Computational Tools to Characterize the Effects of Protein and RNA Variability in Function and Interactions

表征蛋白质和 RNA 变异对功能和相互作用的影响的计算工具

• 批准号: 10387914
• 负责人: Alonso Faruck Morcos
• 金额: $ 6.18万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10387914
• 关键词: Administrative Supplement; Antibiotic Resistance; Attention; Computer Models; Development; Disease; Engineering; Funding; Genetic Transcription; Goals; Hybrids; Linear Algebra; Machine Learning; Memory; Modeling; Molecular; Mutation; Parents; Process; Proteins; RNA; RNA-Protein Interaction; Research; Scientific Advances and Accomplishments; Specificity; Statistical Models; System; Technology; base; computerized tools; design; improved; molecular dynamics; parent grant; parent project; programs; sensor; simulation; statistics

Project Summary The scientific goals of the funded parent project of this proposal (R35 MIRA for ESI) include the development of global probabilistic and computational models of biomolecules that characterize and quantify the landscape of protein variability and their interactions. The ultimate goal is to elucidate the landscape of functional mutations, which is hidden within the much larger non-functional space. We are using this functional landscape to engineer hybrid transcriptional regulators as well as to predict specificity networks in two-component systems. Another important goal is to devise models to characterize the sequence dependance on protein-protein and protein-RNA interactions. These models will allow us to encode and predict recognition from inferred landscapes and to integrate our results with experimental technologies. Devising the spectrum of functional biomolecular variability sculpted by evolutionary processes will be used to estimate the effects of mutations in disease, antibiotic resistance, biomolecular sensor design and the impact of sequence composition on interaction networks. As the research program of the parent grant benefits from generative modeling and atomistic molecular simulations, we have turned our attention to build a framework that will produce more accurate estimation of the sequence statistics of biomolecules and perform molecular dynamics in an efficient way. For this reason, we request additional funds to build a new Graphical Processing Unit (GPU) system based on NVIDIA A100 technology and large memory CPU nodes. This technology will accelerate the goals of the parent research program in problems related to machine learning, biomolecular simulation and linear algebra calculations.

项目摘要 这项提案的资助父项目(ESI的35Mira)的科学目标包括 表征和量化生物分子的全球概率和计算模型的发展 蛋白质可变性及其相互作用的图景。最终的目标是阐明 功能突变，隐藏在更大的非功能空间中。我们正在使用这个 设计杂交转录调控因子以及预测特异性网络的功能格局 双组分系统。另一个重要的目标是设计模型来表征序列依赖性 蛋白质-蛋白质和蛋白质-RNA相互作用。这些模型将允许我们进行编码和预测 从推断的景观中识别，并将我们的结果与实验技术相结合。设计出了 进化过程塑造的功能生物分子可变性的光谱将被用来估计 突变对疾病、抗生素耐药性、生物分子传感器设计的影响以及序列的影响 互动网络上的组合。作为父母资助的研究项目受益于生成性 建模和原子分子模拟，我们已经将注意力转向建立一个框架，它将 更准确地估计生物分子的序列统计量，并执行分子动力学 以一种有效的方式。出于这个原因，我们请求额外资金来建造一个新的图形处理器 基于NVIDIA A100技术和大内存CPU节点的(GPU)系统。这项技术将 在与机器学习、生物分子相关的问题上加速母公司研究计划的目标 模拟和线性代数计算。