Entropy for End-Point and FFT-Based Binding Free Energy Calculations

用于端点和基于 FFT 的结合自由能计算的熵

基本信息

批准号：
10204042
负责人：
David Do Le Minh
金额：
$ 33.48万
依托单位：
ILLINOIS INSTITUTE OF TECHNOLOGY
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：
2018-08-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10204042
关键词：
Accounting Affinity Algorithms Amino Acids Antibiotics Bacteria Benchmarking Binding Binding Proteins Biochemical Biological Biological Assay Biological Process Blinded Charge Chemistry Code Communities Complex Computer Assisted Computer software Computers Computing Methodologies Crystallization Data Set Docking Drug Design Drug Screening Endocrine Disruptors Entropy Enzymes Fourier Transform Free Energy Genetic Variation Human Life Ligands Measures Metabolic Methods Missense Mutation Molecular Molecular Conformation Mutation Occupations Pathway interactions Performance Pharmaceutical Preparations Proteins Research Sampling Science Solvents Statistical Mechanics Structure System TP53 gene Techniques Testing Translations base data resource drug discovery exhaustion flexibility follow-up gain of function improved insight mutant open source physical separation prospective protein complex protein structure prediction restraint simulation small molecule libraries tumor growth virtual

项目摘要

Project Summary Computers are often used to predict how tightly two molecules associate, their binding free energy. These predictions are helpful for designing drugs, predicting the consequences of genetic variation, and understanding how molecules interact to sustain life. Unfortunately, currently available methods are either fast or accurate, but not both. In general, fast methods do a poor job accounting for entropy, which is an important part of the free energy. The main objective of this project is to develop better ways to account for entropy in two popular techniques for studying molecular interactions: “end-point” simulations of the bound complexes and their unbound counterparts; and molecular docking based on the Fast Fourier Transform. Speciﬁcally, new ways to analyze calculation results will be derived, implemented, assessed, and optimized. Additionally, the methods will be combined with enhanced sampling techniques. Our new end-point and FFT-based methods will be assessed by their ability to reproduce benchmark results from slower but more accurate computational methods, as well as experimental results. The benchmark dataset will include protein-ligand complexes and protein-protein complexes with known binding afﬁnities and crystal structures, as well as protein-protein complexes for which the effect of missense mutations on binding have been measured. We will also perform benchmark calculations on mutants of the tumor suppressor p53 that gain the ability to activate new proteins and promote tumor growth. In addition to serving as benchmarks, these calculations may provide mechanistic insight into how proteins bind various ligands and how p53 mutants gain new binding partners. Our new methods will also be tested in recurring community challenges: the “Drug Design Data Resource” (D3R) grand challenge to predict protein-ligand complex structures and afﬁnities and the “Critical Assessment of PRediction of Interactions” (CAPRI) challenge for protein-protein structure prediction. These blinded challenges will allow for an unbiased comparison of our methods to those from other research groups. Finally, we will assess our methods in a drug discovery project. We will use established methods and our new methods to virtually screen a chemical library against a pair of structurally similar bacterial metabolic enzymes. One enzyme is relevant to active and the other to dormant bacteria. Compounds predicted to selectively bind the bacterial (opposed to human) enzymes will be experimentally tested in biochemical assays. We anticipate that our improved methods will be signiﬁcantly more accurate than established approaches, advancing research ranging from interactome prediction to drug discovery.

项目概要计算机通常用于预测两个分子结合的紧密程度及其结合自由能。预测有助于设计药物、预测遗传变异的后果以及理解不幸的是，目前可用的方法要么快速要么准确，但一般来说，快速方法在解释熵方面做得很差，而熵是问题的重要组成部分。该项目的主要目标是开发更好的方法来解释两种流行的熵。研究分子相互作用的技术：结合复合物及其它们的“终点”模拟未结合的单元；以及基于快速傅里叶变换的分子对接。分析计算结果将得到、实施、评估和优化。将与增强的采样技术相结合。我们的新端点和基于 FFT 的方法将根据其再现基准结果的能力进行评估来自较慢但更准确的计算方法以及基准数据集。将包括具有已知结合亲和力和晶体的蛋白质-配体复合物和蛋白质-蛋白质复合物结构，以及错义突变对结合的影响的蛋白质-蛋白质复合物我们还将对肿瘤抑制因子 p53 的突变体进行基准计算。除了作为基准之外，这些还具有激活新蛋白质和促进肿瘤生长的能力。计算可以提供有关蛋白质如何结合各种配体以及 p53 突变体如何获得的机制见解新的绑定伙伴。我们的新方法还将在反复出现的社区挑战中进行测试：“药物设计数据资源” （D3R）预测蛋白质-配体复合物结构和亲和力的重大挑战以及“关键评估” 相互作用预测”（CAPRI）蛋白质-蛋白质结构预测挑战。将允许我们的方法与其他研究小组的方法进行公正的比较。最后，我们将在药物发现项目中评估我们的方法，我们将使用现有的方法和新的方法。针对一对结构相似的细菌代谢酶虚拟筛选化学库的方法。一种酶与活性细菌相关，另一种与预计选择性结合的休眠细菌相关。我们预计细菌（与人类相反）酶将在生化测定中进行实验测试。我们改进的方法将比现有的方法更加准确，从而推进研究范围从相互作用组预测到药物发现。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of the riboflavin cofactor-binding site in the Vibrio cholerae ion-pumping NQR complex: A novel structural motif in redox enzymes.

霍乱弧菌离子泵 NQR 复合物中核黄素辅因子结合位点的鉴定：氧化还原酶中的一种新结构基序。

DOI：
发表时间：
2022-08
期刊：
影响因子：
0
作者：
Tuz, Karina;Yuan, Ming;Hu, Yuyao;Do, Tien T T;Willow, Soohaeng Yoo;DePaolo;Fuller, James R;Minh, David D L;Juárez, Oscar
通讯作者：
Juárez, Oscar

Conserved residue His-257 of Vibrio cholerae flavin transferase ApbE plays a critical role in substrate binding and catalysis.

霍乱弧菌黄素转移酶 ApbE 的保守残基 His-257 在底物结合和催化中发挥着关键作用。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Fang, Xuan;Osipiuk, Jerzy;Chakravarthy, Srinivas;Yuan, Ming;Menzer, William M;Nissen, Devin;Liang, Pingdong;Raba, Daniel A;Tuz, Karina;Howard, Andrew J;Joachimiak, Andrzej;Minh, David D L;Juarez, Oscar
通讯作者：
Juarez, Oscar

A Structural Model for Bax∆2-Mediated Activation of Caspase 8-Dependent Apoptosis.

Bax-2 介导的 Caspase 8 依赖性细胞凋亡激活的结构模型。