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      Applications of targeted proteomics in systems biology and translational medicine

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          Abstract

          Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM‐MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.

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          Most cited references106

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          Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

          A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.
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            Protein 3D Structure Computed from Evolutionary Sequence Variation

            The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.
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              Encoding and decoding cellular information through signaling dynamics.

              A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this Review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes, and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through the dynamics of signaling molecules and possible applications for the treatment of disease. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Proteomics
                Proteomics
                10.1002/(ISSN)1615-9861
                PMIC
                Proteomics
                John Wiley and Sons Inc. (Hoboken )
                1615-9853
                1615-9861
                16 July 2015
                September 2015
                : 15
                : 18 , Focus on Quantitative Proteomics ( doiID: 10.1002/pmic.v15.18 )
                : 3193-3208
                Affiliations
                [ 1 ] Department of Biology Institute of Molecular Systems BiologyEidgenossische Technische Hochschule (ETH) Zurich ZurichSwitzerland
                [ 2 ] Computational Biology CenterMemorial Sloan‐Kettering Cancer Center New York NYUSA
                [ 3 ] Department of Physiology Biophysics and Systems BiologyWeill Cornell Medical College New York NYUSA
                [ 4 ] Faculty of ScienceUniversity of Zurich ZurichSwitzerland
                Author notes
                [*] [* ] Correspondence: Dr. H. Alexander Ebhardt, ETH – D‐BIOL – IMSB, Auguste‐Piccard‐Hof 1, 8093 Zurich, Switzerland

                Email: ebhardt@ 123456imsb.biol.ethz.ch

                Fax: + 41 44 633 15 32

                Article
                PMIC12069
                10.1002/pmic.201500004
                4758406
                26097198
                ce74b50a-619e-4dc9-897a-8b770ab3b51b
                © 2015 The Authors. PROTEOMICS published by Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 08 January 2015
                : 27 April 2015
                : 09 June 2015
                Page count
                Pages: 16
                Funding
                Funded by: SystemsX.ch project PhosphoNetX
                Funded by: SNSF
                Award ID: 3100A0‐688 107679
                Funded by: European Research Council
                Award ID: ERC‐2008‐AdG 233226
                Categories
                Review
                Technology
                Review
                Custom metadata
                2.0
                pmic12069
                September 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.7.6 mode:remove_FC converted:18.02.2016

                Molecular biology
                clinical proteomics,multiple reaction monitoring,selected reaction monitoring,systems biology,targeted proteomics

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