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Algorithms for protein superfamily classification and function prediction

蛋白质超家族分类和功能预测的算法

基本信息

批准号：
7495992
负责人：
Degui Zhi
金额：
$ 7.34万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-15 至 2009-06-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): I am a postdoctoral scholar associated with Steven Brenner's lab at Berkeley, working on structural biology and computational genomics. My long-term vision is to develop new algorithms for inferring protein evolution and function from sequence and structure. Currently I am working on algorithms that can automatically classify a protein into its proper superfamily. The long-term goal of this project is to improve the accuracy of protein structure classification and function prediction. The superfamily defines ancient protein homology. Protein superfamily classification remains a challenging task, even when 3D structure is available. Currently this task still requires experts' manual work. We believe that the classification of protein superfamilies relies on the integration of sequence information and structure information. We will employ recent breakthroughs in kernel-based machine learning approaches for combining different sources of information. We will also develop structure-based discriminative profile models for protein superfamilies. We expect these algorithmic developments will not only result in a practical tool for superfamily classification, but they will also improve our understanding of the interplay of sequence and structure on defining very remote homology. We will extend our structure-based discriminative profile models for protein classification to function prediction. We will develop new methods for the identification of structure-sequence signatures of protein functioin. In addition, we will extend the graph theoretical models for multiple sequence alignment I developed during my Ph.D. study to meet the challenge of domain annotation for large new sequence set. The advancement of medical research is partly based on our detailed understanding of the functions of genes and proteins. My research will improve our understanding of protein evolution and function at the molecular level. Our computational approach will speed up the discovery of biological knowledge from large data sets generated by high-throughput methods.

描述（由申请人提供）：我是伯克利史蒂文·布伦纳实验室的博士后学者，从事结构生物学和计算基因组学研究。我的长期愿景是开发新的算法，从序列和结构推断蛋白质的进化和功能。目前我正在研究可以自动将蛋白质分类到其适当的超家族中的算法。该项目的长期目标是提高蛋白质结构分类和功能预测的准确性。超家族定义了古老的蛋白质同源性。即使 3D 结构可用，蛋白质超家族分类仍然是一项具有挑战性的任务。目前这项工作还需要专家手工完成。我们认为蛋白质超家族的分类依赖于序列信息和结构信息的整合。我们将利用基于内核的机器学习方法的最新突破来组合不同的信息源。我们还将为蛋白质超家族开发基于结构的判别谱模型。我们期望这些算法的发展不仅会成为超家族分类的实用工具，而且还将提高我们对序列和结构在定义非常远的同源性方面的相互作用的理解。我们将把基于结构的蛋白质分类判别模型扩展到功能预测。我们将开发新方法来鉴定蛋白质功能的结构序列特征。此外，我们将扩展我在博士期间开发的多序列比对的图论模型。研究应对大型新序列集领域注释的挑战。医学研究的进步部分基于我们对基因和蛋白质功能的详细了解。我的研究将提高我们对分子水平上蛋白质进化和功能的理解。我们的计算方法将加速从高通量方法生成的大数据集中发现生物知识。