Protein Classification Using Hidden Markov Models

本项目将在NSF支持下发展的隐马尔可夫模型方法扩展应用于寻找蛋白质之间的远程同源性，产生相关蛋白质结构域的多重比对，以及基于共同结构，功能和系统发育关系将蛋白质结构域分类为家族和亚家族。 将mRNA表达数据与序列相似性沿着进行功能分析和发现蛋白质之间相互作用途径的新方法也正在开发中。 更复杂的蛋白质结构模型将提供更好的活性位点识别和远程同源性检测。 在开发用于蛋白质分类和分析的更强大和更全面的计算方法，并使其在万维网上可用时，这项工作将有助于对各种基本蛋白质家族的基础研究，也可能导致发现重要的新蛋白质家族。

This project extends and applies the hidden Markov model methods, which were developed under previous NSF support, to the problems of finding remote homologies between proteins, producing multiple alignments of related protein domains, and classifying protein domains into families and subfamilies based on common structure, function and phylogenetic relationship. New methods of combining mRNA expression data along with sequence similarity to do functional analysis and discover pathways of interactions between proteins are also being developed. More sophisticated models of protein structure will provide better recognition of active sites and better detection of remote homologies. In developing more powerful and comprehensive computational methods for protein classification and analysis, and making them available on the world wide web, this work will contribute to the basic study of a wide variety of fundamental protein families and may also lead to the discovery of important new protein families.