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

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，多年的 研究，以及数百万美元，以确定适合临床试验的临床前候选药物。成立为法团 将计算工具引入药物发现已被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是非常有益的。 研究蛋白质-配体络合物结构和热力学性质及优化药物的工具 提高与感兴趣目标的结合亲和力的候选者。Lambda Dynamic(LD)，一种较新的无炼金术 能量法，便于对化学体系的多个扰动进行采样，同时，在 单分子动力学模拟，克服了与传统方法相关的固有可扩展性限制 自由能方法。到目前为止，各种化学扰动，包括不同的配体官能团 利用(LD)对单个化学实体进行转化和蛋白质侧链突变， 例如，一个小分子或蛋白质，取得了很大的成功。数十到数百个化学态已经被有效地 与传统方法相比，采样使用的计算资源少了一个数量级。这 提案寻求支持以这些发现为基础，并应用基于LD的技术来探索多方面 药物发现中的设计问题，以多个结合伙伴上的化学修饰为特征。具体来说， 将研究药物发现的三个具有挑战性的领域：(1)了解和克服耐药性 起源于药物靶标中的错义突变，(2)表征蛋白质-蛋白质相互作用和结合 特异性，以及(3)通过集成 使用机器或深度学习算法的LD计算。要想在这些努力中取得成功，就需要不断探索 通过大型组合化学空间，这只能用基于LD的技术来完成。型号 多发性骨髓瘤或阿尔茨海默病的高度治疗重要性的蛋白质靶系统将是 在实现我们的目标方面进行了调查。因此，这项工作将有助于加速基于临床前结构的 药物设计通过使复杂的分子设计方案能够在这些毁灭性的疾病中得到解决。

项目成果

Identification of nonhistone substrates of the lysine methyltransferase PRDM9.

• DOI: 10.1016/j.jbc.2023.104651
• 发表时间: 2023-05
• 期刊: JOURNAL OF BIOLOGICAL CHEMISTRY
• 影响因子: 4.8
• 作者: Hanquier, Jocelyne N.;Sanders, Kenidi;Berryhill, Christine A.;Sahoo, Firoj K.;Hudmon, Andy;Vilseck, Jonah Z.;Cornett, Evan M.
• 通讯作者: Cornett, Evan M.

Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling.

• DOI: 10.1038/s41467-023-44208-9
• 发表时间: 2023-12-21
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Robo, Michael T.;Hayes, Ryan L.;Ding, Xinqiang;Pulawski, Brian;Vilseck, Jonah Z.
• 通讯作者: Vilseck, Jonah Z.