A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

/pdf/the-perseus-computational-platform-for-comprehensive-5a4p3g7pp4.pdf

The Perseus computational platform for comprehensive analysis of (prote)omics data.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

/pdf/a-sars-cov-2-protein-interaction-map-reveals-targets-for-5d1m2n6hc1.pdf

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda This protocol update describes an adaptation of an existing protocol that substantially modifies the technique Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs) The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores The software is written in C# and is freely available at http://wwwmaxquantorg

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

Mass-spectrometric exploration of proteome structure and function

The BioPlex Network: A Systematic Exploration of the Human Interactome

Computational methods are designed to solve complex problems systematically and efficiently. Classification and selection procedures are often used in biological sequence and other data analysis. This chapter provides an introduction to different methods like clustering, hypothesis -testing, and classification methods.

Introduction to Algorithms

Fewer than half of all tandem mass spectrometry (MS/MS) spectra acquired in shotgun proteomics experiments are typically matched to a peptide with high confidence. Here we determine the identity of unassigned peptides using an ultra-tolerant Sequest database search that allows peptide matching even with modifications of unknown masses up to ± 500 Da. In a proteome-wide data set on HEK293 cells (9,513 proteins and 396,736 peptides), this approach matched an additional 184,000 modified peptides, which were linked to biological and chemical modifications representing 523 distinct mass bins, including phosphorylation, glycosylation and methylation. We localized all unknown modification masses to specific regions within a peptide. Known modifications were assigned to the correct amino acids with frequencies >90%. We conclude that at least one-third of unassigned spectra arise from peptides with substoichiometric modifications.

/pdf/a-mass-tolerant-database-search-identifies-a-large-gwj5hxdus6.pdf

A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides

UNLABELLED Physico-chemical properties of amino acids can be used to study protein sequence profiles, folding and function. We collated 242 properties for the 20 naturally occurring amino acids and created a dataset. The dataset is available as a database named APDbase( Amino acid Physico-chemical properties Data base). The database can be queried using either key words describing physico-chemical properties or pre-assigned database index number. The database contains corresponding references for each property value and facilitates deposition of new property values for processing and inclusion in the database. AVAILABILITY The database is available for free at http://www.rfdn.org/bioinfo/APDbase.php.

/pdf/apdbase-amino-acid-physico-chemical-properties-database-253fpmkaaf.pdf

APDbase: Amino acid Physico-chemical properties Database.

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The BioPlex Network: A Systematic Exploration of the Human Interactome

Introduction to Algorithms

A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides

APDbase: Amino acid Physico-chemical properties Database.