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.

这项提案汇集了来自分子科学和统计学的强大团队，以解决重要的 如何在复杂系统中整合蛋白质结构和序列信息。一些 这些数据最重要的特征是隐藏在其中的强相关性， 序列数据中的成对相关性已经被常规用于预测结构接触。在这里， 我们正在开发新的方法来使用巨大的数据集来提取高阶依赖关系， 可能与来自基因组学的大量序列数据的可用性有关;此外， 在蛋白质结构中可以直接观察到这种高阶依赖性， 氨基酸基团直接相互作用。重要的是，这些高阶依赖关系反映了 细胞中需要适当统计特征的物理环境。免费的新模型 引入信息论测度来量化高阶相关性， 在这个项目中的核心方法。通过确定基于以下方面进行统计推断的主要挑战， 通过这一措施，我们开发，评估和改进了一个新的统计推断和计算框架 用于分析高阶依赖与一般类型的离散数据，由蛋白质激发 多序列数据。新的计算效率框架使得发现可靠的 具有量化不确定性能力的高阶相关性。这里的初步数据结合了联合收割机 从序列和结构中获取信息，以产生与 蛋白质结构的动力学。其结果是一个全新的方法来处理大量的 大量的蛋白质序列数据和其他组学数据， 经济学分析师的大门