Integration of Evolution to Avoid Resistance in Structure Based Drug Design

整合进化以避免基于结构的药物设计中的耐药性

• 批准号: 10388034
• 负责人: Celia A. Schiffer
• 金额: $ 6.7万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10388034
• 关键词: Active Sites; Amino Acid Sequence; Binding; Binding Sites; Biological Models; Biological Process; Chemicals; Clinical; Complex; Coupled; Data; Data Set; Dihydrofolate Reductase; Disease; Distal; Drug Design; Drug Targeting; Drug resistance; Enzyme Inhibitor Drugs; Enzymes; Epidermal Growth Factor Receptor; Equilibrium; Event; Evolution; HIV; HIV-1 protease; Human; Machine Learning; Malignant Neoplasms; Maps; Modeling; Modification; Molecular; Mutation; Oncology; Pathogenicity; Pathway interactions; Performance; Pharmaceutical Preparations; Phosphotransferases; Plague; Protease Inhibitor; Protein Dynamics; Protein Tyrosine Kinase; Research; Resistance; Resistance profile; Site; Societies; Structure; Supervision; System; Testing; Therapeutic; Variant; Viral; Virus; base; bcr-abl Fusion Proteins; design; experience; improved; inhibitor/antagonist; insight; machine learning method; molecular dynamics; molecular recognition; novel; predictive modeling; pressure; resistance mechanism; resistance mutation; supervised learning; therapeutic target; tool

Integration of Evolution to Avoid Resistance in Structure Based Drug Design Many of the most deadly diseases that plague our society evolve quickly, challenging our therapeutic strategies. Drug resistance occurs as a result of this evolution when drug pressure changes the balance of molecular recognition events, selectively weakening inhibitor binding while maintaining the biological function of the therapeutic target. Disrupting the therapeutic target’s activity is necessary but not sufficient to avoid resistance. We hypothesize that the multi-dimensional landscape of resistance evolution can be elucidated through integration of experimental and computational data to reveal the key pathways and coupled molecular mechanisms to resistance. Furthermore, we hypothesize that this strategy can be incorporated into structure-based drug design to evaluate novel inhibitors. Resistance occurs under gradual and persistent drug pressure, and interestingly the mutations are not limited to the active site of a drug target, but can occur throughout the enzyme to confer high levels of resistance. The molecular mechanism by which this resistance occurs is not clear. Our aim is to exploit the rich and versatile experimental data, integrating inhibitor potency and crystallographic structures with ensemble dynamics in an internally consistent manner using machine learning to elucidate both the molecular mechanisms of drug resistance and generate predictive models of inhibitor potency.

基于结构的药物设计中整合进化以避免耐药性