Peptide and Protein Identification via Tandem MS and Mixed-Integer Optimization

通过串联 MS 和混合整数优化进行肽和蛋白质鉴定

基本信息

批准号：
7835817
负责人：
Christodoulos Achilleus Floudas
金额：
$ 25.72万
依托单位：
PRINCETON UNIVERSITY
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：
2007-05-01 至 2013-04-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The problems of peptide identification and protein identification are of fundamental importance in proteomics. We propose to study a new multi-pronged framework for the de novo peptide sequencing and protein identification under uncertainty problems using tandem mass spectroscopy. The proposed approach will be based on fundamental advances in mathematical modeling via mixed integer optimization, as well as theory and algorithms for optimization under uncertainty. We expect that significant advances will be introduced in theory and algorithmic enhancements. We put forward the following four specific aims: Specific Aim 1: Investigate and develop a novel de novo computational approach for the peptide identification based on information of the ion peaks in the peptide spectrum and a mixed-integer optimization modeling and algorithmic framework. Specific Aim 2: Investigate novel de novo methods for the identification of peptides in complex protein mixtures which will account for experimental uncertainty in the calculation of the mass/charge ratios of the ion peaks. Specific Aim 3: Study and develop a new hybrid in silico method which will combine the de novo approach of Specific Aim 1 with database methods for the peptide identification. Specific Aim 4: Investigate and develop a new approach for the protein identification which will combine the advances in Specific Aims 1-3 with database homology based methods. Preliminary studies are reported in Specific Aims 1, 2, and 3 (sections C.1, C.2, D.1, D.2, D.3), and the results, via comparative studies and computational efficiency, demonstrate the potential of the proposed research for high throughput peptide and protein identification.

描述（由申请人提供）：肽鉴定和蛋白质鉴定的问题在蛋白质组学中至关重要。我们建议使用串联质谱研究在不确定性问题下进行新的肽测序和蛋白质鉴定的新的多管齐下框架。所提出的方法将基于通过混合整数优化的数学建模方面的基本进步，以及在不确定性下进行优化的理论和算法。我们预计将在理论和算法增强中引入重大进展。我们提出了以下四个特定目标：特定目的1：调查并开发一种新颖的从头计算方法基于肽光谱中离子峰的信息以及混合智能优化建模和算法框架的识别。特定目的2：研究新颖的从头方法，以鉴定复杂蛋白质中的肽在离子峰的质量/电荷比计算中可以解释实验不确定性的混合物。特定目的3：研究和开发一种新的硅方法混合动力，将特定目标1的从头方法与肽鉴定的数据库方法结合在一起。特定目的4：调查并开发一种蛋白质鉴定的新方法，该方法将结合特定目标1-3中的进步与基于数据库同源性方法的方法。初步研究在特定目标1、2和3（第1章，c.2，d.1，d.2，d.3）和第3节中进行了报道。结果通过比较研究和计算效率，证明了拟议研究对高通量肽和蛋白质鉴定的潜力。