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.

赠款R01 GM118078支持的主要研究领域，标题为“多蛋白的模拟” 阐明这种系统”正在研究细胞组件中的相互作用。 相互作用将进一步了解细胞过程，并有效 治疗开发。 GPU群集开发物理学意识到的深度学习模型。 使用基于物理学或基于机器学习的方法进行，并获得两者获得的结果 方法是彼此正交的。 开发特殊的深层神经网络架构，该结构编码了几何和物理学 基础系统。 对可用数据训练的参数以最佳的繁殖实验观测。 这样的模型需要大量的GPU资源，因此需要新模型 将应用于了解健康和患病细胞中的分子机制 基因组学量表。 通过大分子建模服务器ClusPro，这是一种拥有25000多名用户的公共资源 每月运行数十万个计算。 需要大量的GPU资源。 培训时间以开发和应用新模型以及添加到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