Toll-like receptor signaling in vertebrates: Testing the integration of protein, complex, and pathway data in the Protein Ontology framework

The Protein Ontology (PRO) provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Annotation of species-specific functions of MD2:TLR4 complexes.

<p>A version of this table with hyperlinks to the databases embedded in each identifier is available as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122978#pone.0122978.s004" target="_blank">S4 Table</a>.</p><p>Annotation of species-specific functions of MD2:TLR4 complexes.</p

TLR3 and TLR4 complexes.

<p>⁺ All PRO annotations are based on experimental evidence (Evidence code ontology ECO:0000269) except ones marked with asterisks, which are based on reconstruction of a biological system (ECO:0000088)</p><p>For each complex involved in the initial steps of TLR3 or TLR4 signaling (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122978#pone.0122978.t001" target="_blank">Table 1</a>), the PRO identifier of its species-agnostic form (parent PRO ID) is listed, together with its PRO name and the PRO identifiers of its human and mouse forms and the Reactome identifier of its human form. A version of this table with hyperlinks to the databases embedded in each identifier is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122978#pone.0122978.s002" target="_blank">S2 Table</a>.</p><p>TLR3 and TLR4 complexes.</p

TLR3 and TLR4 complex components.

<p>For each of the three nonprotein molecules involved in forming TLR3 and TLR4 complexes, its name and identifier in the ChEBI reference database is given. For each of the proteins involved in these complexes, PRO name and the UniProt and PRO identifiers for its mouse and human forms are given. A version of this table with hyperlinks to the databases embedded in each identifier is available as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122978#pone.0122978.s003" target="_blank">S3 Table</a>.</p><p>TLR3 and TLR4 complex components.</p

PRO stanzas illustrating the annotation of occurent properties of proteins and complexes.

<p><b>A</b>, function; <b>B</b>, biological process; <b>C</b>, location. Stanzas are in PAF format as described previously [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122978#pone.0122978.ref007" target="_blank">7</a>]; phrases to capture function, process, and location annotations are highlighted in red.</p

Cytoscape views of the LPS:CD14 complex repertoire.

<p>Nodes are physical entities. Circles denote proteins, triangles denote other molecules, and squares denote complexes. Dashed edges denote has_component relationships between entities; solid ones denote is_a relationships between specific and generic forms of entities.</p

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities

Publisher's PDFThe Protein Ontology (PRO; http://purl.obolibrary. org/obo/pr) formally defines and describes taxonspecific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and proteincontaining complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translationalmodification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.University of Delaware. Center for Bioinformatics & Computational Biology