Algorithms for protein superfamily classification and function prediction

该项目的目标是开发新的蛋白质功能预测算法。近期快速 各种技术发展的进步产生了前所未有数量的生物数据， 复杂性计算方法正在成为现代生物医学研究的重要组成部分。 生物信息学家面临的最大挑战之一是发现不同数据集之间的联系 并产生新的生物学知识或假说。预测新蛋白质的分子功能 是后基因组时代的啊当务之急。特别是，最近对结构基因组工作的评估 揭示了实验性蛋白质结构测定和结构测定之间的差距。 在分子水平上了解蛋白质的生物学功能的知识。 我们将采用判别式机器学习方法的最新发展来构建一个 从结构预测功能的残留水平分类系统。现有系统的功能 从结构进行预测或者使用整个蛋白质链上的全局结构和序列相似性， 使用局部相似性，如假定的功能位点。我们的系统将利用这两个信息 全局和局部相似性，并确定重要的残基和残基簇，是独特的 不同功能的家庭。我们的方法是基于和扩展了一个有效的优化 我们为蛋白质超家族分类开发的框架。我们希望这些方法 这些发展不仅将提高最先进的功能预测的性能，而且还有助于 阐明了我们对序列和结构在定义功能变异上的相互作用的理解 在蛋白质家族中。除了这个主要项目之外，我们还将开展一个额外的项目， 我们早期开发的多序列比对的图论模型， 用于大的新序列集的域注释。

The goal of this project is to develop new algorithms for protein function prediction. Recent rapid advancements in various technological developments produce biological data of unprecedented amount and complexity. Computational methods are becoming essential components in modern biomedical research. One of greatest challenges facing bioinformatician is the discovery of connections among different data sets and generating novel biological knowledge or hypotheses. Predicting the molecular function of novel proteins is ah urgent task for the post-genomics era. Especially, recent assessment of structural genomic efforts revealed a gap between experimental protein structure determination and the use ofthe structural knowledge for gaining understanding of biological function of the proteins at the molecular level. We will employ recent developments in discriminative machine learning approaches for constructing a residue-level classification system for function prediction from structure. Existing systems for functional prediction from structure either use global structural and sequence similarities over entire protein chain or use localized similarities such as putative functional sites. Our system will leverage the information from both global and local similarities, and identifies important residues and clusters of residues that are distinctive among different functional families. Our approach is based on and extend over an efficient optimization framework that we developed for protein superfamily classification. We expect that these methodological developments will not only improve the performance of state-of-the-art function prediction, but also help illuminating our understanding ofthe interplay of sequence and structure on defining functional variations among protein families. Beyond this major project, we will work on an additional project that extends the graph theoretical models for multiple sequence alignment we developed earlier to meet the challenge of domain annotation for large new sequence set.