Tackling Multifaceted Drug Design Problems with Lambda Dynamics Based Technologies

利用基于 Lambda Dynamics 的技术解决多方面的药物设计问题

• 批准号: 10709879
• 负责人: JONAH VILSECK
• 金额: $ 38.33万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-24 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709879
• 关键词: Acceleration; Address; Affinity; Algorithms; Alzheimer' s Disease; Area; Artificial Intelligence; Binding; Binding Proteins; Chemicals; Clinical; Complex; Coupled; Development; Disease; Drug Design; Drug Targeting; Drug resistance; Free Energy; Generations; Goals; Lead; Ligands; Machine Learning; Methods; Missense Mutation; Modeling; Modernization; Modification; Molecular; Multiple Myeloma; Mutation; Process; Property; Proteins; Research; Research Proposals; Sampling; Scientist; Side; Specificity; Structure; System; Techniques; Technology; Testing; Therapeutic; Thermodynamics; Work; analog; combinatorial; computerized tools; computing resources; cost; deep learning algorithm; design; drug candidate; drug discovery; functional group; guided inquiry; improved; insight; interest; lead optimization; molecular dynamics; novel; pre-clinical; protein protein interaction; simulation; small molecule; success; therapeutic protein; tool; Acceleration; Address; Affinity; Algorithms; Alzheimer' s Disease; Area; Artificial Intelligence; Binding; Binding Proteins; Chemicals; Clinical; Complex; Coupled; Development; Disease; Drug Design; Drug Targeting; Drug resistance; Free Energy; Generations; Goals; Lead; Ligands; Machine Learning; Methods; Missense Mutation; Modeling; Modernization; Modification; Molecular; Multiple Myeloma; Mutation; Process; Property; Proteins; Research; Research Proposals; Sampling; Scientist; Side; Specificity; Structure; System; Techniques; Technology; Testing; Therapeutic; Thermodynamics; Work; analog; combinatorial; computerized tools; computing resources; cost; deep learning algorithm; design; drug candidate; drug discovery; functional group; guided inquiry; improved; insight; interest; lead optimization; molecular dynamics; novel; pre-clinical; protein protein interaction; simulation; small molecule; success; therapeutic protein; tool

Project Summary Modern day drug discovery is a long and expensive process requiring teams of scientists, multiple years of research, and millions of dollars to identify preclinical drug candidates suitable for clinical tests. The incorporation of computational tools into drug discovery has proved an effective means to reduce these costs. All-atom molecular dynamics simulations coupled with alchemical free energy calculations have been extremely beneficial tools for studying structural and thermodynamic properties of protein-ligand complexes and optimizing drug candidates for improved binding affinity to a target of interest. Lambda dynamics (LD), a newer alchemical free energy method, facilitates the sampling of multiple perturbations to a chemical system, simultaneously, within a single molecular dynamics simulation, overcoming inherent scalability limitations associated with conventional free energy methods. To date, a variety of chemical perturbations, including diverse ligand functional group transformations and protein side chain mutations, have been performed with (LD) on a single chemical entity, e.g., a small molecule or protein, with much success. Tens to hundreds of chemical states have been efficiently sampled using an order of magnitude less computational resources compared to conventional methods. This proposal seeks support to build upon these findings and apply LD-based techniques to explore multifaceted design problems in drug discovery featuring chemical modifications on multiple binding partners. Specifically, three challenging areas of drug discovery will be investigated: (1) understanding and overcoming drug resistance originating from missense mutations in a drug target, (2) characterizing protein-protein interactions and binding specificities, and (3) automating the generation of novel, target-specific lead compound analogs by integrating LD calculations with machine- or deep-learning algorithms. Success in these efforts will require searching through large combinatorial chemical spaces that can only be accomplished with LD-based techniques. Model protein-target systems of high therapeutic importance from Multiple Myeloma or Alzheimer’s Disease will be investigated in accomplishing our goals. Thus, this work will assist in accelerating preclinical structure-based drug design by enabling complex molecular design scenarios to be addressed in these devastating diseases.

项目摘要 现代药物发现是一个漫长而昂贵的过程，需要科学家团队，多年 研究和数百万美元来识别适合临床测试的临床前药物候选者。工作 药物发现中的计算工具已证明是降低这些成本的有效手段。全原子 分子动力学模拟与炼金术自由能计算相结合非常有益 研究蛋白质配体配合物的结构和热力学特性的工具并优化药物 候选人提高了对目标目标的结合亲和力。 Lambda Dynamics（LD），新的无酒精免费 能量法，促进了对化学系统的多种扰动的采样，只是在 单分子动力学模拟，克服与常规的固有可伸缩性限制 自由能方法。迄今为止，包括潜水配体官能团在内的各种化学扰动 转化和蛋白质侧链突变已在单个化学实体上使用（LD）进行， 例如，一个小分子或蛋白质，取得了很大的成功。数十个到数百个化学状态有效 与常规方法相比，使用数量级的计算资源较少采样。这 建议寻求支持以基于这些发现并应用基于LD的技术来探索多方面的技术 药物发现中的设计问题，具有多个结合伴侣的化学修饰。具体来说， 将研究药物发现的三个挑战领域：（1）理解和克服耐药性 起源于药物靶标的错义突变，（2）表征蛋白质 - 蛋白质相互作用和结合 规格，以及（3）通过集成而自动化新颖的，特定目标的铅化合物类似物 LD通过机器或深度学习算法计算。这些努力的成功将需要搜索 通过只能通过基于LD的技术完成的大型组合化学空间。模型 来自多发性骨髓瘤或阿尔茨海默氏病具有高治疗重要性的蛋白质目标系统将是 在实现我们的目标时进行了调查。这项工作将有助于加速基于临床前结构 在这些破坏性疾病中解决复杂的分子设计方案来设计药物设计。