Computational approaches to protein identification and quantification using MS/MS

使用 MS/MS 进行蛋白质鉴定和定量的计算方法

• 批准号: 7387128
• 负责人: Predrag Radivojac
• 金额: $ 26.77万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7387128
• 关键词: Accounting; Address; Algorithms; Amino Acid Sequence; Analytical Chemistry; Biological Markers; Caring; Chemicals; Computer software; Computing Methodologies; Condition; Coupled; Data; Data Analyses; Data Set; Databases; Detection; Development; Diagnosis; Digestion; Disease; Disease Progression; Disease regression; Endopeptidases; Goals; Health; Informatics; Ions; Knowledge; Label; Learning; Liquid Chromatography; Machine Learning; Measures; Methods; Numerical value; Online Systems; Output; Peptide Hydrolases; Peptide Library; Peptide Sequence Determination; Peptides; Pharmaceutical Preparations; Probability; Problem Formulations; Protein Databases; Proteins; Proteome; Proteomics; Relative (related person); Research Personnel; Sampling; Scheme; Score; Shotguns; Software Tools; Solutions; Staging; Standards of Weights and Measures; Structure; Techniques; Technology; Testing; Tissues; Training; Trypsin; Work; analytical tool; base; computerized tools; concept; design; improved; open source; programs; research study; tandem mass spectrometry; tool; treatment effect

DESCRIPTION (provided by applicant): Shotgun proteomics is one of the most commonly used approaches to MS-based biomarker discovery, due to its high throughput and sensitivity. The general strategy involves simultaneous protease digestion of all proteins in a mixture, liquid chromatography-based separation of peptides and analysis by tandem mass spectrometry (MS/MS) to produce fragmentation spectra of each peptide. Each experimental spectrum is searched against a protein database. Sequences that best match the experimental spectra are considered identified, while a set of reliably identified peptides from the same protein is necessary for a reliable protein identification. The main goal in the proposed work is to generate and interrogate MS/MS data from several proteomics platforms, including ESI/MS, MALDI/TOF/TOF, LC-IMS/TOF and MALDI-PID/TOF to develop customized computational tools that address several challenging problems in shotgun proteomics data analysis: peptide identification, protein identification and label-free protein quantification. Our proposed approach is data-driven. At its core is the application of machine learning methods to the prediction of peptide fragmentation spectra as well as the likelihood of peptide detection in a typical proteomics experiment. Improved peptide identification coupled with the predicted peptide delectability will then be used to develop new methods for improved protein identification and quantification. The methods proposed herein will be extensively evaluated and software will be made public both as web-based tools and open-source deliverables. These software tools will enable researchers using proteomics technologies to more effectively and efficiently study a variety of health related conditions. Such studies might entail disease diagnosis (biomarker discovery), disease progression (tissue profiling), or effects of treatment (drug-induced proteome changes). These studies will enhance understanding of diseases and hasten the development of effective treatments and cures. In addition, these tools will be useful in characterizing new analytical tools for proteome analysis. Here we propose to develop and extensively evaluate computational methodology that will be used to improve the interpretation of tandem mass spectrometry data. These software tools will enable researchers using proteomics technologies to more effectively and efficiently study a variety of health related conditions. Such studies that might entail disease diagnosis, disease progression, or effects of treatment, will enhance understanding of diseases and hasten the development of effective treatments and cures.

描述(申请人提供)：鸟枪蛋白质组学是最常用的MS生物标记物发现方法之一，因为它的高通量和敏感性。一般的策略包括同时消化混合物中的所有蛋白质，基于液相色谱的多肽分离，并通过串联质谱仪(MS/MS)分析产生每个多肽的裂解光谱。每个实验光谱都根据蛋白质数据库进行搜索。与实验光谱最匹配的序列被认为是被识别的，而来自同一蛋白质的一组可靠识别的多肽是可靠的蛋白质识别所必需的。拟议工作的主要目标是生成和询问来自几个蛋白质组平台的MS/MS数据，包括ESI/MS、MALDI/TOF/TOF、LC-IMS/TOF和MALDI-PID/TOF，以开发定制的计算工具，以解决鸟枪式蛋白质组数据分析中的几个具有挑战性的问题：肽鉴定、蛋白质鉴定和无标记蛋白质定量。我们建议的方法是数据驱动的。其核心是将机器学习方法应用于预测多肽碎片光谱以及在典型蛋白质组学实验中检测多肽的可能性。改进的多肽鉴定与预测的多肽可口性相结合，然后将用于开发改进的蛋白质鉴定和定量的新方法。将对本文提出的方法进行广泛评估，并将软件作为基于网络的工具和开放源码交付成果予以公布。这些软件工具将使使用蛋白质组学技术的研究人员能够更有效和高效地研究各种与健康相关的疾病。这样的研究可能涉及疾病诊断(生物标记物的发现)、疾病进展(组织图谱)或治疗效果(药物诱导的蛋白质组变化)。这些研究将增进对疾病的了解，并加速开发有效的治疗方法和治疗方法。此外，这些工具将有助于确定蛋白质组分析的新分析工具的特征。在这里，我们建议开发和广泛评估计算方法，将用于改善串联质谱分析数据的解释。这些软件工具将使使用蛋白质组学技术的研究人员能够更有效和高效地研究各种与健康相关的疾病。这类研究可能涉及疾病诊断、疾病进展或治疗效果，将增进对疾病的了解，并加速开发有效的治疗方法和治疗方法。