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.

主要研究领域得到了R01 GM118078的资助，该基金的标题是《多蛋白质的模拟 系统“，正在研究细胞组装中蛋白质之间的相互作用。澄清这样的情况 相互作用将加深对细胞过程的机械性理解，并将使有效的 治疗的发展。需要NIGMS管理补充以获得高性能 开发物理感知深度学习模型的GPU集群。典型的原子模拟是 使用基于物理或基于机器学习的方法进行，结果由这两种方法获得 方法是相互正交的。项目目标不是简单的方法组合，而是 为了开发特殊的深度神经网络结构，它编码了 底层系统。这种网络推理将执行真实的物理模拟，具有 对现有数据进行参数训练，以最佳地再现实验观测。培训 这类模型需要大量的GPU资源，因此有理由需要集群。新车型 将被用于理解健康和疾病细胞中的分子机制 基因组学规模。此外，科扎科夫实验室的研究结果也已公布 通过高分子建模服务器ClusPro，一个拥有超过25000用户的公共资源 每个月运行数万次计算。在服务器上部署这些方法也是 需要大量的GPU资源。因此，购买的GPU集群将服务于双重目标，首先提供 培训时间来开发和应用新模型，然后添加到ClusPro后端。

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