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The MEROPS website (https://www.ebi.ac.uk/merops) and database was established in 1996 to present the classification and nomenclature of proteolytic enzymes. This was expanded to include a classification of protein inhibitors of proteolytic enzymes in 2004. Each peptidase or inhibitor is assigned to a distinct identifier, based on its biochemical and biological properties, and homologous sequences are assembled into a family. Families in which the proteins share similar tertiary structures are assembled into a clan. The MEROPS classification is thus a hierarchy with at least three levels (protein‐species, family, and clan) showing the evolutionary relationship. Several other data collections have been assembled, which are accessed from all levels in the hierarchy. These include, sequence homologs, selective bibliographies, substrate cleavage sites, peptidase–inhibitor interactions, alignments, and phylogenetic trees. The substrate cleavage collection has been assembled from the literature and includes physiological, pathological, and nonphysiological cleavages in proteins, peptides, and synthetic substrates. In this article, we make recommendations about how best to analyze these data and show analyses to indicate peptidase binding site preferences and exclusions. We also identify peptidases where co‐operative binding occurs between adjacent binding sites.

The distribution of all peptidase homologues across all phyla of organisms was analysed to determine within which kingdom each of the 271 families originated. No family was found to be ubiquitous and even peptidases thought to be essential for life, such as signal peptidase and methionyl aminopeptides are missing from some clades. There are 33 peptidase families common to archaea, bacteria and eukaryotes and are assumed to have originated in the last universal common ancestor (LUCA). These include peptidases with different catalytic types, exo- and endopeptidases, peptidases with different tertiary structures and peptidases from different families but with similar structures. This implies that the different catalytic types and structures pre-date LUCA. Other families have had their origins in the ancestors of viruses, archaea, bacteria, fungi, plants and animals, and a number of families have had their origins in the ancestors of particular phyla. The evolution of peptidases is compared to recent hypotheses about the evolution of organisms., Highlights • Sequences of proteolytic enzymes can be clustered into 271 families. • No family is present in all organisms. • Only 33 families are predicted to originate in the last universal common ancestor. • Different structures and activities predate the last universal common ancestor. • Other families have originated in organism kingdoms, phyla or even families.

The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.

The MEROPS database (http://www.ebi.ac.uk/merops/) is an integrated source of information about peptidases, their substrates and inhibitors. The hierarchical classification is: protein-species, family, clan, with an identifier at each level. The MEROPS website moved to the EMBL-EBI in 2017, requiring refactoring of the code-base and services provided. The interface to sequence searching has changed and the MEROPS protein sequence libraries can be searched at the EMBL-EBI with HMMER, FastA and BLASTP. Cross-references have been established between MEROPS and the PANTHER database at both the family and protein-species level, which will help to improve curation and coverage between the resources. Because of the increasing size of the MEROPS sequence collection, in future only sequences of characterized proteins, and from completely sequenced genomes of organisms of evolutionary, medical or commercial significance will be added. As an example, peptidase homologues in four proteomes from the Asgard superphylum of Archaea have been identified and compared to other archaean, bacterial and eukaryote proteomes. This has given insights into the origins and evolution of peptidase families, including an expansion in the number of proteasome components in Asgard archaeotes and as organisms increase in complexity. Novel structures for proteasome complexes in archaea are postulated.

Listed in this section are hydrolases not accumulated in other parts of the Concise Guide, such as monoacylglycerol lipase and acetylcholinesterase. Pancreatic lipase is the predominant mechanism of fat digestion in the alimentary system; its inhibition is associated with decreased fat absorption. CES1 is present at lower levels in the gut than CES2 (P23141), but predominates in the liver, where it is responsible for the hydrolysis of many aliphatic, aromatic and steroid esters. Hormone-sensitive lipase is also a relatively non-selective esterase associated with steroid ester hydrolysis and triglyceride metabolism, particularly in adipose tissue. Endothelial lipase is secreted from endothelial cells and regulates circulating cholesterol in high density lipoproteins.

Proteolytic enzymes and their homologues have been classified into clans by comparing the tertiary structures of the peptidase domains, into families by comparing the protein sequences of the peptidase domains, and into protein-species by comparing various attributes including domain architecture, substrate preference, inhibitor interactions, subcellular location, and phylogeny. The results are compared with the earlier classification (Rawlings and Barrett, 1993 [1]). The numbers of sequences, protein-species, families, clans and even catalytic type have substantially increased during the intervening 26 years. The alternative classifications by catalytic type and/or activity are shown not to reflect evolutionary relationships.

