About

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) has been created to provide users with detailed information on experimentally tested peptides regarding their chemical structure and activity against specific targets. The database is manually curated and contains information on ribosomal, nonribosomal, and synthetic peptides that show antimicrobial activity as Monomers, Multimers, and Multi-Peptides.

Monomer – consists of one polypeptide chain (for example, Melittin, Magainin).

Multimer – consists of two or more polypeptide chains with an interchain covalent bond(s) (for example, Distinctin, Halocidin).

Multi-Peptide – consists of two or more distinct polypeptide chains without interchain covalent bonds. Their antimicrobial activity requires synergistic interaction between the peptides in equimolar concentrations as the individual peptides may have little or no activities (for example, Enterocin X, Salivaricin P).

Project was initially supported by Shota Rustaveli National Science Foundation (GEORGIA) and Centre National de la Recherche Scientifique (FRANCE).

Now it is a joint project between the Laboratory of Bioinformatics of Ivane Beritashvili Center of Experimental Biomedicine (IBCEB, GEORGIA) and  the Office of Cyber Infrastructure and Computational Biology (OCICB/NIAID/NIH, USA)

Publications

Malak Pirtskhalava, Anthony A Amstrong, Maia Grigolava, Mindia Chubinidze, Evgenia Alimbarashvili, Boris Vishnepolsky, Andrei Gabrielian, Alex Rosenthal, Darrell E Hurt, Michael Tartakovsky, DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D288–D297, https://doi.org/10.1093/nar/gkaa991

Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei Gabrielian, Alex Rosenthal, Darrell E Hurt, Michael Tartakovsky, Malak Pirtskhalava, Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction, Briefings in Bioinformatics, Volume 23, Issue 4, July 2022, bbac233, https://doi.org/10.1093/bib/bbac233

Pirtskhalava M, Gabrielian A, Cruz P, Griggs HL, Squires RB, Hurt DE, Grigolava M, Chubinidze M, Gogoladze G, Vishnepolsky B, Alekseev V, Rosenthal A, and Tartakovsky M. DBAASP v.2: an Enhanced Database of Structure and Antimicrobial/Cytotoxic Activity of Natural and Synthetic Peptides. Nucl. Acids Res., 2016, 44 (D1), D1104-D1112.

Vishnepolsky B, Gabrielian A, Rosenthal A, Darrell EH, Tartakovsky M, Managadze G, Grigolava M, Makhatadze GI, and Pirtskhalava M. Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria. J. Chem. Inf. Model. 2018, 58, 1141-1151.

Vishnepolsky B and Pirtskhalava M.  Prediction of Linear Cationic Antimicrobial Peptides Based on Characteristics Responsible for Their Interaction with the Membranes J. Chem. Inf. Model. 2014, 54, 1512−1523.

Gogoladze G, Grigolava M, Vishnepolsky B, Chubinidze M, Duroux P, Lefranc MP and Pirtskhalava M. DBAASP: Database of Antimicrobial Activity and Structure of Peptides. FEMS Microbiol Lett. 2014, 357, 63-68.