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.

项目总结/摘要 该提案描述了开发、测试和改进机器学习方法的努力，以识别潜在的 金属-β-内酰胺酶（MBL）抑制剂。MBL是细菌酶， 在临床上变得越来越普遍，并导致更多的抗生素耐药性事件， 可治疗的感染，包括COVID患者的继发感染。我们付出了巨大的努力 以鉴定新的MBL抑制剂;然而，迄今为止，还没有这些酶的临床抑制剂。这项建议 描述了一种新的，多学科的方法，以确定新的MBL抑制剂。 在具体目标1中，我们建议改进我们的初始计算机模型，该模型目前将化合物排列在 基于先前的抑制数据，化学文库对其作为潜在MBL抑制剂的可能性进行评估 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来开发 来自对MBL、NDM-1、Vim-2和IMP-1的定量HTS（qHTS）实验。新模式将被用于 筛选包含130万种化合物的ChemBridge化学库，并使用Vim-2进行qHTS研究， 将进行IMP-1和NDM-1，以检验虚拟筛选的结果。最后，验证模型 将在我们的MBL抑制剂网站上向公众提供。在《目标2》中，我们将 对我们的模型确定的前500种化合物进行微生物、结构和生化研究， 经QHTS确认。除了最小抑制剂浓度值，我们建议确定 抑制机制并对酶抑制剂复合物进行结构表征。 希望这种机器学习方法将识别新的泛MBL抑制剂和化合物， 可以进一步重新设计。此外，希望这种方法一旦得到发展和测试， 用于识别其他生物医学重要酶的抑制剂。