A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition

使用数据独立采集进行元蛋白质组学蛋白质鉴定和定量的计算框架

基本信息

批准号：
10047086
负责人：
Xuan Guo
金额：
$ 36.13万
依托单位：
UNIVERSITY OF NORTH TEXAS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10047086
关键词：
Address Affect Amino Acid Substitution Binding Bioinformatics Biological Sciences Biology Clinic Complex Computational Biology Data Detection Development Diabetes Mellitus Disease Educational Curriculum Frequencies Genome Grain Health Heterogeneity Homologous Protein Human Human Microbiome Hybrids Immune system In Situ Inflammatory Bowel Diseases Knowledge Lead Libraries Linear Programming Mass Spectrum Analysis Measurement Medical Metabolic Methodology Methods Microbe Modeling Modernization Nature Obesity Organism Outcome Peptides Physiological Physiology Play Positioning Attribute Process Protein Databases Proteins Proteomics Reporting Reproducibility Research Research Activity Role Sampling Science Shotguns Techniques Training Variant bacterial community base bioinformatics tool career computer framework computer science computerized tools data acquisition design dysbiosis experimental study graduate student host microbiome host-microbe interactions human microbiota improved insight mass spectrometer metaproteomics microbial microbial community microbiome microbiome analysis microbiome research microbiota microorganism novel novel diagnostics novel therapeutics pathogen programs stem success theories transcriptome sequencing undergraduate student

项目摘要

Summary Extensive efforts to characterize the human microbiome have tremendously increased the knowledge about the diversity of the microbiome and about its composition in health and in disease. Dysbiosis in human microbiota underlies the development of many diseases, such as obesity, diabetes, and inflammatory bowel disease. Metaproteomics based on mass spectrometry (MS) has become widely used in microbiome research for gaining insights into the functional states of microbial communities. Mass spectrometry with data-dependent acquisition (DDA) is the most common method of choice for identifying and quantifying microbial proteins in metaproteomics, but this technique is fundamentally limited in terms of reproducibility and comprehensiveness. Proteomics using data-independent acquisition (DIA) can, in theory, resolve the fundamental problems associated with the DDA method. However, the lack of bioinformatics tools still presents unresolved challenges in the context of DIA, and only few DIA applications on microbiome or host-microbe interactions have been reported. MS-based metaproteomics is a challenging measurement due to the high complexity with thousands of species at vastly different abundances. To obtain a comprehensive characterization of the functional state of microbial communities requires considering proteins not just from dominant microorganisms but also low-abundance microorganisms. This proposal addresses the need for identifying and quantifying proteins through the availability of a set of computational tools that use DIA data to identify and quantify peptides and their variants at the microbial strain level. The false peptide identifications are controlled by newly proposed methods for false discovery rate assessment at multiple granularities. The protein inference and quantification are optimized by linear programming models that contain information from genome/transcriptome sequencing data and metaproteome sample replicas. The improvement will increase the number of identified protein variants, especially those from the low-abundance microorganisms, which can help accurately characterize the functional composition in microbial communities and reveal the functional redundancy.

概括为人类微生物组来表征人类微生物组的广泛努力已大大提高了有关微生物组的多样性及其在健康和疾病方面的组成。人类微生物群的营养不良基础是许多疾病的发展，例如肥胖，糖尿病和炎症性肠病。基于质谱（MS）的元蛋白质组学已广泛用于微生物组研究中用于获得洞悉微生物群落的功能状态。质谱依赖数据的质谱法（DDA）是识别和量化微生物蛋白的最常见方法但是，这种技术在重现性和全面性方面从根本上受到限制。蛋白质组学使用从理论上讲，独立于数据的获取（DIA）可以解决与DDA相关的基本问题方法。但是，缺乏生物信息学工具在DIA的背景下仍提出未解决的挑战，并且仅报道了几乎没有关于微生物组或宿主微叶相互作用的DIA应用。基于MS的元蛋白质组学是一个挑战性的测量在截然不同的丰度中。获得微生物功能状态的全面表征社区不仅要考虑蛋白质不仅是主要的微生物，而且还需要低含量的蛋白质微生物。该提议解决了通过识别和量化蛋白质的需求一组使用DIA数据识别和量化肽及其变体的计算工具的可用性在微生物应变水平上。假肽识别由新提出的方法控制在多个粒度上的发现率评估。蛋白质推断和定量通过线性编程模型，其中包含来自基因组/转录组测序数据的信息和元蛋白酶样品复制品。改进将增加已识别蛋白质变异的数量，尤其是来自低丰度微生物的人，这可以帮助准确表征功能微生物群落中的组成并揭示了功能冗余。