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An Algorithm to Classify Amino Acid Sequences into Protein Groups of Bothrops jararacussu Venomous Gland

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An algorithm for automatic clustering of database protein sequences from Bothrops jararacussu venomous gland, according to sequence similarities of their domains, is described. The program was written in C and Perl languages. This algorithm compares a domain with each ORF protein sequence in the database. Each nucleotide FASTA sequence generates six ORFs. As a result, the user has a list containing all sequences found in a specific domain and a display of the sequence, domain and number of hits. The algorithm lists only the sequences that present a minimum similarity of 30 hits and the best alignment. This limit was considered appropriate. The algorithm is available in the Internet ( and it can quickly and accurately organizes large database into classes.

Keywords: algorithm; bioinformatic; bothrops jararacussu; proteins; snake venom

Document Type: Review Article


Affiliations: Unidade de Biotecnologia, Universidade de Ribeirao Preto, UNAERP, 14096-380, Ribeirao Preto - SP, Brazil.

Publication date: 2005-05-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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