AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen

Abstract

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

Corynebacterium striatum—Got Worse by a Pandemic?

“…The C. striatum strains get more aggressive in the presence of invasive medical devices, including catheters and endotracheal tubes. Consequently, the formation of bacterial biofilms leads to an increase in healthcare costs, extended hospitalization, and presumably the spread of antibiotic resistance genes [22][23][24][25].…”

‘Targeting’ the Search: An Upgraded Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies (B-AMP v2.0) with a Focus on Biofilm Protein Targets

“…C. striatum is a multidrug resistant, biofilm-forming, bacterial pathogen, increasingly associated with a range of wound, eye, skin and ear infections (de Souza et al, 2015; McMullen et al, 2017; Datta et al, 2021; Mhade et al, 2021). In C. striatum , the sortase-pilin machinery encodes the pilus-specific sortase C enzyme, known to be important for biofilm formation.…”

“…As part of updates to B-AMP v1.0, additional AMPs were identified from the DRAMP database v3.0, using previously published methods, and modeled using PEPFOLD3 (Lamiable et al, 2016; Mhade et al, 2021; Shi et al, 2022). The AMP library was updated with new tiles for each additional AMP containing Pep IDs (unique to B-AMP), FASTA sequences, PDB structures and PDBQT files.…”

Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage

“…To combat specific mechanisms of resistance, targeted AMP discovery methods are being developed. A method to discover AMPs with anti-biofilm activity is described in a preprint [ 26 ], and a curated 3D structural and functional repository of AMPs relevant to biofilm studies called B-AMP was recently published [ 26 ]. Finding solutions to these and other challenges in developing AMPs as replacements for conventional small molecule antibiotics is an active field of research [ 56 , 57 , 58 ].…”

“…To resolve this, a scalable, rapid, high throughput in silico methodology built on genomics technologies and able to mine RNA sequencing (RNA-seq) datasets, would greatly aid in the discovery of AMPs funneling into drug development and enhancement processes. There are in silico AMP discovery methodologies presented in earlier studies [ 22 , 23 , 24 , 25 , 26 ], most of which start with processed data such as assembled genomic or protein sequences. Additionally, there are several state-of-the-art tools that perform AMP prediction [ 27 ].…”