RESUMEN
Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.
Asunto(s)
Algoritmos , Cromatografía Liquida/métodos , Espectrometría de Masas/métodos , Mapeo Peptídico/métodos , Proteoma/química , Proteómica/métodos , Análisis de Secuencia de Proteína/métodos , Secuencia de Aminoácidos , Biotecnología/métodos , Datos de Secuencia Molecular , Programas Informáticos , Diseño de SoftwareRESUMEN
Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.
Asunto(s)
Proteínas/metabolismo , Espectrometría de Masa por Ionización de Electrospray/métodos , Humanos , Inmunoprecipitación , Unión ProteicaRESUMEN
The success of mass-spectrometry-based proteomics as a method for analyzing proteins in biological samples is accompanied by challenges owning to demands for increased throughput. These challenges arise from the vast volume of data generated by proteomics experiments combined with the heterogeneity in data formats, processing methods, software tools and databases that are involved in the translation of spectral data into relevant and actionable information for scientists. Informatics aims to provide answers to these challenges by transferring existing solutions from information management to proteomics and/or by generating novel computational methods for automation of proteomics data processing.