Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram

Abstract

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Thrombin-Derived Peptides Potentiate the Activity of Gram-Positive-Specific Antibiotics against Gram-Negative Bacteria

“…Indeed, the synergistic activity of peptides 1 , 2 , and 3 validated this hypothesis (See Table 2 ). Notably, this represents the first demonstration of the synergistic activity for these peptides even though their antimicrobial activity has been well-studied [ 26 , 35 , 36 , 50 , 51 , 52 ]. Peptide 1 exhibits synergy comparable to PMBN ( Table 1 ).…”

The role of full-length apoE in clearance of Gram-negative bacteria and their endotoxins

“…ApoE was scanned for antimicrobial peptides (AMPs) using two in silico predictors: AmpGram ( 26 ) and a method for detection of “cryptic” AMPs, utilizing amino acid properties and peptide length developed by Pane et al. ( 27 ).…”

“…ApoE was scanned for antimicrobial peptides using two in silico predictors: AmpGram (26) and a method for detection of "cryptic" antimicrobial peptides, utilizing amino acid properties and peptide length developed by Pane et al (27). AmpGram is an AMP predictor, utilizing n-grams and a random forest classifier to predict AMPs with high accuracy whilst allowing for high throughput proteomic screening.…”

“…Two in silico predictors were used on the apoE sequence to scan for antimicrobial peptide(s). The second predictor used is called AmpGram (26). AmpGram is a novel predictor which utilizes a machine learning approach and was used to scan apoE for 10-mers.…”

The Puzzle of Metabolite Exchange and Identification of Putative Octotrico Peptide Repeat Expression Regulators in the Nascent Photosynthetic Organelles of Paulinella chromatophora

“…TMHs were predicted with TMHMM 2.0 (Krogh et al, 2001) and CCTOP (Dobson et al, 2015), pore-lining residues in TMHs were predicted with MEMSAT-SVM-pore (Nugent and Jones, 2012), and AMP peptides were predicted with AmpGram (Burdukiewicz et al, 2020). Sequence motifs were discovered using MEME 5.0.5 algorithm (Bailey and Elkan, 1994) in classic mode and visualized with WebLogo (Crooks et al, 2004), number and position of motifs in protein sequences were determined with MAST 5.0.5 using default settings (Bailey and Gribskov, 1998).…”