Computational tools for new pharmaceutical paradigms

About This Grant

PROJECT SUMMARY/ABSTRACT Advances in molecular simulation models and methods have made it possible in many cases to simulate biological macromolecules and their environments over time scales of physical relevance with near-quantitative accuracy. In particular, such approaches are now used directly in the pharmaceutical industry to guide synthesis of new compounds using computed changes in small molecule binding affinities. However, despite substantial work by many researchers over decades, the majority of protein-ligand binding phe- nomena related to human health remain prohibitively expensive to model with sufficient accuracy using current simulation capabilities. Such therapeutic modalities include targeting protein-protein interfaces, kinases and phos- phatases inhibitors, and partners of short peptide interaction motifs. One significant reason for this difficulty is that these targets favor bigger and more flexible binders, with both larger scale motions and less frequent transi- tions between metastable states, requiring significantly more sampling than is currently computationally feasible. Additionally, existing force fields have been developed in a piecemeal fashion, do not generalize over all of rel- evant chemistry, and the proper trade-offs between increased complexity and computational cost of force field improvements are not clear. We propose to address these challenges building on our extensive expertise in methods development and simu- lation software for improved biophysical modeling and drug design. In collaboration with other researchers in the Open Force Field Initiative, my group will work to build systematically improvable force fields that achieve high accuracy and broad coverage of NIH-relevant chemistry, that self-consistently model heterogeneous biomolecular systems, and that can be broadly applied across a range of high-performance software packages. In particular, we will use new sources of thermophysical data to optimize and validate behavior in complex chemical environments and will develop statistical approaches to better gauge the utility of increasing force field complexity. In addition, we will develop novel, well-validated molecular simulation sampling methodologies that can capture the full configurational ensembles needed to accurately compute binding free energies of large, flexible ligands. Such algorithms will be designed to support new and anticipated computing architectures such as heterogeneous clusters and cloud computing via asynchronous approaches, and will augment molecular dynamics with recent developments in machine learning structural prediction. This research is significant and transformative as the computational concepts and technology developed via this research will enable accurate prediction of larger, flexible ligands from across all of bioorganic chemistry. The effort has the potential to assist drug developers break away from existing design paradigms of relatively small, rigid molecules bound to relatively rigid well-defined binding sites, accessing new targets and mechanisms of action and as well as therapeutics targeting them.