding and aggregation has the potential to provide insight into neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, prion diseases, and related diseases that have a major impact on society. An important, and more immediate, goal of the project is to share detailed results generated by the new methods with the community in a publicly available database of protein motions. This project will develop new modeling, simulation and analysis tools that specialize and apply a novel computational method for studying molecular motions that has been developed and validated against experimental data in preliminary work. This method represents a trade-off between methods such as molecular dynamics and Monte Carlo simulations that provide detailed individual folding trajectories and techniques such as statistical mechanical methods that provide global landscape statistics. The approach, derived from robotic motion planning methods, builds a graph that encodes many (typically thousands) of motion pathways. The proposed work involves both algorithmic research to further develop and optimize the techniques and research necessary to apply them to study issues of current interest in protein science. While the algorithmic research will be performed by computer scientists, the application and testing of the techniques will benefit from collaborations with labs currently studying these problems. The main research goals include: (i) The development of new and/or improved metrics and analysis techniques for conformations, pathways, and roadmaps that can be applied to modeling more complex motion applications. These methods will be validated and applied to protein transitions, decoy database improvement, and ligand binding. (ii) New methods for modeling and simulating constrained motion and incorporating greater bond flexibility in areas of legitimate need (neither supported by current framework). These methods will be applied to modeling protein transitions, and ligand binding. (iii) Strategies for employing high-performance computing to increase the size and complexity of the systems that can be studied.