QuasiNovo: An Information Theoretic Approach to De Novo Peptide Sequencing

南卡罗来纳大学获得了一笔赠款，用于开发肽鉴定工具。串联质谱法在高通量蛋白质鉴定中越来越重要。从质谱中识别肽最常用的方法是使用实验谱来查询已知肽的数据库。所有数据库方法的一个主要缺点是它们无法识别数据库中不包含的肽。在微生物肽的情况下，这是一个特别重要的限制。众所周知，在环境中发现的所有微生物中只有1%-10%是可以培养的。因此，有许多细菌以前没有被识别出来。事实上，即使在可培养的生物中，由于极端的多样性，许多生物仍然没有特征。只有从头测序才能提供鉴定新肽的可能性。该项目的目标是在从头肽测序的准确性方面做出重大改进。这将通过在片段化和相互关联肽评分功能中使用氨基酸使用模型的系统研究来完成。初步结果强烈支持这样的假设，即考虑氨基酸使用模式的评分函数将能够更好地区分候选肽。这反过来又会导致肽预测的准确性更高。此外，一个贝叶斯模型探索候选肽的不确定性产生从头测序将被开发。该模型与其他小组开发的模型之间的主要区别在于，所提出的方法将蛋白质组特征的概念作为先验。现有的从头测序模型没有明确表明氨基酸的使用，因此隐含地假设氨基酸使用的平坦先验。拟议的研究将被整合到南卡罗来纳大学计算机科学和统计学的本科和研究生课程中。这项研究还将通过开发培训讲习班来支持培训和教育，这些讲习班将在SC-INBRE生物信息学核心年度全州会议上展示。年度研讨会的目标受众将是南卡罗来纳州生物信息学相关学科（生物学，计算机科学，统计学等）的本科生和研究生以及教职员工。

The University of South Carolina is awarded a grant to develop tools for peptide identification. Tandem mass spectrometry has become increasingly important in high-throughput protein identification. The most popular approach to identifying peptides from mass spectra uses the experimental spectrum to query a database of known peptides. A major weakness of all database approaches is that they are unable to identify peptides that are not contained in the database. This is a particularly important limitation in the case of microbial peptides. It is well known that only 1%-10% of all microbes found in the environment can be cultured. Thus there are many bacteria that have not been previously identified. Indeed, even among culturable organisms many remain uncharacterized due to extreme diversity. Only de novo sequencing offers the possibility of identifying novel peptides.The goal of this project is to make significant improvements in the accuracy of de novo peptide sequencing. This will be accomplished through a systematic study of the use of amino acid usage models in fragmentation and cross-correlation peptide scoring functions. Preliminary results strongly support the hypothesis that a scoring function that considers amino acid usage patterns will be better able to distinguish between candidate peptides. This in turn will lead to much higher accuracy in peptide prediction. In addition, a Bayesian model for exploring the uncertainty of candidate peptides produced by de novo sequencing will be developed. A major difference between this model with that developed by other groups is that the proposed approach will incorporate the concept of proteome signature as a prior. Existing models for de novo sequencing do not expressly indicate amino acid usage, and thus implicitly assume flat priors for amino acid usage. The proposed research will be integrated into undergraduate and graduate courses taught in computer science and statistics at the University of South Carolina. The research will also be leveraged to support training and education through the development of training workshops to be presented at annual SC-INBRE Bioinformatics Core statewide meetings. The target audience of the annual workshops will be undergraduate and graduate students as well as faculty and staff in bioinformatics-related disciplines (biology, computer science, statistics, etc.) in South Carolina.