. This proposal responds to PAR-17-046 ?Exploratory Research for Technology Development (R21)?. The goal of the proposal is to explore a novel way of constructing implicit solvation models that can be as accurate as the standard explicit solvent models, but much faster. Progress in modern bio-molecular sciences, from structural biology to structure-based drug design, is greatly accelerated by methods of atomic-level modeling and simulations that bridge the gap between theory and experiment. The so-called implicit solvation model can provide critical advantages of speed and versatility through representing the effects of solvent ? often the most computationally expensive part of such simulations ? in an approximate manner. The resulting speed-up of modeling efforts is critical in many areas such as protein folding or protein-ligand docking. However, the accuracy of these fast models does not reach the standard of the more traditional, but computationally very demanding explicit solvent approach. In particular, even sophisticated implicit solvation models are unable to emulate explicit solvation effects with chemical accuracy (errors less than 1 kcal/mol), simultaneously for small drug-like molecules and amino-acids ? the two key building blocks of every bio-medically relevant atomistic simulation. In general, chemical accuracy is a prerequisite for quantitative in-silico drug design. As a result, reliability of the practical, fast implicit solvation models remains low for many bio-medically relevant problems such as protein-ligand binding. Here, the accuracy limitation will be addressed in a novel, systematic way; advantages of the new implicit solvation models will be demonstrated within the context of bio-medically relevant applications. We will use a novel approach to systematically add most of the explicit solvation effects to the very basic, but efficient implicit solvation framework of the Poisson model, with little computational overhead. We will explore the possibility of adaptation of the new models for MD simulations. We have set high accuracy standards for the new theory: chemical accuracy simultaneously for small drug-like molecules and amino-acids. Reaching that goal is paramount for ushering in the next generation of implicit solvent models that can make a profound difference in bio-medically relevant atomistic calculations. Results will benefit the entire biomolecular modeling community by providing it with an approach to build new, accurate and fast tools for atomistic simulation. Example of an immediate impact: Close to ?explicit solvent? accuracy in protein-ligand binding calculations, but without the associated expense. Example of a long term impact: A clear quantitative understanding of which of the many explicit solvent effects missing from the basic continuum solvent description, are most/least important for accuracy of practical atomistic computations.

Public Health Relevance

Molecular modeling and simulations are indispensable tools in biomedical science and the drug Project Narrative. dthisecolivkeerlyihporoodceosfs.imTphoerptarnotpodsiescdorveesreieasrchbywmillaskiignngifitchaenttloyoelsnhance the capabilities of these tools and more accurate, faster, and more widely available.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Exploratory/Developmental Grants (R21)
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Macromolecular Structure and Function D Study Section (MSFD)
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Smith, Ward
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Virginia Polytechnic Institute and State University
Biostatistics & Other Math Sci
Biomed Engr/Col Engr/Engr Sta
United States
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