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Next generation implicit solvation for atomistic modeling

用于原子建模的下一代隐式溶剂化

基本信息

批准号：
10544161
负责人：
ALEXEY VLAD ONUFRIEV
金额：
$ 30.38万
依托单位：
VIRGINIA POLYTECHNIC INST AND ST UNIV
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10544161
关键词：
2019-nCoV ACE2 Acceleration Address Affinity Algorithms Amino Acids Area Binding Binding Proteins Biological Process Biomedical Research Cells Chemicals Code Communities Complex Computer software Coupling DNA Docking Drug Design Face Free Energy Gene Expression Goals Head Health High Performance Computing Human Hydration status Incentives Individual Industry Standard Ligands Methods Modeling Modernization Molecular Molecular Conformation Mutation Noise Physics Process Property Proteins Protocols documentation Readiness Research SARS-CoV-2 genome Sampling Science Signal Pathway Solvents Speed Structure Translating Virus Water base computerized tools conventional therapy design drug development drug discovery electric dipole experimental study future pandemic improved in silico innovation models and simulation molecular dynamics molecular modeling mutant next generation novel novel strategies pandemic disease pandemic response protein folding receptor receptor binding screening simulation small molecule structural biology technology research and development theories tool web-based tool

项目摘要

Project Summary. This proposal responds to PAR-19-253 “Focused Technology Research and Development”. Our main goal is to develop a novel class of implicit solvation models, as accurate, and even more accurate, than standard explicit solvent models, but much faster. The high accuracy, fast implicit solvation models will be combined with several innovative strategies to deliver new computational protocols to improve accuracy and speed of binding free energies prediction, directly relevant to drug design. We will develop a computational tool for fast screening of existing and potential multiple simultaneous mutations in the SARS-CoV-2 coronavirus genome for high affinity to human cells, which translates into high infectivity. Progress in modern bio-molecular sciences, from structural biology to structure-based drug design, is greatly accelerated by atomic-level modeling and simulations that bridge the gap between theory and experiment. The so-called implicit solvation models can provide critical advantages in speed and versatility through representing the effects of solvent – often the most computationally expensive part of such simulations – in a particularly efficient 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 the current fast models does not reach the standard of the more traditional, but computationally very demanding explicit solvent approach. As a result, prediction reliability of the practical, fast implicit solvation models remains low. In general, high accuracy is a prerequisite for quantitative in-silico drug design. Here, the accuracy limitation of the current implicit solvation framework will be addressed in a novel, systematic way; advantages of the new implicit solvation models will be demonstrated in the context of improving the accuracy of protein-ligand binding free energy calculations. We will use a novel approach to systematically add most of the missing explicit solvation effects to the very basic, but computationally efficient implicit solvation framework of the Poisson and generalized Born (GB) models, with little computational overhead. The GB model is particularly well suited for molecular dynamics simulations. We have set high accuracy standards for the new theory: one kT (thermal noise) deviation from experiment for small molecules hydration, which is better than what most widely used explicit water models, such as TIP3P, can currently deliver. Based on preliminary results, this goal is within reach. The high accuracy combined with the expected computational efficiency will usher in the next generation of implicit solvation models that can make a profound difference in bio-medically relevant atomistic calculations. Example of an immediate impact: Close to, or better than, “industry standard” accuracy in protein- ligand binding calculations, but at a significantly reduced computational expense. Example of a long term impact: Fast, versatile computational tools to analyze binding of viruses to host cells will increase our preparedness for future pandemics.

项目总结。该提案响应PAR-19-253“重点技术研究和