Biomedical subjects

Neil D Rawlings

Publications and source records attributed to Neil D Rawlings.

6 recordsLinked to original sources

PubMed2004

Evolutionary families of peptidase inhibitors.

The proteins that inhibit peptidases are of great importance in medicine and biotechnology, but there has never been a comprehensive system of classification for them. Some of the terminology currently in use is potentially confusing. In the hope of facilitating the exchange, storage and retrieval of information about this important group of proteins, we now describe a system wherein the inhibitor units of the peptidase inhibitors are assigned to 48 families on the basis of similarities detectable at the level of amino acid sequence. Then, on the basis of three-dimensional structures, 31 of the families are assigned to 26 clans. A simple system of nomenclature is introduced for reference to each clan, family and inhibitor. We briefly discuss the specificities and mechanisms of the interactions of the inhibitors in the various families with their target enzymes. The system of families and clans of inhibitors described has been implemented in the MEROPS peptidase database (http://merops.sanger.ac.uk/), and this will provide a mechanism for updating it as new information becomes available.

Biological Evolution↗

PubMed2004

MEROPS: the peptidase database.

Peptidases (proteolytic enzymes) are of great relevance to biology, medicine and biotechnology. This practical importance creates a need for an integrated source of information about them, and also about their natural inhibitors. The MEROPS database (http://merops.sanger.ac.uk) aims to fill this need. The organizational principle of the database is a hierarchical classification in which homologous sets of the proteins of interest are grouped in families and the homologous families are grouped in clans. Each peptidase, family and clan has a unique identifier. The database has recently been expanded to include the protein inhibitors of peptidases, and these are classified in much the same way as the peptidases. Forms of information recently added include new links to other databases, summary alignments for peptidase clans, displays to show the distribution of peptidases and inhibitors among organisms, substrate cleavage sites and indexes for expressed sequence tag libraries containing peptidases. A new way of making hyperlinks to the database has been devised and a BlastP search of our library of peptidase and inhibitor sequences has been added.

Amino Acid Sequence↗

PubMed2003

A comparison of Pfam and MEROPS: two databases, one comprehensive, and one specialised.

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.

Animals↗

PubMed2003

The CHAP domain: a large family of amidases including GSP amidase and peptidoglycan hydrolases.

Cleavage of peptidoglycan plays an important role in bacterial cell division, cell growth and cell lysis. Here, we reveal that several known peptidoglycan amidases fall into a family, which includes many proteins of previously unknown function. The family includes two different peptidoglycan cleavage activities: L-muramoyl-L-alanine amidase and D-alanyl-glycyl endopeptidase activity. The family includes the amidase portion of the bifunctional glutathionylspermidine synthase/amidase enzyme from bacteria and pathogenic trypanosomes. The glutathionylspermidine synthase is thought to be a key component of the alternative pathway in trypanosomes for protection from oxygen-radical damage and has been proposed as a potential drug target. The CHAP (cysteine, histidine-dependent amidohydrolases/peptidases) domain is often found in association with other domains that cleave peptidoglycan. The large number of multifunctional hydrolases suggests that they might act in a cooperative manner to cleave specialized substrates.

Amidohydrolases↗

PubMed2003

Managing peptidases in the genomic era.

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.

Animals↗

PubMed2002

MEROPS: the protease database.

The MEROPS database (http://www.merops.ac.uk) has been redesigned to accommodate increased amounts of information still in pages of moderate size that load rapidly. The information on each PepCard, FamCard or ClanCard has been divided between several sub-pages that can be reached by use of navigation buttons in a frame at the top of the screen. Several important additions have also been made to the database. Amongst these are CGI searches that allow the user to find a peptidase by name, its MEROPS identifier or its human or mouse chromosome location. The user may also list all published tertiary structures for a peptidase clan or family, and search for peptidase specificity data by entering either a peptidase name, substrate or bond cleaved. The PepCards, FamCards and ClanCards now have literature pages listing about 10 000 key papers in total, mostly with links to MEDLINE. Many PepCards now include a protein sequence alignment and data table for matching human, mouse or rat expressed sequence tags. FamCards and ClanCards contain Structure pages showing diagrammatic representations of known secondary structures of member peptidases or family type examples, respectively. Many novel peptidases have been added to the database after being discovered in complete genomes, libraries of expressed sequence tags or data from high-throughput genomic sequencing, and we describe the methods by which these were found.

Amino Acid Sequence↗