Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is the manually-curated database. It has been developed to provide the information and analytical resources to the scientific community in order to develop antimicrobial compounds with the high therapeutic index.
Antimicrobial peptides (AMP) represent ancient defense molecules which are widespread in all forms of life, from multi-cellular organisms to bacterial cell. In many cases, their primary role is in the killing of invading pathogenic organisms (bacteria, fungi, some parasites and viruses). Recent studies show that some AMPs are active against pathogens that are resistant to conventional antibiotics. Hence, AMPs are widely used in medicine, food preservation and agriculture.
Structure/activity study on peptides in order to reveal physical-chemical parameters that respond to antimicrobial activity and high therapeutic index requires: full information on structure (chemical, 3D) and activity of natural and artificial peptides for which antimicrobial activity against particular target species have been evaluated experimentally. This means having information: regarding posttranslational modification (chemical groups bound to the N and C termini of peptide; cycles; modification which take place in particular amino acid) and 3D structure; about antimicrobial and hemolytic(cytotoxic) activities and experimental conditions in which activities were estimated.
DBAASP provide users with information on detailed structure (chemical, 3D) and activity for those peptides, for which antimicrobial activity against particular target species have been evaluated experimentally. The database contains information on ribosomal, non-ribosomal and synthetic peptides that show antimicrobial activity as Monomers, Dimers and Multi-Peptides.
Benefits and Features
DBAASP is the depository of the necessary information for structure / activity study.
Prediction service allows to reveal the existence of antimicrobial activity for the queried peptides based on amino acid sequence information only. The tools providing prediction with the high accuracy allow to conduct task-oriented design of the new antibiotics and consequently diminish costs of production.
DBAASP offers users search by particular target species and measure of activity and is presenting search results in the form of the ranking list of activity values.
DBAASP allow to form the positive (AMP) and negative (non-AMP) experimentally validated sets of peptides against particular target species. Consequently DBAASP provides the development of the machine–learning based prediction tool and the effective method of the design of antibiotics.
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.