Proteins are molecular machines that are largely responsible for processes as varied as digestion of food to building new components of cells. Many proteins are capable of spontaneously folding from an extended chain into compact, functional machines. Once folded, proteins continue to undergo motions that are related to their stability and function. Understanding the functional relevance of these motions remains extremely challenging because it is difficult to observe movement on the atomic scale and provide the necessary structural detail to connect these motions with a protein's function. The objectives of this project are 1) to develop powerful algorithms for simulating these protein motions, 2) apply these algorithms to understand how proteins fold, and 3) to combine these algorithms with biochemical experiments to design proteins that are more stable than their natural counterparts. Completion of this research will lay the foundation for future efforts to understand the role of protein motions in processes like cellular communications and to design proteins for applications such as the synthesis of biofuels. In concert with these research objectives, the PI will develop a python programming boot camp to teach students in biology the basic programming skills required to analyze their own data, providing a starting point for more sophisticated integration of computation and experiments and opening new job opportunities in the STEM fields.
This project will identify general properties of free energy landscapes of proteins from simulation datasets created with specialized hardware and leverage them to empower similar studies with commodity hardware. This work will be guided by the hypothesis that leveraging ideas from optimization theory regarding exploration/exploitation tradeoffs will allow efficient conformational searches. Based on preliminary analyses, the PI's lab has already begun to prototype a new algorithm, referred to as fluctuation amplification of specific traits, or FAST. Further developing this algorithm, demonstrating its power, and disseminating it to the broader scientific community will lay a foundation for understanding and designing protein's conformational ensembles. Specific goals include: 1) develop the fluctuation amplification of specific traits (FAST) algorithm to efficiently explore a protein's conformational space, 2) test whether FAST can fold proteins, and 3) test whether FAST can reveal opportunities for designing stabilized proteins without perturbing their functions.
This project is jointly funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Physics of Living Systems Program in the Division of Physics in the Directorate of Mathematical and Physical Sciences.