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      Inferring protein-protein interaction networks from mass spectrometry-based proteomic approaches: A mini-review

      , ,
      Computational and Structural Biotechnology Journal
      Elsevier BV

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          Abstract

          Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.

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          Most cited references77

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          Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

          Quantitative proteomics has traditionally been performed by two-dimensional gel electrophoresis, but recently, mass spectrometric methods based on stable isotope quantitation have shown great promise for the simultaneous and automated identification and quantitation of complex protein mixtures. Here we describe a method, termed SILAC, for stable isotope labeling by amino acids in cell culture, for the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid, in this case deuterated leucine (Leu-d3). We find that growth of cells maintained in these media is no different from growth in normal media as evidenced by cell morphology, doubling time, and ability to differentiate. Complete incorporation of Leu-d3 occurred after five doublings in the cell lines and proteins studied. Protein populations from experimental and control samples are mixed directly after harvesting, and mass spectrometric identification is straightforward as every leucine-containing peptide incorporates either all normal leucine or all Leu-d3. We have applied this technique to the relative quantitation of changes in protein expression during the process of muscle cell differentiation. Proteins that were found to be up-regulated during this process include glyceraldehyde-3-phosphate dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple, inexpensive, and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system.
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            The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

            Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.
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              Proteomic characterization of the human centrosome by protein correlation profiling.

              The centrosome is the major microtubule-organizing centre of animal cells and through its influence on the cytoskeleton is involved in cell shape, polarity and motility. It also has a crucial function in cell division because it determines the poles of the mitotic spindle that segregates duplicated chromosomes between dividing cells. Despite the importance of this organelle to cell biology and more than 100 years of study, many aspects of its function remain enigmatic and its structure and composition are still largely unknown. We performed a mass-spectrometry-based proteomic analysis of human centrosomes in the interphase of the cell cycle by quantitatively profiling hundreds of proteins across several centrifugation fractions. True centrosomal proteins were revealed by both correlation with already known centrosomal proteins and in vivo localization. We identified and validated 23 novel components and identified 41 likely candidates as well as the vast majority of the known centrosomal proteins in a large background of nonspecific proteins. Protein correlation profiling permits the analysis of any multiprotein complex that can be enriched by fractionation but not purified to homogeneity.
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                Author and article information

                Journal
                Computational and Structural Biotechnology Journal
                Computational and Structural Biotechnology Journal
                Elsevier BV
                20010370
                June 2019
                June 2019
                Article
                10.1016/j.csbj.2019.05.007
                e89ec697-ce8e-475e-8a7c-8d74cfc2c2ea
                © 2019

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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