PepBank - a database of peptides based on sequence text mining and public peptide data sources

We have constructed a new database (PepBank), which at the time of writing contains a total of 19,792 individual peptide entries. The database has a web-based user interface with a simple, Google-like search function, advanced text search, and BLAST and Smith-Waterman search capabilities. The major source of peptide sequence data comes from text mining of MEDLINE abstracts. Another component of the database is the peptide sequence data from public sources (ASPD and UniProt). An additional, smaller part of the database is manually curated from sets of full text articles and text mining results. We show the utility of the database in different examples of affinity ligand discovery.We have created and maintain a database of peptide sequences. The database has biological and medical applications, for example, to predict the binding partners of biologically interesting peptides, to develop peptide based therapeutic or diagnostic agents, or to predict molecular targets or binding specificities of peptides resulting from phage display selection. The database is freely available on http://pepbank.mgh.harvard.edu/ webcite, and the text mining source code (Peptide::Pubmed) is freely available above as well as on CPAN (http://www.cpan.org/ webcite).Peptides have emerged as important affinity ligands for diagnostic and therapeutic medical uses as well as materials for a host of applications in biotechnology. While many excellent databases exist that provide protein sequence data [1-3], protein interaction data [4-9], and peptide data [10-13], a substantial fraction of literature data remains untapped. Unfortunately, the wealth of the peptide sequences in these sources is often difficult to access by modern methods of sequence similarity searching, because peptide sequences are not extracted in a suitable format. We therefore sought to address this issue by developing a combination of automatically mining MEDLINE abstracts for peptide sequences, combining the existing bioinforma