Comparison of substructural epitopes in enzyme active sites using self-organizing maps.
This paper presents a new algorithm to compare substructural epitopes in protein binding cavities. Through the comparison of binding cavities accommodating well characterized ligands with cavities whose actual guests are yet unknown, it is possible to draw some conclusions on the required shape of a putative ligand likely to bind to the latter cavities. To detect functional relationships among proteins, their binding-site exposed physicochemical characteristics are described by assigning generic pseudocenters to the functional groups of the amino acids flanking the particular active site. The cavities are divided into small local regions of four pseudocenters having the shape of a pyramid with triangular basis. To find similar local regions, an emergent self-organizing map is used for clustering. Two local regions within the same cluster are similar and form the basis for the superpositioning of the corresponding cavities to score this match. First results show that the similarities between enzymes with the same EC number can be found correctly. Enzymes with different EC numbers are detected to have no common substructures. These results indicate the benefit of this method and motivate further studies.
Kupas
Katrin
K
Data Bionics Research Group, Department of Computer Science, University of Marburg, Germany.
Ultsch
Alfred
A
Klebe
Gerhard
G
D000465:Algorithms; D020134:Catalytic Domain; D016000:Cluster Analysis; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D000939:Epitopes; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results
D004798:Enzymes; D000939:Epitopes; D008024:Ligands