DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

Abstract

The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

“…,10). Details of calculations of the distribution of i-spaced amino acid pairs are presented in the last paper on DBAASP [17]. Figure 3a,b show that the abundance of the pairs of basic amino acids with 3, 6, 7 residues between them (Figure 3a) and pairs of hydrophobic amino acids with 2, 3, 6, 7, 10 residues between them (Figure 3b) is not the result of random processes.…”

“…Fi frequencies are estimated for random sequences generated by shuffling of the sequences of the considered set of peptides. The assessments were performed using the tools of the page of "Statistics" (https://dbaasp.org/statistics, accessed on 20 March 2021) of the DBAASP [17].…”

“…It is important to know the peculiarities of amino acid composition and residue distributions in the amino acid sequences of AMPs. To assess the composition and distribution of amino acids in different sets of AMPs, data from the DBAASP database [ 17 ] was explored. When looking for the peculiarities of AMP sequences, it is reasonable to use the data on peptides that are under evolutionary pressure, meaning that only ribosomal peptides should be used.…”

“…The distribution of DiSAAP for the basic (R, K, H) and hydrophobic (V, I, L, F, M) residues are presented in Figure 3 a,b. Brown middle bars correspond to observed frequencies of DiSAAP (fi) in the set of ribosomal AMPs (derived from the DBAASP data [ 17 ]). The right, dark green and left, dark blue bars correspond to the values of Fi + 3σ i and Fi − 3σ i respectively, where Fi is the average value of the frequencies assessed on the basis of random sequences and σ i are their standard deviation ( i = 0,1,2,…,10).…”

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions

“…It also contributed to the development of several databases such as dbAMP ( Jhong et al, 2019 ), LAMP2 ( Ye et al, 2020 ), PlantPepDB ( Das et al, 2020 ), ADAPTABLE ( Ramos-Martín et al, 2019 ), and starPepDB ( Aguilera-Mendoza et al, 2019 ). Most recently, another updated version of DBAASP (DBAASP v3) ( Pirtskhalava et al, 2021 ) has been released to include new content and additional user services. This database has continuously developed its predictive tools to be employed in the de novo design of peptide-based drugs.…”