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The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

BackgroundCA_C2195 from Clostridium acetobutylicum is a protein of unknown function. Sequence analysis predicted that part of the protein contained a metallopeptidase-related domain. There are over 200 homologs of similar size in large sequence databases such as UniProt, with pairwise sequence identities in the range of ~40-60%. CA_C2195 was chosen for crystal structure determination for structure-based function annotation of novel protein sequence space.ResultsThe structure confirmed that CA_C2195 contained an N-terminal metallopeptidase-like domain. The structure revealed two extra domains: an α+β domain inserted in the metallopeptidase-like domain and a C-terminal circularly permuted winged-helix-turn-helix domain.ConclusionsBased on our sequence and structural analyses using the crystal structure of CA_C2195 we provide a view into the possible functions of the protein. From contextual information from gene-neighborhood analysis, we propose that rather than being a peptidase, CA_C2195 and its homologs might play a role in biosynthesis of a modified cell-surface carbohydrate in conjunction with several sugar-modification enzymes. These results provide the groundwork for the experimental verification of the function.

Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa. Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted protein-encoding genes led to multiple discoveries, including chromosomal integrations of bacterial (Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These genome data provide a foundation for research into trypanosomiasis prevention and yield important insights with broad implications for multiple aspects of tsetse biology.

BackgroundA novel highly conserved protein domain, DUF162 [Pfam: PF02589], can be mapped to two proteins: LutB and LutC. Both proteins are encoded by a highly conserved LutABC operon, which has been implicated in lactate utilization in bacteria. Based on our analysis of its sequence, structure, and recent experimental evidence reported by other groups, we hereby redefine DUF162 as the LUD domain family.ResultsJCSG solved the first crystal structure [PDB:2G40] from the LUD domain family: LutC protein, encoded by ORF DR_1909, of Deinococcus radiodurans. LutC shares features with domains in the functionally diverse ISOCOT superfamily. We have observed that the LUD domain has an increased abundance in the human gut microbiome.ConclusionsWe propose a model for the substrate and cofactor binding and regulation in LUD domain. The significance of LUD-containing proteins in the human gut microbiome, and the implication of lactate metabolism in the radiation-resistance of Deinococcus radiodurans are discussed.

Imelysin-like proteins define a superfamily of bacterial proteins that are likely involved in iron uptake. Members of this superfamily were previously thought to be peptidases and were included in the MEROPS family M75. We determined the first crystal structures of two remotely related, imelysin-like proteins. The Psychrobacter arcticus structure was determined at 2.15 Å resolution and contains the canonical imelysin fold, while higher resolution structures from the gut bacteria Bacteroides ovatus, in two crystal forms (at 1.25 Å and 1.44 Å resolution), have a circularly permuted topology. Both structures are highly similar to each other despite low sequence similarity and circular permutation. The all-helical structure can be divided into two similar four-helix bundle domains. The overall structure and the GxHxxE motif region differ from known HxxE metallopeptidases, suggesting that imelysin-like proteins are not peptidases. A putative functional site is located at the domain interface. We have now organized the known homologous proteins into a superfamily, which can be separated into four families. These families share a similar functional site, but each has family-specific structural and sequence features. These results indicate that imelysin-like proteins have evolved from a common ancestor, and likely have a conserved function.

NlpC/P60 superfamily papain-like enzymes play important roles in all kingdoms of life. Two members of this superfamily, LRAT-like and YaeF/YiiX-like families, were predicted to contain a catalytic domain that is circularly permuted such that the catalytic cysteine is located near the C-terminus, instead of at the N-terminus. These permuted enzymes are widespread in virus, pathogenic bacteria, and eukaryotes. We determined the crystal structure of a member of the YaeF/YiiX-like family from Bacillus cereus in complex with lysine. The structure, which adopts a ligand-induced, "closed" conformation, confirms the circular permutation of catalytic residues. A comparative analysis of other related protein structures within the NlpC/P60 superfamily is presented. Permutated NlpC/P60 enzymes contain a similar conserved core and arrangement of catalytic residues, including a Cys/His-containing triad and an additional conserved tyrosine. More surprisingly, permuted enzymes have a hydrophobic S1 binding pocket that is distinct from previously characterized enzymes in the family, indicative of novel substrate specificity. Further analysis of a structural homolog, YiiX (PDB 2if6) identified a fatty acid in the conserved hydrophobic pocket, thus providing additional insights into possible function of these novel enzymes.

