The Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org/) is an open-access, comprehensive database containing information related to amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial ...
DBAASP v.2: An enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptidesdoi:10.1093/nar/gkv1174Malak PirtskhalavaAndrei GabrielianPhillip CruzMichael Tartakovsky
DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics Nucleic Acids Res., 49 (2021), pp. D288-D297 CrossrefView in ScopusGoogle Scholar 38. G. Wang, et al. APD3: the antimicrobial peptide database as a tool ...
DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides Nucleic Acids Res, 44 (2016), pp. D1104-D1112 CrossrefView in ScopusGoogle Scholar Possenti et al., 2008 R.A. Possenti, R. Franzolin, E.A. Schammas, J.J.A.A. Dem...
Considering this, we developed a comprehensive user-friendly data repository of antimicrobial peptides (DRAMP), which holds 17349 antimicrobial sequences, including 4571 general AMPs, 12704 patented sequences and 74 peptides in drug development. Entries in the database have detailed annotations, ...
Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we ...
By using database resources, natural AMPs from DRAMP were used for sequence alignment, and a 7-amino-acid consensus sequence (short peptides 1, FLRRIRV-NH2) was apparent in some peptides (Fig.2), and was selected as seed peptide. The second position of most AMPs is tryptophan39, contrib...
Antimicrobial peptides: Interaction with model and biological membranes and synergism with chemical antibiotics[J]. Frontiers in Chemistry,2018,6:204. doi: 10.3389/fchem.2018.00204 [9] PIRTSKHALAVA M, AMSTRONG A A, GRIGOLAVA M, et al. DBAASP v3: Database of antimicrobial/cytotoxic activity ...
Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cos