Introduction to Computational Proteomics

Introduction: The technological and scientific progress performed in the Human Proteome Project (HPP) has provided to the scientific community a new set of experimental and bioinformatic methods in the challenging field of shotgun and SRM/MRM-based Proteomics. The requirements for a protein to be considered experimentally validated are now well-established, and the information about the human proteome is available in the neXtProt database, while targeted proteomic assays are stored in SRMAtlas. However, the study of the missing proteins continues being an outstanding issue. Areas covered: This review is focused on the implementation of proteogenomic methods designed to improve the detection and validation of the missing proteins. The evolution of the methodological strategies based on the combination of different omic technologies and the use of huge publicly available datasets is shown taking the Chromosome 16 Consortium as reference. Expert commentary: Proteogenomics and other strategies of data analysis implemented within the C-HPP initiative could be used as guidance to complete in a near future the catalog of the human proteins. Besides, in the next years, we will probably witness their use in the B/D-HPP initiative to go a step forward on the implications of the proteins in the human biology and disease.

Data for an examplary metaproteomics data analysis with the MetaProteomeAnalyzer (MPA) and Prophane software tools. Data is from the PRIDE dataset PXD010550.

Files include:

• protein databases (FASTA) :
  1. UniProt Swiss-Prot: UniprotSwP-2020_03.fasta
  2. Metagenome (+ Swiss-Prot): MG_BG_UPSP.fasta
• MS Datasets (MGF):
  1. FASP digetst: FASP_BGP_A.mgf
  2. in get digest: InGel_BGP_A.mgf
• MPA data dump including preprocessed UniProt Swiss-Prot FASTA (optionally used by conda mpa-server package)

Examples of computational methods for spatial proteomics datasets for prediction and novelty detection.

Intrinsically disordered proteins and protein regions lack a stable three-dimensional structure under physiological conditions. Several proteomic investigations of intrinsic disorder have been performed to date and have found disorder to be prevalent in eukaryotic proteomes. Here we present descriptive statistics of intrinsic disorder features for ten model eukaryotic proteomes that have been calculated from computational disorder prediction algorithms. The data descriptor also provides consensus disorder annotations as well as additional physical parameters relevant to protein disorder, and further provides protein existence information for all proteins included in our analysis. The complete datasets can be downloaded freely, and it is envisaged that they will be updated periodically with new proteomes and protein disorder prediction algorithms. These datasets will be especially useful for assessing protein disorder, and conducting novel analyses that advance our understanding of intri...

Protein localization is paramount to protein function, and the intracellular movement of proteins underlies the regulation of numerous cellular processes. Given advances in spatial proteomics, the investigation of protein localization at a global scale has become attainable. Also becoming apparent is the need for dedicated analytical frameworks that allow the discovery of global intracellular protein movement events. Here, we describe TRANSPIRE, a computational pipeline that facilitates TRanslocation ANalysis of SPatIal pRotEomics data sets. TRANSPIRE leverages synthetic translocation profiles generated from organelle marker proteins to train a probabilistic Gaussian process classifier that predicts changes in protein distribution. This output is then integrated with information regarding co-translocating proteins and complexes and enriched gene ontology associations to discern the putative regulation and function of movement. We validate TRANSPIRE performance for predicting nuclear-cytoplasmic shuttling events. Analyzing an existing data set of nuclear and cytoplasmic proteomes during Kaposi Sarcoma-associated herpesvirus (KSHV)-induced cellular mRNA decay, we confirm that TRANSPIRE readily discerns expected translocations of RNA binding proteins. We next investigate protein translocations during infection with human cytomegalovirus (HCMV), a β-herpesvirus known to induce global organelle remodeling. We find that HCMV infection induces broad changes in protein localization, with over 800 proteins predicted to translocate during virus replication. Evident are protein movements related to HCMV modulation of host defense, metabolism, cellular trafficking, and Wnt signaling. For example, the low-density lipoprotein receptor (LDLR) translocates to the lysosome early in infection in conjunction with its degradation, which we validate by targeted mass spectrometry. Using microscopy, we also validate the translocation of the multifunctional kinase DAPK3, a movement that may contribute to HCMV activation of Wnt signaling.

We used decoupleR to evaluate the performance of individual methods by recovering perturbed transcription factors (TFs) from a curation of single-gene perturbation experiments (Holland et al., 2020). As a resource we used DoRothEA, a gene regulatory network linking TFs to target genes by their mode of regulation (Garcia-Alonso et al., 2019). Perturbation experiments where the targeted regulator was not in DoRothEA were removed. After filtering, this dataset is composed of gene expression data from 92 knockdown and overexpression experiments of 40 unique TFs in human cells. Additionally, we tested the performance of decoupleR on phospho-proteomic data. For this, we filtered in a similar fashion a curated set of knockdown and overexpression single-kinase perturbation experiments, obtaining 63 experiments including 14 unique kinases, and applied a weighted resource from the same publication that links kinases to their target phosphosites (Hernandez-Armenta et al., 2017). For the transcriptomic dataset, differential expression analysis was performed with limma (Ritchie et al., 2015) and the resulting t-values were used as input. For the phospho-proteomics, the quantile-normalized log2-fold changes from different studies were used to make them comparable.

