Antimicrobial Peptides and Activities

The paper designs an antimicrobial peptide based on the structure of C. difficile translation initiation factor 1, showing strong antibacterial activity against C. difficile and other bacteria.

This paper introduces AntiBP3, a hybrid method for predicting antibacterial peptides effective against gram-positive, gram-negative, and gram-variable bacteria, achieving high AUC scores on independent datasets.

This paper develops a theoretical framework to link physicochemical properties of antimicrobial peptides with their synergistic activity, finding that less similar peptides exhibit greater synergy due to complementary antibacterial actions.

This paper reveals that daptomycin forms a stable complex with phosphatidylglycerol and calcium for selective bacterial membrane uptake, explaining its mechanism and potential resistance strategies.

The paper characterizes six gut-derived nisin-like lantibiotics, evaluates their antimicrobial activity against pathogens and commensals, and identifies key residues affecting their activity.

This study investigates how melittin interacts with Gram-negative bacterial outer membranes, showing it anchors and translocates without disrupting membrane integrity.

This paper develops a prodrug that releases an antimicrobial peptide upon activation by proteases from Candida albicans and Porphyromonas gingivalis, enabling both treatment and detection of these pathogens.

This study explores lipid modifications of a chitin-binding domain to enhance antifungal activity, finding that specific lipid lengths improve efficacy against Trichoderma viride without significant cytotoxicity.

This paper identifies bibacillin 1, a two-component lantibiotic from Bacillus thuringiensis, showing cooperative bactericidal activity and distinct mechanism of action compared to other lantibiotics.

This study uses unsupervised learning and molecular dynamics to show that mixing two AMPs reduces their conformational diversity and alters aggregation behavior, revealing how synergy influences their structural dynamics.

Autoencoder design plus a roughly 24-hour cell-free screen tested 500 candidates over five rounds, finding 30 functional AMPs; six were broad-spectrum with no resistance emerging in E. coli.

PandoraGAN generates novel antiviral peptides using a GAN model trained on curated data, producing sequences with properties similar to known active peptides.

This study identifies new macrocyclic peptide antibiotics active in human serum by screening a library under serum conditions, finding that sequence factors like length, charge, and disulfide bonds influence activity.

This study introduces a novel computational pipeline to discover antimicrobial peptides (AMPs) in helminths, revealing unique AMP-like peptides and highlighting their potential as novel antimicrobials.

This paper introduces HydrAMP, a deep generative model for creating potent antimicrobial peptides, which outperforms existing methods and validates nine new peptides in lab tests.

The paper investigates the antiviral effects of cyclic lipopeptides against SARS-CoV-2 by assessing their ability to inhibit membrane fusion in vitro.

The paper assesses the impact of missense mutations on human LL-37 structure and antimicrobial activity using computational methods, finding some mutations may reduce activity and contribute to inflammatory diseases.

The paper introduces B-AMP v2.0, an upgraded repository of antimicrobial peptides and biofilm targets, with tools for in silico evaluation of AMPs against specific bacterial biofilm proteins.

This study shows that AMP prediction benchmarks are biased due to negative data selection and introduces a web server for fair benchmarking.

The paper determines the cryo-EM structures of two amphibian antimicrobial peptides, revealing cross-β amyloid fibrils and their structural polymorphism.

The paper compares machine learning algorithms for predicting antimicrobial peptides (AMPs) specific to microbial strains, finding Random Forest and AdaBoost perform best with improved accuracy using genome characteristics.

AMPlify is a deep learning model that predicts antimicrobial peptides from bullfrog genomes, identifying novel peptides effective against WHO priority pathogens including drug-resistant bacteria.

This paper introduces LMPred, a model using pre-trained language models and convolutional neural networks to predict antimicrobial peptides with high accuracy.

The paper shows that certain small molecules, like niacinamide, enhance the activity of antimicrobial peptides against Staphylococcus aureus by altering bacterial membrane properties.

This paper introduces a new antimicrobial activity predictor called 'Sense the Moment' that outperforms deep-learning methods in distinguishing antimicrobial peptides from shuffled versions.

The paper evaluates taxonomic bias in 10 AMP prediction tools across 19 invertebrate taxa using 1525 AMP and 3050 non-AMP sequences, finding significant variation in performance between taxa.

The paper identifies 155 novel host defense peptides from nine transcriptomes of seven dung beetle species using transcriptomic data and sequence similarity analysis.

The paper synthesizes hybrid lanthipeptides from nisin and ripcin templates, showing selective antimicrobial activity against S. aureus and resistance to nisin resistance protein.

This paper identifies novel anionic cecropins in the spruce budworm with a poly-L-aspartic acid C-terminus, showing structural and functional differences from conventional cationic cecropins and suggesting potential anticancer activity.

MACREL identifies high-quality antimicrobial peptide candidates from genomes and metagenomes using a novel set of peptide features and classifiers.