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

项目摘要 本提案资助的母项目（ESI的R35 MIRA）的科学目标包括： 开发生物分子的全球概率和计算模型， 蛋白质变异性及其相互作用的景观。最终目标是阐明 功能突变，隐藏在更大的非功能空间中。我们正在使用这 功能景观工程杂交转录调节以及预测特异性网络， 双组分体系另一个重要的目标是设计模型来描述序列依赖性 蛋白质与蛋白质以及蛋白质与RNA的相互作用。这些模型将使我们能够编码和预测 从推断的景观识别，并将我们的结果与实验技术相结合。厘定 由进化过程塑造的功能性生物分子变异性的光谱将用于估计 突变对疾病的影响，抗生素耐药性，生物分子传感器设计和序列的影响 交互网络上的组合。由于父母补助金的研究计划受益于生殖 建模和原子分子模拟，我们已经把注意力转向建立一个框架， 对生物分子的序列统计产生更准确的估计，并执行分子动力学 以有效的方式。因此，我们要求追加资金，以建立一个新的图形处理股 (GPU)系统基于NVIDIA A100技术和大内存CPU节点。这项技术将 加速与机器学习，生物分子相关的问题的父研究计划的目标 模拟和线性代数计算。

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.