Protein sequence, structure, and computational analysis

描述(由申请人提供)：建议的研究将增加 通过对蛋白质序列与功能关系的认识 发展创新的计算和统计技术。这个 方法旨在最大化从数据集中提取的信息，这些数据集 包括对来自不同分类群的序列进行密集采样。新技术的发展 而基于系统发育的快速似然方法将使研究人员能够 在一定范围和密度范围内采样的大型多蛋白质数据集的优势 生物多样性目前并不常见，但在不久的将来将迅速增加 未来。在第一阶段，该项目将开发新的计算 方法分析蛋白质进化和协同进化的模式，创建快速 用于分析不同分类的大型数据集，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、功能和 来自不同分类的大型数据集的系统发育信息。在 第二阶段，它将进一步开发新的计算方法来分析 蛋白质进化和共同进化的模式，将分析工具应用于 广泛的蛋白质和蛋白质复合体，执行计算机程序 采用这些对一般社区可用的方法，以及 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 和视觉化。该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 可以对替代的效果进行预测。它将决定 分类多样性在功能和结构预测中的价值 信息。通过关注接近人类的进化环境( 脊椎动物)，结果将直接适用于理解 人类蛋白质的结构背景及其在人体内的替代效应 既可能导致单基因座疾病又可能导致定量疾病的蛋白质。

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.