MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database.

The extracellular matrix (ECM) is a complex assembly of proteins that constitutes the scaffold organizing cells, tissues, and organs. Over the past decade, mass-spectrometry-based proteomics has become the method of choice to profile the composition of the ECM, or the matrisome, of tissues. To assist non-specialists with the reuse of ECM proteomic datasets, we released MatrisomeDB (https://matrisomedb.org) in 2020. Here, we report the expansion of the database to include 25 new curated studies on the ECM of 24 new tissues in addition to datasets on tissues previously included, more than doubling the size of the original database and achieving near-complete coverage of the in-silico predicted matrisome. We further enhanced data visualization by maps of peptides and post-translational-modifications detected onto domain-based representations and 3D structures of ECM proteins. We also referenced external resources to facilitate the design of targeted mass spectrometry assays. Last, we implemented an abstract-mining tool that generates an enrichment word cloud from abstracts of studies in which a queried protein is found with higher confidence and higher abundance relative to other studies in MatrisomeDB.

Sequence Coverage Visualizer: A Web Application for Protein Sequence Coverage 3D Visualization.

Protein structure defines protein function and plays an extremely important role in protein characterization. Recently, two groups of researchers from DeepMind and the Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. This enabled us to visualize the entire human proteome using predicted 3D structures for the first time. To help other researchers best utilize these protein structure predictions in proteomics experiments, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular proteomics experiment (identified peptide list) into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help to compare different protein structures from different sources, including predicted ones and existing PDB entries. We hope our tool can provide help in the process of improving protein structure prediction accuracy. Overall, SCV is a convenient and powerful tool for visualizing proteomics results in 3D.