Efficient and Accurate Force Fields for Computer-Aided Drug Design

用于计算机辅助药物设计的高效、准确的力场

基本信息

批准号：
10092175
负责人：
Feng Wang
金额：
$ 29.46万
依托单位：
UNIVERSITY OF ARKANSAS AT FAYETTEVILLE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10092175
关键词：
Acids Address Affinity Antibiotic Resistance Antibiotics Bacterial Infections Binding Binding Proteins Biological Carbon Dioxide Code Complex Computer Assisted Computer-Aided Design Custom Databases Development Drug Costs Drug Design Drug usage Environment Evaluation Failure Fluorescence Resonance Energy Transfer Free Energy Hydration status Ice Lead Ligands Lipids Mechanics Membrane Methods Modeling Multi-Drug Resistance Nature Nisin Partition Coefficient Performance Pharmaceutical Preparations Pharmacologic Substance Physiological Procedures Property Proteins Protocols documentation Public Health Research Resolution Role Salts Sampling Series Solubility Source Code Structure System Techniques Temperature Testing Time Work base blind combat computer studies design drug candidate drug discovery electronic structure experimental study improved innovation macromolecule melting molecular dynamics novel predictive modeling protocol development quantum simulation

项目摘要

Efficient and Accurate Force Fields for Computer-Aided-Drug Design Computer-aided drug design is hampered by the limited accuracy in the force fields that describe the interactions between drug molecules, their targets, and shared environment. An automated protocol, adaptive force matching (AFM), is proposed for creating customized force fields that enable more efficient and accurate computational studies of the structure and function of drug candidates. Putting AFM in place will require the adaption of highly efficient techniques for sampling of configurations and quantum mechanical calculations of large systems. The approach will be developed and evaluated in a series of aims with increasing complexity. In the first two aims, force fields are generated for prediction of hydration free energies of small drug-like molecules and of the binding affinities for guest-host pairs. The performance of the method will be tested on examples from the recent SAMPL4 competition. In the last aim, force fields are created for simulations that allow one to identify nisin derivatives with improved solubility and stability at physiological pH as required for the design of nisin-based novel antibiotics. This class of drug is generally referred to as lantibiotics, and it carries the promise to address the increasingly severe problem of multi-drug resistant bacterial infection.

用于计算机辅助药物设计的高效、准确的力场计算机辅助药物设计受到描述药物的力场精度有限的阻碍。药物分子、其靶标和共享环境之间的相互作用。自动化协议，自适应力匹配（AFM）被提议用于创建定制的力场，从而实现更高效和准确的候选药物的结构和功能的计算研究。将 AFM 放置到位需要采用高效技术进行配置采样和量子力学计算大型系统。该方法将针对一系列日益复杂的目标进行开发和评估。在前两个目标，产生力场来预测小药物的水合自由能分子和客体-主体对的结合亲和力。该方法的性能将在最近 SAMPL4 竞赛的示例。在最后一个目标中，为模拟创建力场允许人们根据需要鉴定在生理 pH 下具有改善的溶解度和稳定性的乳链菌肽衍生物基于乳链菌肽的新型抗生素的设计。这类药物一般称为羊毛硫抗生素，它有望解决日益严重的多重耐药细菌感染问题。