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Biomedical subjects

A D Michie

Publications and source records attributed to A D Michie.

6 recordsLinked to original sources

CATH--a hierarchic classification of protein domain structures.

BACKGROUND: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. RESULTS: We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast, architecture summarises the shape revealed by the orientations of the secondary structure units, such as barrels and sandwiches. At the topology level, sequential connectivity is considered, such that members of the same architecture might have quite different topologies. When structures belonging to the same T-level have suitably high similarities combined with similar functions, the proteins are assumed to be evolutionarily related and put into the same homologous superfamily. CONCLUSIONS: Analysis of the structural families generated by CATH reveals the prominent features of protein structure space. We find that nearly a third of the homologous superfamilies (H-levels) belong to ten major T-levels, which we call superfolds, and furthermore that nearly two-thirds of these H-levels cluster into nine simple architectures. A database of well-characterised protein structure families, such as CATH, will facilitate the assignment of structure-function/evolution relationships to both known and newly determined protein structures.

Databases, Factual

Novel developments with the PRINTS protein fingerprint database.

The PRINTS database of protein family 'fingerprints' is a diagnostic resource that complements the PROSITE dictionary of sites and patterns. Unlike regular expressions, fingerprints exploit groups of conserved motifs within sequence alignments to build characteristic signatures of family membership. Thus fingerprints inherently offer improved diagnostic reliability by virtue of the mutual context provided by motif neighbours. To date, 600 fingerprints have been constructed and stored in PRINTS, representing a 50% increase in the size of the database in the last year. The current version, 13.0, encodes approximately 3000 motifs, covering a range of globular and membrane proteins, modular polypeptides, and so on. The database is accessible via UCL's Bioinformatics World Wide Web (WWW) server at http://www.biochem.ucl.ac.uk/bsm/dbbrowser / . We describe here progress with the database, its Web interface, and a recent exciting development: the integration of a novel colour alignment editor (http://www.biochem.ucl.ac.uk/bsm/dbbrowser++ +/CINEMA ), which allows visualisation and interactive manipulation of PRINTS alignments over the Internet.

Amino Acid Sequence

Analysis of domain structural class using an automated class assignment protocol.

The extent to which the contemporary dataset of protein structures can be segregated into four structural "classes" as originally defined by Levitt & Chothia in 1976 is examined and a simple method presented for the assignment of protein domains into these classes. Assignments are based on known three-dimensional structures, and for successful assignment it was found that helix/sheet content, contacts between secondary structures and their sequential order had to be used. The procedure attempts to maximise the automatic separation into classes for a dataset of 197 manually classified, non-homologous domains. It was found that approximately 90% of the structures were classified automatically; the remainder were borderline and were left for manual inspection. The method was then applied to a test set of 43 protein domains with similar results. The data support the concept of distinct classes of protein structure, although a few intermediate structures are found, demonstrating that it is possible to define relatively simple parameters complying with commonly accepted nomenclature that automatically define 90% of protein domains with essentially 100% accuracy. However, re-examination of the data also suggested that the previously separate alpha/beta and alpha + beta classes show considerable overlap and are more naturally represented as a single alpha beta class. This large alpha beta class can then be most easily subdivided by consideration of whether the sheets are mainly parallel, antiparallel or mixed. The correlation between structural class and function is discussed, together with the conservation of class within a sequence superfamily. This represents the first step in an automated phenetic description of protein structure complementing the usual phylogenetic approach to protein structure classification.

Algorithms

Structural similarity between the pleckstrin homology domain and verotoxin: the problem of measuring and evaluating structural similarity.

An unexpected structural similarity is described between the pleckstrin homology (PH) domain and verotoxin. This similarity has escaped detection primarily due to the differences in topology that exist between the two proteins. By comparing this result with two previously reported similarities for the PH domain, one with the lipocalins and another with the FK506 binding protein, we discuss the problems of measuring and assessing structural similarities.

Amino Acid Sequence