Statistical methods for higher order dependences to understand protein functions

用于了解蛋白质功能的高阶依赖性统计方法

• 批准号: 10492723
• 负责人: Wen Zhou
• 金额: $ 20.33万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10492723
• 关键词: Algorithms; Amino Acid Sequence; Amino Acids; Biological; Cells; Characteristics; Complex; Data; Data Set; Databases; Dependence; Drug Design; Measures; Methods; Modeling; Molecular; Molecular Structure; Outcome; Physical environment; Prognosis; Protein Dynamics; Proteins; Public Health; Science; Statistical Methods; Structure; System; Uncertainty; amino group; base; computer framework; discrete data; genomic data; improved; novel; novel strategies; protein function; protein structure; statistics; web site

This proposal brings together a strong team from molecular science and statistics to tackle the important problem of how to integrate protein structure and sequence information in complex systems. Some of the most important characteristics of these data are the strong correlations buried within them, with the pairwise correlations in the sequence data already being routinely used to predict structural contacts. Here, we are developing novel ways to use huge data sets to extract higher-order dependences, which are now possible with the availability of the large volumes of sequence data from genomics; and in addition, in the molecular structures such higher-order dependences are directly observable in the protein structures where groups of amino acids interact directly. Importantly, these higher-order dependences reflect the dense physical environment in the cell that requires for proper statistical characterization. A new model free information-theoretic measure is introduced to quantify the higher-order dependences, which serves as the central method in this project. By identifying the major challenges in drawing statistical inference based on this measure, we develop, evaluate, and improve a new statistical inference and computational framework for analyses of higher-order dependences with discrete data of a general type, motivated by the protein multiple sequence data. The new computationally efficient framework makes it possible to discover reliable higher-order dependences with the ability of quantifying uncertainty. The preliminary data here combine the information from sequences and structures to yield unexpected results that immediately relate to the dynamics of the protein structures. The outcome is an entirely new approach to handle the large volumes of protein sequence data and other omics data now available and the enormous volumes about to arrive on the doorsteps of omics analysts.

该提案汇集了一个来自分子科学和统计数据的强大团队，以应对重要的团队 如何在复杂系统中整合蛋白质结构和序列信息的问题。一些 这些数据的最重要特征是埋在其中的强相关性，与 序列数据中的成对相关性已经通常用于预测结构接触。这里， 我们正在开发新颖的方法来使用巨大的数据集来提取高阶依赖性，而现在 可能会从基因组学中获得大量序列数据的可用性；此外，在 分子结构在蛋白质结构中可以直接观察到这种高阶依赖性 氨基酸组直接相互作用。重要的是，这些高阶依赖性反映了密集 细胞中需要正确统计表征的物理环境。免费的新型号 引入了信息理论措施以量化高阶依赖性，这作为 该项目的中心方法。通过确定基于统计推断的主要挑战 我们开发，评估和改进了新的统计推断和计算框架 用于用一般类型的离散数据的高阶依赖分析，由蛋白质促进 多个序列数据。新的计算高效框架使发现可靠 高阶依赖性具有量化不确定性的能力。这里的初步数据结合了 来自序列和结构的信息，产生意外的结果，这些结果立即与 蛋白质结构的动力学。结果是处理大量大量的全新方法 现在可用的蛋白质序列数据和其他OMIC数据以及即将到达的大量卷 OMICS分析师的大门。