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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. • 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. [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]

Background CA_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. Results The 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. Conclusions Based 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. [ABSTRACT FROM AUTHOR]

Genome sequencing projects are generating enormous amounts of biological data that require analysis, which in turn identifies genes and proteins that require characterization. Enzymes that act on proteins are especially difficult to characterize because of the time required to distinguish one from another. This is particularly true of peptidases, the enzymes that activate, inactivate and degrade proteins. This article aims to identify clusters of sequences each of which represents the species variants of a single putative peptidase that is widely distributed and is thus merits biochemical characterization. The MEROPS database maintains large collections of sequences, references, substrate cleavage positions and inhibitor interactions of peptidases and their homologues. MEROPS also maintains a hierarchical classification of peptidase homologues, in which sequences are clustered as species variants of a single peptidase; homologous sequences are assembled into a family; and families are clustered into a clan. For each family, an alignment and a phylogenetic tree are generated. By assigning an identifier to a peptidase that has been biochemically characterized from a particular species (called a holotype), the identifier can be automatically extended to sequences from other species that cluster with the holotype. This permits transference of annotation from the holotype to other members of the cluster. By extending this concept to all peptidase homologues (including those of unknown function that have not been characterized) from model organisms representing all the major divisions of cellular life, clusters of sequences representing putative peptidases can also be identified. The 42 most widely distributed of these putative peptidases have been identified and discussed here and are prioritized as ideal candidates for biochemical characterization. [ABSTRACT FROM AUTHOR]

The terms "proteolytic enzyme" and "peptidase" have been treated as synonymous, and all proteolytic enzymes have been considered to be hydrolases (EC 3.4). However, the recent discovery of proteins that cleave themselves at asparagine residues indicates that not all peptide bond cleavage occurs by hydrolysis. These self-cleaving proteins include the Tsh protein precursor of Escherichia coli, in which the large C-terminal propeptide acts as an autotransporter; certain viral coat proteins; and proteins containing inteins. Proteolysis is the action of an amidine lyase (EC 4.3.2). These proteolytic enzymes are also the first in which the nucleophile is an asparagine, defining the seventh proteolytic catalytic type and the first to be discovered since 2004. We have assembled ten families based on sequence similarity in which cleavage is thought to be catalyzed by an asparagine. [ABSTRACT FROM AUTHOR]

Abstract: In metazoan organisms protein inhibitors of peptidases are important factors essential for regulation of proteolytic activity. In vertebrates genes encoding peptidase inhibitors constitute up to 1% of genes reflecting a need for tight and specific control of proteolysis especially in extracellular body fluids. In stark contrast unicellular organisms, both prokaryotic and eukaryotic consistently contain only few, if any, genes coding for putative peptidase inhibitors. This may seem perplexing in the light of the fact that these organisms produce large numbers of proteases of different catalytic classes with the genes constituting up to 6% of the total gene count with the average being about 3%. Apparently, however, a unicellular life-style is fully compatible with other mechanisms of regulation of proteolysis and does not require protein inhibitors to control their intracellular and extracellular proteolytic activity. So in prokaryotes occurrence of genes encoding different types of peptidase inhibitors is infrequent and often scattered among phylogenetically distinct orders or even phyla of microbiota. Genes encoding proteins homologous to alpha-2-macroglobulin (family I39), serine carboxypeptidase Y inhibitor (family I51), alpha-1-peptidase inhibitor (family I4) and ecotin (family I11) are the most frequently represented in Bacteria. Although several of these gene products were shown to possess inhibitory activity, with an exception of ecotin and staphostatins, the biological function of microbial inhibitors is unclear. In this review we present distribution of protein inhibitors from different families among prokaryotes, describe their mode of action and hypothesize on their role in microbial physiology and interactions with hosts and environment. [ABSTRACT FROM AUTHOR]

