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Advancing predictive physical modeling through focused development of model systems to drive new modeling innovations

通过集中开发模型系统来推进预测物理建模，以推动新的建模创新

基本信息

批准号：
10245037
负责人：
David Lowell Mobley
金额：
$ 28.24万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-10 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10245037
关键词：
Accounting Affect Affinity Area Automobile Driving Back Binding Binding Proteins Biological Biological Models Cell Membrane Permeability Chemicals Collaborations Communities Computational Biology Computational Technique Computing Methodologies Data Data Set Development Disease Docking Drug Design Drug Industry Drug Targeting Ensure Evaluation Failure Fostering Free Energy Funding Generations Head Health Human Individual Informatics Learning Ligands Measurement Measures Methodology Methods Modeling Modification Molecular Performance Pharmaceutical Preparations Pharmacologic Substance Phase Play Proteins Publications Research Robotics Running Sampling Science Series Serum Albumin Solubility Solvents Stress Tests System Techniques Technology Testing Time Treatment Factor United States National Institutes of Health Work aqueous base blind catalyst cavitand crowdsourcing data resource design drug development drug discovery experimental study foot improved innovation insight lead optimization model development molecular recognition new technology novel personalized medicine physical model physical property predictive tools protonation receptor small molecule small molecule therapeutics success targeted treatment tautomer virtual screening

项目摘要

PROJECT SUMMARY / ABSTRACT This work seeks to advance quantitative methods for biomolecular design, especially for predicting biomolecular interactions, via a focused series of community blind prediction challenges. Physical methods for predicting binding free energies, or “free energy methods”, are poised to dramatically reshape early stage drug discovery, and are already ﬁnding applications in pharmaceutical lead optimization. However, performance is unreliable, the domain of applicability is limited, and failures in pharmaceutical applications are often hard to understand and ﬁx. On the other hand, these methods can now typically predict a variety of simple physical properties such as solvation free energies or relative solubilities, though there is still clear room for improvement in accuracy. In recent years, competitions and crowdsourcing have proven an effective model for driving innovations in diverse ﬁelds. In our ﬁeld, blind prediction challenges have played a key role in driving innovations in prediction of physical properties and binding, especially in the form of the SAMPL series of challenges. Here, we will continue and extend SAMPL prediction challenges to include new physical properties, more complicated host-guest binding data, and application to biomolecular systems. Carefully selected systems and novel experimental data will provide challenges of gradually increasing complexity spanning between systems which are now tractable to those which are marginally out of reach of today's methods but still slightly simpler than those covered by the Drug Design Data Resource (D3R) series of challenges on existing pharmaceutical data. We will work with D3R to run blind challenges on the data we generate and to ensure it is designed to maximally beneﬁt the ﬁeld. In Aim 1, we will collect new measurements on partitioning, distribution, and protonation of drug-like compounds, in collaboration with partners in the pharmaceutical industry. In Aim 2, we leverage our expertise in host-guest binding to generate new data on host-guest binding in cucubiturils and deep cavity cavitands. And in Aim 3, we use high-throughput robotic experiments to generate new protein-ligand binding data of biological relevance. Aim 4 focuses on using this data in the SAMPL series of challenges, applying proven crowdsourcing-based techniques to drive the development of new methods and new understanding of the strengths and weaknesses of existing techniques. We will also run reference calculations with the latest techniques. This work will ensure the continued success of SAMPL challenges which have already driven considerable innovation in the ﬁeld and been the focus of 100 different publications (each typically cited 5-50 times) since their inception around 2007, and will play a key role in driving the next several generations of improvements in computational techniques for molecular design. The research proposed here will lead to signiﬁcant improvements in the predictive power of physical models for drug discovery, molecular design and the prediction of physical properties.

项目总结/摘要本工作旨在促进生物分子设计的定量方法，特别是用于预测生物分子互动，通过一系列集中的社区盲预测挑战。预测结合的物理方法自由能量，或“自由能量方法”，正准备大大重塑早期药物发现，已经在制药铅优化中找到了应用。但是，性能不可靠，适用范围有限，药物应用中的失败往往难以理解， fix.另一方面，这些方法现在通常可以预测各种简单的物理性质，溶剂化自由能或相对溶解度，尽管在精确度上仍有明显的改进空间。在近年来，竞争和众包已被证明是推动多样化创新的有效模式。菲尔德。在我们的领域，盲预测挑战在推动物理预测创新方面发挥了关键作用。属性和约束力，特别是在SAMPL的形式一系列的挑战。在这里，我们将继续，扩展SAMPL预测挑战，包括新的物理性质，更复杂的主客体结合数据和应用于生物分子系统。精心挑选的系统和新颖的实验数据将提供逐渐增加的复杂性的挑战，跨越系统之间，现在是易于处理的那些，略超出了今天的方法，但仍然比药物设计所涵盖的方法略简单数据资源（D3 R）对现有制药数据的一系列挑战。我们将与D3 R合作，挑战我们生成的数据，并确保它的设计是为了最大限度地贝内该领域。在目标1中，我们将收集关于药物样化合物的分配、分布和质子化的新测量结果，与制药行业的合作伙伴合作。在目标2中，我们利用我们在主客体关系方面的专业知识，结合产生新的数据，在葫芦脲和深腔cavitands的主客体结合。在目标3中，使用高通量机器人实验来产生新的生物相关的蛋白质-配体结合数据。目的 4侧重于在SAMPL系列挑战中使用这些数据，应用经过验证的基于众包的技术推动新方法的发展，并对现有方法的优点和缺点有新的认识，技术.我们还将使用最新技术运行参考计算。这项工作将确保SAMPL挑战的持续成功，这些挑战已经推动了相当大的在该领域的创新，并成为100个不同的出版物的焦点（每个通常被引用5-50次），因为他们在2007年左右成立，并将在推动未来几代的改进中发挥关键作用，分子设计的计算技术。这里提出的研究将导致显着的改善在药物发现、分子设计和物理模型预测方面，特性.

项目成果

期刊论文数量（60）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling.

DOI：
10.3390/ijms22063078
发表时间：
2021-03-17
期刊：
International journal of molecular sciences
影响因子：
5.6
作者：
Boz E;Stein M
通讯作者：
Stein M

Biomolecular Solvation Structure Revealed by Molecular Dynamics Simulations.

分子动力学模拟揭示的生物分子溶剂化结构。

DOI：
10.1021/jacs.8b13613
发表时间：
2019
期刊：
Journal of the American Chemical Society
影响因子：
15
作者：
Wall,MichaelE;Calabró,Gaetano;Bayly,ChristopherI;Mobley,DavidL;Warren,GregoryL
通讯作者：
Warren,GregoryL

An overview of the SAMPL8 host-guest binding challenge.