Machine Learning Approach for finding novel metallo-b-lactamase inhibitors

寻找新型金属 β 内酰胺酶抑制剂的机器学习方法

• 批准号: 10514544
• 负责人: MICHAEL W CROWDER
• 金额: $ 43.35万
• 依托单位: MIAMI UNIVERSITY OXFORD
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10514544
• 关键词: Antibiotic Resistance; Binding; Biochemical; COVID-19 patient; Chemicals; Clinic; Clinical; Complex; Computer Models; Coupled; Data; Data Set; Enzyme Inhibitor Drugs; Enzymes; Future; Infection; Integrons; Laboratories; Learning; Libraries; Machine Learning; Microbiology; Modeling; Property; Research; Research Personnel; Techniques; Test Result; Testing; Training; base; beta-Lactamase; design; experimental study; high throughput screening; improved; inhibitor; interdisciplinary approach; model development; novel; secondary infection; small molecule libraries; virtual screening; web site

PROJECT SUMMARY/ABSTRACT This proposal describes efforts to develop, test, and improve a machine learning approach to identify potential metallo-β-lactamase (MBL) inhibitors from large chemical libraries. The MBLs are bacterial enzymes that are becoming more prevalent in the clinic and are leading to more incidents of antibiotic resistance in once easily- treatable infections, including secondary infections in COVID patients. There have been tremendous efforts to identify new MBL inhibitors; however to date, there are no clinical inhibitors of these enzymes. This proposal describes a novel, multidisciplinary approach to identify new MBL inhibitors. In Specific Aim 1, we propose to improve our initial computer model, which currently ranks compounds in chemical libraries on their likelihood of being a potential MBL inhibitor, based on previous inhibition data collected on MBL NDM-1. The improved model will be developed using data sets containing inhibition data from quantitative HTS (qHTS) experiments on MBLs, NDM-1, VIM-2, and IMP-1. The new model will be used to screen the 1.3 million compound-containing ChemBridge chemical library, and qHTS studies with VIM-2, IMP-1, and NDM-1 will be conducted to test the results from the virtual screenings. The final, validated model will be made available to the public on our MBL inhibitor website. In Specific Aim 2, we will perform microbiological, structural, and biochemical studies on the top 500 compounds identified by our model and confirmed with qHTS. In addition to minimal inhibitor concentration values, we propose to determine the mechanism of inhibition and to structurally-characterize the enzyme-inhibitor complexes. It is hoped that this machine learning approach will identify novel, pan MBL inhibitors and compounds that can be further re-designed. In addition, it is hoped that this approach, once developed and tested, can be used to identify inhibitors of other biomedically-important enzymes.

项目摘要/摘要 该建议描述了开发，测试和改进机器学习方法以识别潜力的努力 来自大型化学文库的金属-β-内酰胺酶（MBL）抑制剂。 MBL是细菌酶 在诊所变得更加普遍，并在一次轻松地导致更多的抗生素耐药性事件 - 可治疗的感染，包括在互联患者中的继发感染。付出了巨大的努力 识别新的MBL抑制剂；但是，迄今为止，这些酶没有临床抑制剂。这个建议 描述了一种新型的多学科方法来识别新的MBL抑制剂。 在特定目标1中，我们建议改善我们的初始计算机模型，该模型目前对 化学库基于以前的抑制数据，其可能是潜在的MBL抑制剂的可能性 在MBL NDM-1上收集。改进的模型将使用包含抑制数据的数据集开发 来自MBL，NDM-1，VIM-2和IMP-1的定量HTS（QHT）实验。新型号将使用 要筛选130万种化合物化学化学文库的130万库，并使用VIM-2进行QHTS研究 将进行IMP-1和NDM-1，以测试虚拟筛选的结果。最终验证的模型 将在我们的MBL抑制剂网站上向公众提供。在特定的目标2中，我们将执行 我们模型确定的前500种化合物的微生物，结构和生化研究 用QHT确认。除了最小的抑制剂浓度值外，我们还建议确定 抑制机制和结构表征酶抑制剂复合物。 希望这种机器学习方法能够识别出新颖，泛MBL抑制剂和化合物 可以进一步重新设计。此外，希望这种方法一旦开发和测试，就可以是 用于鉴定其他生物医学重要酶的抑制剂。