Premium
ProtoMap: Automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space
Author(s) -
Yona Golan,
Linial Nathan,
Linial Michal
Publication year - 1999
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/(sici)1097-0134(19991115)37:3<360::aid-prot5>3.0.co;2-z
Subject(s) - structural classification of proteins database , merge (version control) , cluster analysis , hierarchical clustering , transitive relation , protein sequencing , protein family , hierarchy , biology , protein superfamily , computational biology , biological classification , computer science , pattern recognition (psychology) , protein structure , artificial intelligence , genetics , evolutionary biology , mathematics , peptide sequence , information retrieval , combinatorics , market economy , biochemistry , economics , gene
We investigate the space of all protein sequences in search of clusters of related proteins. Our aim is to automatically detect these sets, and thus obtain a classification of all protein sequences. Our analysis, which uses standard measures of sequence similarity as applied to an all‐vs.‐all comparison of SWISSPROT, gives a very conservative initial classification based on the highest scoring pairs. The many classes in this classification correspond to protein subfamilies. Subsequently we merge the subclasses using the weaker pairs in a two‐phase clustering algorithm. The algorithm makes use of transitivity to identify homologous proteins; however, transitivity is applied restrictively in an attempt to prevent unrelated proteins from clustering together. This process is repeated at varying levels of statistical significance. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the protein space into well‐defined groups of proteins, which are closely correlated with natural biological families and superfamilies. Different indices of validity were applied to assess the quality of our classification and compare it with the protein families in the PROSITE and Pfam databases. Our classification agrees with these domain‐based classifications for between 64.8% and 88.5% of the proteins. It also finds many new clusters of protein sequences which were not classified by these databases. The hierarchical organization suggested by our analysis reveals finer subfamilies in families of known proteins as well as many novel relations between protein families. Proteins 1999;37:360–378. ©1999 Wiley‐Liss, Inc.