Self-assembly is the spontaneous organization of molecules into a supramolecular complex or new phase without the formation of chemical bonds. This process describes a wide range of phenomena from protein folding, to liquid crystal orientation, to polymer nanocomposite formation. Modeling of peptide self-assembly has been driven by applications in materials science and structural biology. In structural biology, research is motivated by the many disease causing peptides that self-assemble into toxic structures. The main objective of this proposal is the development of a framework for multiscale modeling of peptide assembly, in which molecular simulations are corrected using experimental data. The proposed research combines state-of-the art computer simulation techniques with the novel capability of using experimental data as an extra input to simulations to improve their accuracy.
Self-assembly is one of the most challenging problems for molecular modeling and simulation because it spans multiple length-scales and often multiple time-scales. Coarse-grain techniques which group atoms together in order to simulate larger-length scale interactions suffer from poor agreement with reference experiments and lack rigorous theory for correcting these discrepancies. This proposal aims to address these shortcomings by minimally biasing simulations to correct discrepancies between simulation predictions and experimental data. The proposed new methodology is supported by preliminary data demonstrating improved accuracy and efficiency. These improvements allow a large number of distinct systems to be simulated and guarantee consistency with proposed parallel laboratory experiments. The increase in fidelity and simulation number may lead to the development of deep-learning models that will allow de novo design of self-assembling structures. The main objective of the proposal is to use this biasing technique along with established multiscale simulation methods to study multiple self-assembling peptides, with a particular emphasis on Amyloid beta peptide which is the toxic agent responsible for Alzheimer's disease. The overall scientific objective of the proposed research is to better understand the molecular details of the interplay between entropy, molecular structure and self-assembly through the use of computer simulations. Integration of research and education will involve the development of web-based applications for teaching undergraduate students about coarse-graining, experiment-directed simulation, and self-assembly and a virtual reality workshop to high school students participating in the University of Rochester's Kearns Center mentorship program.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