Listed in this section are hydrolases not accumulated in other parts of the Concise Guide, such as monoacylglycerol lipase and acetylcholinesterase. Pancreatic lipase is the predominant mechanism of fat digestion in the alimentary system; its inhibition is associated with decreased fat absorption. CES1 is present at lower levels in the gut than CES2 (P23141), but predominates in the liver, where it is responsible for the hydrolysis of many aliphatic, aromatic and steroid esters. Hormone-sensitive lipase is also a relatively non-selective esterase associated with steroid ester hydrolysis and triglyceride metabolism, particularly in adipose tissue. Endothelial lipase is secreted from endothelial cells and regulates circulating cholesterol in high density lipoproteins.

The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

Automatic annotation of protein function is routinely applied to newly sequenced genomes. While this provides a fine-grained view of an organism's functional protein repertoire, proteins, more commonly function in a coordinated manner, such as in pathways or multimeric complexes. Genome Properties (GPs) define such functional entities as a series of steps, originally described by either TIGRFAMs or Pfam entries. To increase the scope of coverage, we have migrated GPs to function as a companion resource utilizing InterPro entries. Having introduced GPs-specific versioned releases, we provide software and data via a GitHub repository, and have developed a new web interface to GPs (available at https://www.ebi.ac.uk/interpro/genomeproperties). In addition to exploring each of the 1286 GPs, the website contains GPs pre-calculated for a representative set of proteomes; these results can be used to profile GPs phylogenetically via an interactive viewer. Users can upload novel data to the viewer for comparison with the pre-calculated results. Over the last year, we have added ∼700 new GPs, increasing the coverage of eukaryotic systems, as well as increasing general coverage through automatic generation of GPs from related resources. All data are freely available via the website and the GitHub repository.

The MEROPS database (http://merops.sanger.ac.uk) is an integrated source of information about peptidases, their substrates and inhibitors, which are of great relevance to biology, medicine and biotechnology. The hierarchical classification of the database is as follows: homologous sets of sequences are grouped into a protein species; protein species are grouped into a family; families are grouped into clans. There is a type example for each protein species (known as a 'holotype'), family and clan, and each protein species, family and clan has its own unique identifier. Pages to show the involvement of peptidases and peptidase inhibitors in biological pathways have been created. Each page shows the peptidases and peptidase inhibitors involved in the pathway, along with the known substrate cleavages and peptidase-inhibitor interactions, and a link to the KEGG database of biological pathways. Links have also been established with the IUPHAR Guide to Pharmacology. A new service has been set up to allow the submission of identified substrate cleavages so that conservation of the cleavage site can be assessed. This should help establish whether or not a cleavage site is physiologically relevant on the basis that such a cleavage site is likely to be conserved.

The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This chapter describes how to retrieve data on lysosomal peptidases from the MEROPS database for proteolytic enzymes, their substrates and inhibitors ( http://merops.sanger.ac.uk ). Features described in this chapter include the summary page, pages for structure, interactions with inhibitors, substrates, literature and involvement in physiological pathways, and how to download data from the MEROPS FTP site. The lysosomal peptidase legumain is used as an example.

Processing of the ‘CaaX’ motif found on the C-termini of many proteins, including the proto-oncogene Ras, requires the ER (endoplasmic reticulum)-resident protease RCE1 (Ras-converting enzyme 1) and is necessary for the proper localization and function of many of these ‘CaaX’ proteins. In the present paper, we report that several mammalian species have a novel isoform (isoform 2) of RCE1 resulting from an alternate splice site and producing an N-terminally truncated protein. We demonstrate that both RCE1 isoform 1 and the newly identified isoform 2 are required to reinstate proper H-Ras processing and thus plasma membrane localization in RCE1-null cells. In addition, we show that the deubiquitinating enzyme USP17 (ubiquitin-specific protease 17), previously shown to modulate RCE1 activity, can regulate the abundance and localization of isoform 2. Furthermore, we show that isoform 2 is ubiquitinated on Lys43 and deubiquitinated by USP17. Collectively, the findings of the present study indicate that RCE1 isoform 2 is required for proper ‘CaaX’ processing and that USP17 can regulate this via its modulation of RCE1 isoform 2 ubiquitination.

Peptidases, their substrates and inhibitors are of great relevance to biology, medicine and biotechnology. The MEROPS database (http://merops.sanger.ac.uk) aims to fulfill the need for an integrated source of information about these. The database has hierarchical classifications in which homologous sets of peptidases and protein inhibitors are grouped into protein species, which are grouped into families, which are in turn grouped into clans. Recent developments include the following. A community annotation project has been instigated in which acknowledged experts are invited to contribute summaries for peptidases. Software has been written to provide an Internet-based data entry form. Contributors are acknowledged on the relevant web page. A new display showing the intron/exon structures of eukaryote peptidase genes and the phasing of the junctions has been implemented. It is now possible to filter the list of peptidases from a completely sequenced bacterial genome for a particular strain of the organism. The MEROPS filing pipeline has been altered to circumvent the restrictions imposed on non-interactive blastp searches, and a HMMER search using specially generated alignments to maximize the distribution of organisms returned in the search results has been added.