Structural and Functional Property Integration for Protein Sequence Feature Representations to Enable Advanced Machine Learning Remote Homology Detection

通过成对序列比对确定同源性是众所周知的不敏感，因为许多具有相似结构和功能的蛋白质通常只有8-10%的序列同一性。远低于常规方法所需的检测阈值。已经表明，通过将蛋白质序列转化为与序列和结构相关的性质的载体，可以显着改善发现这些远程同源物（具有低序列同一性的蛋白质）的任务。然而，现有的基于特征的方法仅限于将蛋白质分类到一个家族中，这意味着蛋白质不能被分类，除非它们落入预定义的家族中。通过评估成对相似性来克服这一警告的方法主要依赖于网络传播，因为成对训练需要非常大的训练空间。例如，4000个蛋白质的小基准数据集相当于超过800万对。不幸的是，这些网络传播方法已经证明了对最先进的PSI-BLAST方法的边际改进。这在很大程度上是因为（1）仅使用少量有限数量的特征，以及（2）网络传播方法对从BLAST分数导出的相似性网络的潜在依赖。因此，使用统计鉴别方法来回答成对问题在同源性检测领域仍然遥不可及。这种限制是大规模基因组测序的严重技术差距，因为如果没有高度可靠的同源性检测，自动注释是不可能的。 生物驱动的集成蛋白质特征表示的发展将显着提高远程同源性检测的任务。此外，SVM的使用，它只需要一个线性计算的分类任务，将提供一个快速的计算时间。这两个组成部分？更快的序列比较和更高的灵敏度？将打破远程同源性检测领域中长期存在的时间/灵敏度范例。所提出的成对SVM实现也可以应用于其他大型真实的世界不同的科学和工程问题，其特征在于通过关联分类。 PI已经与WSU联合任命了一名教师，目前正在担任两名学生的委员会成员。对于拟议的工作，一个额外的博士学位。WSU计算机科学系的一名研究生将对拟议项目的组成部分进行论文工作，使她能够亲自使用独特的超级计算设施。

The determination of homology by pair-wise sequence alignments is notoriously insensitive because many proteins with similar structure and function often have only 8-10% sequence identity ? well below the detection threshold required for conventional methods. It has been shown that by transforming protein sequences into vectors of properties associated with sequence and structure can significantly improve the task of finding these remote homologues (proteins with low sequence identity). However, existing feature-based methods are limited to classifying a protein into a family, which means that proteins cannot be classified unless they fall into a pre-defined family. Methods devised to overcome this caveat by assessing pair-wise similarity have primarily relied on network propagation because of the extremely large training space needed for pair-wise training. For example, a small benchmark dataset of 4000 proteins equates to over 8 million pairs. Unfortunately, these network propagation methods have demonstrated only marginal improvement over the state-of-the-art PSI-BLAST method. This is largely because (1) only a small limited number of features are used and (2) the underlying reliance of the network propagation method to a similarity network derived from BLAST scores. Thus, the use of statistical discrimination methods to answer the pair-wise question has remained beyond reach in the homology detection field. This limitation is a serious technological gap for large-scale genome sequencing since automated annotation is not possible without highly reliable homology detection. The development of a biologically-driven integrated protein feature representation will significantly improve the task of remote homology detection. Additionally, the use of a SVM, which only requires a linear computation for the classification task, will offer a fast computation time. These two components ? faster sequence comparisons and improved sensitivity ? will break a long standing time/sensitivity paradigm in the field of remote homology detection. The proposed pair-wise SVM implementation can also be applied to other large real - world diverse science and engineering problems characterized by classification through association. The PI already has a joint faculty appointment to WSU and is currently serving as a committee member for two students. For the proposed work, one additional Ph.D. graduate student from the WSU computer science department will perform thesis work on components of the proposed project, giving her hands-on access to unique supercomputing facilities.