Abstract: Many of the 181 families of peptidases contain homologues that are known to have functions other than peptide bond hydrolysis. Distinguishing an active peptidase from a homologue that is not a peptidase requires specialist knowledge of the important active site residues, because replacement or lack of one of these catalytic residues is an important clue that the homologue in question is unlikely to hydrolyse peptide bonds. Now that the rate at which proteins are characterized is outstripped by the rate that genome sequences are determined, many genes are being incorrectly annotated because only sequence similarity is taken into consideration. We present a tool called the MEROPS batch BLAST which not only performs a comparison against the MEROPS sequence collection, but also does a pair-wise alignment with the closest homologue detected and calculates the position of the active site residues. A non-peptidase homologue can be distinguished by the absence or unacceptable replacement of any of these residues. An analysis of peptidase homologues in the genome of the bacterium Erythrobacter litoralis is presented as an example. [Copyright &y& Elsevier]

A good system for the naming and classification of peptidases can contribute much to the study of these enzymes. Having already described the building of families and clans in the MEROPS system, we here focus on the lowest level in the hierarchy, in which the huge number of individual peptidase proteins are assigned to a lesser number of what we term ‘species’ of peptidases. Just over 2000 peptidase species are recognised today, but we estimate that 25 000 will one day be known. Each species is built around a peptidase protein that has been adequately characterised. The cluster of peptidase proteins that represent the single species is then assembled primarily by analysis of a sequence ‘tree’ for the family. Each peptidase species is given a systematic identifier and a summary page of data regarding it is assembled. Because the characterisation of new peptidases lags far behind the sequencing, the majority of peptidase proteins are so far known only as amino acid sequences and cannot yet be assigned to species. We suggest that new forms of analysis of the sequences of the unassigned peptidases may give early indications of how they will cluster into the new species of the future. [ABSTRACT FROM AUTHOR]

The enzymes that hydrolyse peptide bonds, called peptidases or proteases, are very important to mankind and are also very numerous. The many scientists working on these enzymes are rapidly acquiring new data, and they need good methods to store it and retrieve it. The storage and retrieval require effective systems of classification and nomenclature, and it is the design and implementation of these that we mean by 'managing' peptidases. Ten years ago Rawlings and Barrett proposed the first comprehensive system for the classification of peptidases, which included a set of simple names for the families. In the present article we describe how the system has developed since then. The peptidase classification has now been adopted for use by many other databases, and provides the structure around which the MEROPS protease database (http://merops.sanger.ac.uk) is built. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • Proteolytic enzymes are classified by tertiary structure similarity into clans and by sequence similarity into families. • Criteria have been devised to differentiate paralogous enzymes within a family, and a unique identifier assigned. • There has been a significant increase in clans, families, different enzymes and active site mechanisms since 1993. [ABSTRACT FROM AUTHOR]

Peptidases are enzymes that hydrolyse peptide bonds in proteins and peptides. Peptidases are important in pathological conditions such as Alzheimer's disease, tumour and parasite invasion, and for processing viral polyproteins. The MEROPS database is an Internet resource containing information on peptidases, their substrates and inhibitors. The database now includes details of cleavage positions in substrates, both physiological and non-physiological, natural and synthetic. There are 39 118 cleavages in the collection; including 34 606 from a total of 10 513 different proteins and 2677 cleavages in synthetic substrates. The number of cleavages designated as ‘physiological’ is 13 307. The data are derived from 6095 publications. At least one substrate cleavage is known for 45% of the 2415 different peptidases recognized in the MEROPS database. The website now has three new displays: two showing peptidase specificity as a logo and a frequency matrix, the third showing a dynamically generated alignment between each protein substrate and its most closely related homologues. Many of the proteins described in the literature as peptidase substrates have been studied only in vitro. On the assumption that a physiologically relevant cleavage site would be conserved between species, the conservation of every site in terms of peptidase preference has been examined and a number have been identified that are not conserved. There are a number of cogent reasons why a site might not be conserved. Each poorly conserved site has been examined and a reason postulated. Some sites are identified that are very poorly conserved where cleavage is more likely to be fortuitous than of physiological relevance. This data-set is freely available via the Internet and is a useful training set for algorithms to predict substrates for peptidases and cleavage positions within those substrates. The data may also be useful for the design of inhibitors and for engineering novel specificities into peptidases. [ABSTRACT FROM PUBLISHER]