Deep-learning methods based computational modeling

基于深度学习方法的计算建模

• 批准号: 10816248
• 负责人: Juan Du
• 金额: $ 5万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10816248
• 关键词: Administrative Supplement; Artificial Intelligence; Calcium; Cations; Complement; Computational Biology; Computer Models; Funding; Future; Methods; Molecular Conformation; National Institute of Neurological Disorders and Stroke; Research Project Grants; Running; TRP channel; computing resources; deep learning; learning strategy; protein structure prediction

Project summary Recent advancements in deep learning-based computational protein structure prediction by AlphaFold, RoseTTAFold, ESMFold, and OpenFold methods offer promising opportunities to advance our ongoing NINDS-funded research project entitled “Activation and Inhibition Mechanisms of Calcium-Activated Nonselective Cation Channels” (1R01NS128180). We plan to use current and future deep learning-based computational protein structure prediction methods to predict gating conformational changes in TRP channels with high accuracy to complement our functional studies. We aim to capitalize on these opportunities with this administrative supplement. To fully utilize the extensive capabilities of deep learning-based computational protein structure prediction method capabilities, powerful computational resources are needed with advanced GPU, CPU, RAM, and disk capacity. We are requesting $50,000 NINDS supplement funding to purchase an AI system capable of running all current and future deep learning-based computational protein structure prediction methods. We have identified the ideal AI system configuration from Bizon Technostore. A quote is submitted together with this application.

项目摘要 基于深度学习的计算蛋白质结构预测的最新进展 AlphaFold、RoseTTAFold、ESMFold和OpenFold方法提供了有希望的机会， 推进我们正在进行的NINDS资助的研究项目，题为“激活和抑制 钙激活的非选择性阳离子通道的机制”（1R01NS128180）。我们计划 使用当前和未来基于深度学习的计算蛋白质结构预测方法 以高精度预测TRP通道的门控构象变化， 我们的功能研究。我们的目标是利用这些机会， 补充.为了充分利用基于深度学习的计算能力， 蛋白质结构预测方法的能力，需要强大的计算资源 拥有先进的GPU、CPU、RAM和磁盘容量。 我们正在申请50，000美元的NINDS补充资金，以购买能够 运行所有当前和未来基于深度学习的计算蛋白质结构预测 方法.我们已经从Bizon Technostore中确定了理想的AI系统配置。报价 与本申请书一并提交。

项目成果

Gain-of-function of TRPM4 predisposes mice to psoriasiform dermatitis.

• DOI: 10.3389/fimmu.2022.1025499
• 发表时间: 2022
• 期刊: Frontiers in immunology
• 影响因子: 7.3