Protein sequence, structure, and computational analysis

描述（由申请人提供）：拟议的研究将增加 了解蛋白质序列和功能之间的关系， 发展创新的计算和统计技术。的 这种方法旨在最大限度地利用从数据集中提取的信息， 包括对来自不同分类群序列进行密集采样。发展新 快速的基于概率的可能性方法将使研究人员能够 在一定范围和密度下采样的大型多蛋白质数据集的优势 生物多样性，目前是罕见的，但将迅速增加，在不久的将来， 未来在第一阶段，该项目将开发新的计算 方法来分析蛋白质进化和共同进化的模式，建立一个快速的 一种用于分析大型的、分类上多样化的数据集的方法，并评估 使用这种方法的模型近似的实用性和准确性，并开始 开发方法来管理和可视化序列，结构，功能， 系统发育信息来自大型的，分类学上不同的数据集。在 第二阶段，将进一步开发新的计算方法， 蛋白质进化和共同进化的模式，应用分析工具， 广泛的蛋白质和蛋白质复合物，实现计算机程序 采用这些一般社区都能使用的方法， 提供对蛋白质序列生物多样性数据的过滤访问，以便于分析 和可视化。本项目的长期目标是了解 序列多样性和结构之间的关系，以便更准确地 可以预测替代的效果。它将决定 分类多样性在预测功能和结构方面的价值 信息.通过关注接近人类的进化环境（ 脊椎动物），结果将直接适用于了解 人类蛋白质的结构背景和人类蛋白质中取代的影响 可能导致单位点和定量疾病的蛋白质。

DESCRIPTION (provided by the applicant): The proposed research will increase understanding of the relationship between protein sequence and function through development of innovative computational and statistical technologies. The approach is designed to maximize information extracted from datasets that include dense sampling of sequences from diverse taxa. The development of new and fast phylogeny-based likelihood methods will allow researchers to take advantage of large multi-protein datasets sampled over a range and density of biodiversity that is currently uncommon, but will increase rapidly in the near future. In the first phase, the project will develop novel computational methods to analyze patterns of protein evolution and coevolution, create a fast method for analyzing large, taxonomically diverse datasets, and evaluate the utility and accuracy of model approximations using this method, and begin to develop methods to manage and visualize sequence, structure, function, and phylogenetic information from large, taxonomically diverse datasets. In the second phase, it will further develop novel computational methods to analyze patterns of protein evolution and coevolution, apply analytical tools to a broad range of proteins and protein complexes, implement computer programs employing these methods that are accessible to the general community, and provide filtered access to protein sequence biodiversity data for easy analysis and visualization. The long-term goal of this project is to understand the relationship between sequence diversity and structure such that more accurate predictions of the effect of substitution can be made. It will determine the value of taxonomic diversity in predicting functional and structural information. By focusing on the near-human evolutionary environment (the vertebrates), results will be directly applicable towards understanding the structural context of human proteins and the effect of substitutions in human proteins that may lead to both single locus and quantitative disease.