title: IPSA-Inductive Protein Structure Analysis.
abstract: The Inductive Structure Protein Analysis (IPSA) project presents a new method for investigating protein structure. IPSA includes the creation of a new database which was designed specifically for the analysis of protein structure by statistics and machine learning. The Protein Representation Language (PRL) database includes explicit and symbolic representations of geometrical, topological and chemophysical information about secondary structures and the relationships between secondary structures. The IPSA methodology consists of: the use of PRL information to produce a new database of examples of secondary structures which associate together (examples of possible super-secondary structures); then the use of a variety of clustering techniques to produce a consensus clustering of these examples (super-secondary structures); these super-secondary structures are finally examined to uncover any biological features of significance. We have applied this method to find simple super-secondary structures consisting of pairs of alpha-helices. We found four well-defined super-secondary structures, one formed exclusively by long range interactions, and another in association with an additional element of secondary structure (alpha t alpha-motif). Examinations were carried out using homologous pairs and conformational fits which confirm our clustering.
authors.affiliation: Brainware GmbH, Berlin, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Schulze-Kremer
authors.forename: R D
authors.initials: RD
authors.lastname: King
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins