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Proteome Bioinformatics. Edited by Simon J. Hubbard and Andrew R. Jones,

Proteomics is a still-evolving combination of various technologies to characterize all expressed proteins in a biological system. Understanding the structure and function of each protein and its relation to other expressed molecules (including proteins, DNA, metabolites and molecular complexes) is a key to a more complete understanding of biological processes. A major effort in mass spectrometry (MS)-based proteomics research is currently devoted to the fractionation of proteins and peptides prior to MS analysis. Typically, proteins are digested into short peptides with enzymes like trypsin. Liquid chromatography (LC) is commonly used to simplify the resulting peptide mixtures to the extent required for MS analysis of the component peptides. Various bioinformatics tools have been developed to investigate the protein expression in a proteome. This timely published book Proteome Bioinformatics edited by Simon J. Hubbard at the University of Manchester and Andrew R. Jones at University of Liverpool, UK provides a general overview of the computational approaches used in MS-based expression proteomics. This book is part of the Methods in Molecular Biology series, and is published by Humana Press (http://www.springerprotocols.com/BookToc/doi/ 10.1007/978-1-60761-444-9). This book did an excellent job in reviewing protein identification informatics. Proteins can be identified either by single-stage MS or by tandem MS (MS/MS). If single-stage MS is used, a protein is digested and the pattern of peptide masses is compared with a theoretical digest of database sequences to make the identification. This approach is called peptide mass fingerprinting (PMF). Several PMF scoring functions and statistical models to evaluate the confidence of the PMF score are reviewed in this book. In the MS/MS approach, individual peptides are usually selected in the first stage of MS and subjected to a fragmentation process, and the products of the fragmentation are analyzed in the second-stage MS to produce information about the mass-to-charge ratios (m/z) of the resulting fragment ions. Three approaches have been developed to identify proteins from the MS/MS data: database searching, de novo sequencing and spectrum library searching. The database-searching methods assume that the peptide and parent protein sequence in question is present in the database that is to be searched. The de novo sequencing methods seek to identify the peptide directly from the MS/MS spectra. This approach is required for identification of novel proteins, proteins resulting from some mutations and proteins with unexpected modifications. Advancement in MS also provides an opportunity to construct annotated mass spectrum library for peptide identification by determining which spectrum entry in the library corresponds best to the experimental spectrum. A variety of algorithms using these three protein identification approaches have been developed, and these algorithms are reviewed in great detail in this book. Statistical significance measures to simplify and unify the interpretation of protein identification are also introduced. The properties that describe how the protonated peptides fragment when activated by collisions in a mass spectrometer and how that information can be employed for protein identification are also addressed in this book. Two types of analytical platforms have been developed in expression proteomics for protein quantification: label-free and stable isotope labeling. Label-free quantification is an LC–MS-based method that aims to determine the differentially expressed proteins in two or more biological samples based on precursor ion signal intensity. This book outlined the computational procedure for recovering the quantitative information from label-free LC–MS experiments and provided examples of statistical tests used to evaluate the relevance of the results. Bioinformatics packages such as OpenMS and TransProteomicPeipeline (TPP) are introduced. The stable isotope-labeled approach introduces stable isotope signature mass tags to peptides/proteins that can be detected in the mass spectrometer to quantify each analyte and to determine the sample from which it originates. Even though many bioinformatics tools have been developed for analyses of stable isotope-labeled data, two of them are introduced in this book: Multi-Q and MaXIC-Q for MS/MSand MS-based quantification analysis. Informatics and statistics for analyzing 2D gel electrophoresis images are also introduced in this book. BRIEFINGS IN BIOINFORMATICS. VOL 12. NO 1. 80^81 doi:10.1093/bib/bbq076 Advance Access published on 22 December 2010