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Background: In recent years, an innovative strategy using laser microdissection and mass spectrometry markedly expanded the landscape of antigens associated with membranous nephropathy (MN). Specific associations with phenotypes, diseases and sometimes reversible triggers led to a novel antigen-based classification of MN, paving the way for precision medicine and stressing the need for more routine use of proteomics in MN. Methods: To explore the proteomic landscape of human glomeruli and identify podocyte antigens and disease mechanisms in MN, we expanded the original technique to an integrative approach combining laser capture microdissection, next-generation mass spectrometry and computational analysis. Next to conventional data-dependent acquisition (DDA), we used and assessed the diagnostic yield of the more comprehensive data-independent acquisition (DIA) mass spectrometry, which enables the detection and quantification of every peptide in a sample irrespective of its level of abundance or m/z value. Our proteomic pipeline was applied to residual material from kidney biopsies in 64 individuals, including 31 healthy controls; 5 disease controls; 5 PLA2R-associated MN; and 23 PLA2R-negative MN. Results: Unbiased analyses confirmed the significant enrichment in PLA2R, IgG4 and complement proteins in glomeruli from patients with PLA2R-MN compared with healthy and disease controls, while molecular characterization of complement fragments provided evidence for complement activation in PLA2R-MN. Compared to DDA, DIA mass spectrometry increased the number of glomerular proteins (~3800 vs. ~1200) identified in healthy glomeruli; allowed the detection all known antigens except NELL1 in normal glomeruli; and increased the detection rate of podocyte antigens from 50% to >80% in PLA2R-negative MN. Conclusions: This proof-of-concept study suggests that an integrative approach combining laser microdissection, DIA mass spectrometry and computational biology is a powerful tool, with translational potential, to identify podocyte antigens and unravel disease mechanisms in MN.

Summary of the dataset and subsequent analyses.

Meningiomas are among the most common primary tumors of the central nervous system (CNS) and originate from the arachnoid or meningothelial cells. Risk factors for the disease include exposure to ionizing radiation, head trauma, and family history. Surgery is the first option of treatment for meningioma, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Literature shown several specific unfoldings of this disease when comparing male versus female patients; for example, in one hand, its incidence is higher in female patients, on the other hand, male patients usually develop the malignant and more aggressive type. Here, we compare the proteomic profile of tumor biopsies of male and female patients diagnosed with grade I meningioma and list several differentially abundant proteins between the two groups that ultimately reflected in different enriched pathways. For male patients, the enriched pathways were mostly involved in neutrophil degranulation, cell-matrix organization, antigen processing, and the presentation of exogenous peptides; for the female group, pathways related to the organization of cellular components and organelles, and RNA splicing. Tackling on these differences is important to better understand this disease and, ultimately, provide individual treatments.

Supplementary File 2 from Identification of Optimal Drug Combinations Targeting Cellular Networks: Integrating Phospho-Proteomics and Computational Network Analysis

These tissue-level multi-omic graphical analysis reports are provided as additional data for the manuscript “Temporal dynamics of the multi-omic response to endurance exercise training across tissues” (MoTrPAC Study Group, bioRxiv, 2022). Find the preprint here. Extensive background is included in each report. Briefly, we used a graphical clustering approach to define and visualize the temporal dynamics of molecular analytes regulated by endurance exercise training at multiple training time points in male and female rats across many data types ("omes") and tissues. The objective of these multi-omic reports is to share representations of >34,000 training-regulated molecular features in interactive HTML reports that allow researchers to extract meaningful biology from a complex dataset. Each report presents a summary of the significantly training-regulated features across omes in a specific tissue and the corresponding graphical analysis results, as well as features and pathway enrichment results corresponding to the largest graphical clusters (nodes, edges, and paths) for that tissue. A graphical cluster is a group of training-regulated features that share temporal behavior at some point during the training time course. These multi-omic reports are generated using data and functions available through the MotrpacRatTraining6mo R package. Install this R package to explore the data yourself! Get started with this tutorial. {"references": ["Ignatiadis N, Klaus B, Zaugg JB, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods. 2016 Jul;13(7):577-80. doi: 10.1038/nmeth.3885. Epub 2016 May 30. PMID: 27240256; PMCID: PMC4930141.", "Heller R, Yaacoby S, Yekutieli D. repfdr: a tool for replicability analysis for genome-wide association studies. Bioinformatics. 2014 Oct 15;30(20):2971-2. doi: 10.1093/bioinformatics/btu434. Epub 2014 Jul 9. PMID: 25012182.", "Almende B.V. and Contributors, Thieurmel B (2022). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.1.2, https://CRAN.R-project.org/package=visNetwork.", "Gay N, Amar D, Jean Beltran P, MoTrPAC Study Group (2022). MotrpacRatTraining6mo: Analysis of the MoTrPAC endurance exercise training data in 6-month-old rats. R package version 1.5.2, https://motrpac.github.io/MotrpacRatTraining6mo/."]}

MassIVE is a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.

aAnnotated with MaxQuant.

Unfiltered supplementary tables for "Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning"

Computational and Structural Biotechnology Journal Publication fee - ResearchHelpDesk - Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology The journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence, and enables the rapid publication of papers under the following categories: Research articles Review articles Mini Reviews Highlights Communications Software/Web server articles Methods articles Database articles Book Reviews Meeting Reviews

FASTA Sequences corresponding to entries in BioLiP for metalloproteins.

We describe the formalization of the reactive docking protocol, a method developed to model and predict reactions between small molecules and biological macromolecules. The method has been successfully used in a number of applications already, including recapitulating large proteomics data sets, performing structure–reactivity target optimizations, and prospective virtual screenings. By modeling a near-attack conformation-like state, no QM calculations are required to model the ligand and receptor geometries. Here, we present its generalization using a large data set containing more than 400 ligand–target complexes, 8 nucleophilic modifiable residue types, and more than 30 warheads. The method correctly predicts the modified residue in ∼85% of complexes and shows enrichments comparable to standard focused virtual screenings in ranking ligands. This performance supports this approach for the docking and screening of reactive ligands in virtual chemoproteomics and drug design campaigns.

Available data types in the proteomic data commons