Next generation implicit solvation for atomistic modeling

用于原子建模的下一代隐式溶剂化

• 批准号: 10344019
• 负责人: ALEXEY VLAD ONUFRIEV
• 金额: $ 29.4万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 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/10344019
• 关键词: 2019-nCoV; ACE2; Address; Affinity; Algorithms; Amino Acids; Area; Binding; Binding Proteins; Biological Process; Biomedical Research; Cells; Chemicals; Code; Communities; Complex; Computer software; Coronavirus; Coupling; DNA; Docking; Drug Design; Face; Free Energy; Future; Gene Expression; Genome; Goals; Health; High Performance Computing; Human; Hydration status; Incentives; Individual; Industry Standard; Ligands; Liver; Medical; Methods; Modeling; Modernization; Molecular; Molecular Conformation; Mutation; Noise; Physics; Process; Property; Protein Engineering; Proteins; Protocols documentation; Readiness; Research; Sampling; Science; Signal Pathway; Solvents; Speed; Structure; System; Time; Translating; Virus; Water; base; computerized tools; conventional therapy; drug development; drug discovery; electric dipole; experimental study; improved; in silico; innovation; models and simulation; molecular dynamics; molecular modeling; mutant; next generation; novel; novel strategies; pandemic disease; protein folding; receptor; receptor binding; response; screening; simulation; small molecule; structural biology; technology research and development; theories; tool; web-based tool

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.

项目摘要。本提案响应PAR-19-253“重点技术研究和 发展”。我们的主要目标是开发一类新的隐式溶剂化模型，准确，甚至 比标准的显式溶剂模型更准确，但速度更快。准确度高，快速隐式 溶剂化模型将与几种创新策略相结合，以提供新的计算协议， 提高了结合自由能预测的准确性和速度，直接关系到药物设计。我们将 开发一种计算工具，用于快速筛选基因组中现有和潜在的多个同时突变， SARS-CoV-2冠状病毒基因组对人类细胞具有高亲和力，从而转化为高感染性。 现代生物分子科学的进展，从结构生物学到基于结构的药物设计， 原子级建模和模拟大大加速了这一差距， 实验所谓的隐式溶剂化模型可以提供速度和通用性方面的关键优势 通过表现溶剂的影响--这通常是这种模拟中计算成本最高的部分 - 以特别有效的方式。因此，建模工作的加速在许多领域都至关重要，例如 蛋白质折叠或蛋白质-配体对接;然而，目前的快速模型的准确性没有达到 标准的更传统的，但计算非常苛刻的明确的溶剂的方法。因此，在本发明中， 实用的快速隐式溶剂化模型的预测可靠性仍然较低。一般来说，高精度是 定量计算机药物设计的先决条件。这里，当前隐式溶剂化的精度限制 框架将在一个新的，系统的方式解决;新的隐式溶剂化模型的优点将 在提高蛋白质-配体结合自由能计算的准确性的背景下被证明。 我们将使用一种新的方法，系统地添加大部分缺失的显式溶剂化效应， 非常基本的，但计算效率高的隐式求解框架的泊松和广义玻恩（GB） 模型，几乎没有计算开销。GB模型特别适合于分子动力学 模拟我们为新理论设定了高精度标准： 小分子水化实验，这比最广泛使用的显式水模型更好， 例如TIP 3 P，目前可以提供。根据初步结果，这一目标是可以实现的。高精度 结合预期的计算效率将迎来下一代隐式溶剂化 这些模型可以在生物医学相关的原子计算中产生深远的影响。 直接影响的例子：接近或优于蛋白质的“行业标准”准确性- 配体结合计算，但在显着减少的计算费用。长期的例子 影响：快速，多功能的计算工具来分析病毒与宿主细胞的结合将增加我们的研究。 为未来的流行病做好准备。