Computational approaches to protein identification and quantification using MS/MS

描述（由申请人提供）：霰弹枪蛋白质组学是基于ms的生物标志物发现最常用的方法之一，由于其高通量和灵敏度。一般策略包括同时蛋白酶消化混合物中的所有蛋白质，基于液相色谱的肽分离和串联质谱（MS/MS）分析，以产生每个肽的片段谱。每个实验光谱都是根据蛋白质数据库进行搜索的。与实验光谱最匹配的序列被认为是鉴定的，而来自同一蛋白质的一组可靠鉴定的肽对于可靠的蛋白质鉴定是必要的。该工作的主要目标是生成和查询来自多个蛋白质组学平台的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，解决shotgun蛋白质组学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。我们提出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肽片段谱，以及在典型蛋白质组学实验中检测肽的可能性。改进的多肽鉴定加上预测的多肽可选性将用于开发改进的蛋白质鉴定和定量的新方法。本文提出的方法将被广泛评估，软件将作为基于网络的工具和开源交付物公开。这些软件工具将使研究人员使用蛋白质组学技术更有效和高效地研究各种健康相关疾病。这些研究可能涉及疾病诊断（生物标志物发现）、疾病进展（组织分析）或治疗效果（药物诱导的蛋白质组改变）。这些研究将增进对疾病的了解，并加快有效治疗方法的发展。此外，这些工具将有助于描述蛋白质组分析的新分析工具。在这里，我们建议发展和广泛评估计算方法，将用于改善串联质谱数据的解释。这些软件工具将使研究人员使用蛋白质组学技术更有效和高效地研究各种健康相关疾病。这些可能涉及疾病诊断、疾病进展或治疗效果的研究，将增进对疾病的了解，并加速开发有效的治疗和治愈方法。

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.