Accurate and Efficient Solvent Models for Molecular Simulations: Methods and Biological Applications

About This Grant

Progress in modern bio-molecular sciences, from structural biology to structure-based drug design, is greatly accelerated by methods of atomic-level modeling and classical simulations that bridge the gap between theory and experiment; 45,000+ research papers that use these methods are published each year. Accurate and computationally facile water models are just as important for outcomes of these studies as water is for Life. In practice, several principal levels of compromise exist between level of detail and speed of solvent models. However, critical accuracy and performance gaps remain at each level, these gaps mute the strong potential of atomistic modeling. For example, even with most detailed (explicit) water models, significant discrepancies with experimental binding free energies are still seen, which is one critical factor that hampers rational drug design efforts. Another problem is computational cost, which can become prohibitive when most accurate existing models are used. On the other hand, in many areas, which can benefit from faster, less detailed (so-called “implicit solvent”) water models, simulations based on these faster models are often unreliable. New solvent models appear regularly, but these are often limited to re-parameterizations of old ones, or utilization of old “base models” to add key new features such as electronic polarization. My lab has always focused on ground up, physics-based approaches to model development, which are more likely than many alternatives to yield robust, transferable models that stand the test of time. The previous funding period has enabled us to accumulate a critical mass of innovations in the field of solvent model development, innovations that have already shown significant promise in practical applications. Importantly, the reported improvements in water model accuracy came without sacrificing the speed. The goal for the next 5 years is to move the entire field of atomistic simulations to the next level of predictive accuracy by delivering to the community a novel class of solvent models, at each key level of detail--speed compromise. To demonstrate utility of the new models (once thoroughly tested), we will apply them to: (1) Improving the accuracy, without sacrificing speed, of estimation of receptor-ligand binding free energies. In structure-based drug discovery, the accuracy and computational efficiency of in-silico binding free energy predictions for small molecules to biomolecular targets are crucial for high-throughput screening of drug candidates. (2) Generation of novel insights into regulation of DNA accessibility in the nucleosome, which directly affects gene expression. Understanding how DNA accessibility in the nucleosome is controlled/affected by various biologically relevant factors is a fundamentally important problem of direct biomedical relevance. The disruption of the histone functions leads to diseases. In addition, the novel, higher accuracy, yet efficient solvent models will be implemented into H++ web-server maintained by the PI (12,000+ registered users, 20,000+ requests per year), thus immediately improving outcomes of structure preparation and analysis efforts for a large modeling community.