The effect of a specific cysteine enzyme protease inhibitor on excystation of Giardia was analyzed by incubation of Giardia muris fecal cysts with peptidyl fluoromethyl ketone morpholine-tyrosine (o-methyl)homophenylalanine fluoromethyl ketone (Mu-Tyr(Ome)-hPhe-FMK) at 7-100uM. Excystation was arrested by the cysteine protease inhibitor E-64. However, the enzyme reportedly has no effect on trophozoite motility and replication. Furthermore, enzyme protease activity was localized in cytoplasmic vacuoles by using fluorescent microscopy.

Abstract Background Peptidase family A1, to which pepsin belongs, had been assumed to be restricted to eukaryotes. The tertiary structure of pepsin shows two lobes with similar folds and it has been suggested that the gene has arisen from an ancient duplication and fusion event. The only sequence similarity between the lobes is restricted to the motif around the active site aspartate and a hydrophobic-hydrophobic-Gly motif. Together, these contribute to an essential structural feature known as a psi-loop. There is one such psi-loop in each lobe, and so each lobe presents an active Asp. The human immunodeficiency virus peptidase, retropepsin, from peptidase family A2 also has a similar fold but consists of one lobe only and has to dimerize to be active. All known members of family A1 show the bilobed structure, but it is unclear if the ancestor of family A1 was similar to an A2 peptidase, or if the ancestral retropepsin was derived from a half-pepsin gene. The presence of a pepsin homologue in a prokaryote might give insights into the evolution of the pepsin family. Results Homologues of the aspartic peptidase pepsin have been found in the completed genomic sequences from seven species of bacteria. The bacterial homologues, unlike those from eukaryotes, do not possess signal peptides, and would therefore be intracellular acting at neutral pH. The bacterial homologues have Thr218 replaced by Asp, a change which in renin has been shown to confer activity at neutral pH. No pepsin homologues could be detected in any archaean genome. Conclusion The peptidase family A1 is found in some species of bacteria as well as eukaryotes. The bacterial homologues fall into two groups, one from oceanic bacteria and one from plant symbionts. The bacterial homologues are all predicted to be intracellular proteins, unlike the eukaryotic enzymes. The bacterial homologues are bilobed like pepsin, implying that if no horizontal gene transfer has occurred the duplication and fusion event might be very ancient indeed, preceding the divergence of bacteria and eukaryotes. It is unclear whether all the bacterial homologues are derived from horizontal gene transfer, but those from the plant symbionts probably are. The homologues from oceanic bacteria are most closely related to memapsins (or BACE-1 and BACE-2), but are so divergent that they are close to the root of the phylogenetic tree and to the division of the A1 family into two subfamilies.

Abstract Background We wished to compare two databases based on sequence similarity: one that aims to be comprehensive in its coverage of known sequences, and one that specialises in a relatively small subset of known sequences. One of the motivations behind this study was quality control. Pfam is a comprehensive collection of alignments and hidden Markov models representing families of proteins and domains. MEROPS is a catalogue and classification of enzymes with proteolytic activity (peptidases or proteases). These secondary databases are used by researchers worldwide, yet their contents are not peer reviewed. Therefore, we hoped that a systematic comparison of the contents of Pfam and MEROPS would highlight missing members and false-positives leading to improvements in quality of both databases. An additional reason for carrying out this study was to explore the extent of consensus in the definition of a protein family. Results About half (89 out of 174) of the peptidase families in MEROPS overlapped single Pfam families. A further 32 MEROPS families overlapped multiple Pfam families. Where possible, new Pfam families were built to represent most of the MEROPS families that did not overlap Pfam. When comparing the numbers of sequences found in the overlap between a MEROPS family and its corresponding Pfam family, in most cases the overlap was substantial (52 pairs of MEROPS and Pfam families had an intersection size of greater than 75% of the union) but there were some differences in the sets of sequences included in the MEROPS families versus the overlapping Pfam families. Conclusions A number of the discrepancies between MEROPS families and their corresponding Pfam families arose from differences in the aims and philosophies of the two databases. Examination of some of the discrepancies highlighted additional members of families, which have subsequently been added in both Pfam and MEROPS. This has led to improvements in the quality of both databases. Overall there was a great deal of consensus between the databases in definitions of a protein family.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]