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KAPPA, a simple algorithm for discovery and clustering of proteins defined by a key amino acid pattern: a case study of the cysteine-rich proteins
Author(s) -
Valentin Joly,
Daniel P. Matton
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv047
Subject(s) - executable , computer science , cluster analysis , computational biology , kappa , data mining , pairwise comparison , cysteine , set (abstract data type) , sequence (biology) , protein superfamily , protein sequencing , peptide sequence , bioinformatics , theoretical computer science , artificial intelligence , biology , mathematics , genetics , biochemistry , programming language , enzyme , geometry , gene
Proteins defined by a key amino acid pattern are key players in the exchange of signals between bacteria, animals and plants, as well as important mediators for cell-cell communication within a single organism. Their description and characterization open the way to a better knowledge of molecular signalling in a broad range of organisms, and to possible application in medical and agricultural research. The contrasted pattern of evolution in these proteins makes it difficult to detect and cluster them with classical sequence-based search tools. Here, we introduce Key Aminoacid Pattern-based Protein Analyzer (KAPPA), a new multi-platform program to detect them in a given set of proteins, analyze their pattern and cluster them by comparison to reference patterns (ab initio search) or internal pairwise comparison (de novo search).

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