Simulation of Multi-Protein systems

多蛋白系统的模拟

• 批准号: 10798597
• 负责人: Dmytro Kozakov
• 金额: $ 14.39万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10798597
• 关键词: Administrative Supplement; Area; Awareness; Cell physiology; Cells; Collaborations; Communities; Data; Disease; Genomics; Geometry; Goals; Grant; Machine Learning; Methods; Modeling; Molecular; National Institute of General Medical Sciences; Performance; Physics; Proteins; Research; Resources; Running; System; Time; Training; deep learning; deep learning model; deep neural network; effective therapy; learning strategy; neural network architecture; physical process; simulation; therapy development

The main area of research, supported by the grant R01 GM118078, entitled “Simulation of Multi-Protein Systems”, is investigating the interactions between proteins in cellular assemblies. Elucidating such interactions will further mechanistic understanding of cellular processes, and will enable effective therapy development. NIGMS administrative supplement is requested to acquire a high performance GPU cluster to develop physics aware deep learning models . Typically atomic simulations are performed using either physics or machine learning based approaches, with results obtained by the two methods being orthogonal to each other. Instead of a trivial combination of methods, the project goal is to develop special deep neural network architectures, which encode geometry and physics of the underlying system. The inference of such network will be performing realistic physical simulations, with parameters trained on the available data to optimally reproduce experimental observations. Training of such models require significant GPU resources thus justifying the need for the cluster. The new models will be applied to understanding the molecular mechanisms in healthy and diseased cells on the genomics scale. In addition, the results of the research in the Kozakov lab are made available through the macromolecular modeling server ClusPro, a public resource with more than 25000 users running tens of thousands calculation each month. Deploying these methods on the server is also requiring significant GPU resources. Thus the purchased GPU cluster will serve dual goals, first provide training time to develop and apply the new models and then adding to the ClusPro backend.

主要研究领域，由R01 GM118078资助，题为“多蛋白模拟” Systems”正在研究细胞组装中蛋白质之间的相互作用。阐明这种相互作用 相互作用将进一步加深对细胞过程的机械理解，并将实现有效的 疗法的发展。需要 NIGMS 行政补充以获得高性能 GPU 集群用于开发物理感知深度学习模型。通常原子模拟是 使用基于物理或机器学习的方法进行，两者获得的结果 方法彼此正交。项目目标不是简单的方法组合，而是 开发特殊的深度神经网络架构，对几何和物理进行编码 底层系统。这种网络的推理将执行真实的物理模拟，其中 根据可用数据训练参数以最佳地再现实验观察结果。培训 此类模型需要大量 GPU 资源，因此需要集群。新型号 将应用于了解健康和患病细胞的分子机制 基因组学规模。此外，Kozakov 实验室的研究结果也已公开 通过大分子建模服务器 ClusPro，一个拥有超过 25000 个用户的公共资源 每个月运行数万次计算。将这些方法部署在服务器上也是 需要大量 GPU 资源。因此，购买的 GPU 集群将服务于双重目标，首先提供 开发和应用新模型然后添加到 ClusPro 后端的培训时间。

项目成果

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery.

• DOI: 10.1038/s41467-022-31809-z
• 发表时间: 2022-07-13
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Havugimana, Pierre C.;Goel, Raghuveera Kumar;Phanse, Sadhna;Youssef, Ahmed;Padhorny, Dzmitry;Kotelnikov, Sergei;Kozakov, Dima;Emili, Andrew
• 通讯作者: Emili, Andrew