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: 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
• 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

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。这项对Par-19-253的提案回应“专注的技术研究和 开发”。我们的主要目标是开发一种新颖的隐性解决方案模型，即准确，甚至是 比标准的显式溶剂模型更准确，但要快得多。高精度，快速隐式 解决方案模型将与几种创新策略相结合，以提供新的计算协议 提高与药物设计直接相关的结合自由能预测的准确性和速度。我们将 开发一种计算工具，用于快速筛选现有和潜在的多个简单突变 SARS-COV-2冠状病毒基因组对人类细胞的高亲和力，转化为高感染。 从结构生物学到基于结构的药物设计，现代生物分子科学的进展是 通过原子级建模和模拟弥合理论和 实验。所谓的隐式解决方案模型可以提供速度和多功能性的关键优势 通过表示溶剂的效果 - 通常是计算上最昂贵的部分模拟部分 - 以特别有效的方式。在许多领域，例如 蛋白质折叠或蛋白质配体对接；但是，当前快速模型的准确性未达到 更传统但在计算上非常苛刻的明确溶剂方法的标准。因此， 实际，快速隐式解决方案模型的预测可靠性仍然很低。通常，高精度是 定量内部药物设计的先决条件。在这里，当前隐式解决方案的准确性限制 框架将以新颖的系统方式解决；新隐性解决方案模型的优势将 在提高蛋白质 - 配体结合能量计算的准确性的背景下得到证明。 我们将使用一种新颖的方法系统地将大部分缺失的明确解决方案效应添加到 非常基本的，但在计算上有效的隐式解决方案框架和广义诞生（GB） 模型，几乎没有计算开销。 GB模型特别适合分子动力学 模拟。我们为新理论设定了高精度标准：一个KT（热噪声）偏离 小分子水合的实验，这比最广泛使用的显式水模型要好 例如TIP3P，目前可以交付。基于初步结果，该目标已达到。高精度 结合预期的计算效率将引入下一代隐式解决方案 可以在生物医学相关的原子计算中产生深远影响的模型。 直接影响的示例：接近或比蛋白质的“行业标准”准确性接近或更好 配体结合计算，但计算费用显着降低。长期的例子 影响：快速，多功能的计算工具分析病毒与宿主细胞的结合将增加我们的 准备未来的大流行。