Next generation implicit solvation for atomistic modeling

项目摘要。该提案响应了PAR-19-253《聚焦技术研究和 发展“。我们的主要目标是开发一类新的隐式溶剂化模型，作为精确的，甚至 比标准的显式溶剂模型更准确，但速度快得多。高精度、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。我们会 开发一种计算工具，用于快速筛查现有的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，这转化为高传染性。 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 原子级建模和模拟大大加快了速度，弥补了理论和模拟之间的差距 做实验。所谓的隐式溶剂化模型可以在速度和通用性方面提供关键的优势 通过表示溶剂的影响--通常是此类模拟中计算量最大的部分 --以特别高效的方式。由此产生的建模工作的加速在许多领域都是至关重要的，例如 蛋白质折叠或蛋白质-配体对接；然而，当前快速模型的准确性达不到 更传统的标准，但在计算上要求非常高的显式溶剂方法。结果, 实用、快速的隐式求解模型的预测可靠性仍然很低。一般来说，高精度是一种 定量硅内药物设计的先决条件。在这里，电流隐式求解的精度极限 框架将以一种新颖、系统的方式解决；新隐式溶剂化模型的优点将 将在提高蛋白质-配体结合自由能计算的准确性的背景下进行论证。 我们将使用一种新的方法来系统地将大多数缺失的显式溶剂化效应添加到 Poisson和广义Born(GB)的非常基本但计算高效的隐式求解器框架 模型，计算开销很小。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：1kT(热噪声)偏差 小分子水合实验，这比最广泛使用的显式水模型更好， 例如TIP3P，目前可以交付。根据初步结果，这一目标是可以实现的。高精确度 结合期望的计算效率，将迎来下一代隐式求解 可以在生物医学相关的原子计算中产生深远影响的模型。 立竿见影的例子：蛋白质的准确度接近或超过“行业标准” 配体结合计算，但计算费用显著降低。一个长期的例子 影响：快速、通用的计算工具来分析病毒与宿主细胞的结合将增加我们的 为未来的大流行做好准备。

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.