Antimicrobial Peptides and Activities

This study shows that helicity and membrane permeabilization are not universal requirements for cathelicidin-mediated killing of gram-negative bacteria, and identifies lead compounds with improved selectivity.

The paper demonstrates that adding an N-terminal biphenyl motif from LL-37 to short cathelicidin peptides significantly enhances their antibacterial activity against gram-negative bacteria.

The paper identifies LL-37 variants that kill gram-negative bacteria without membrane disruption and bacteria that are permeabilized without being killed, suggesting alternative mechanisms of antibacterial activity.

This study examines how metal ion environments affect the antimicrobial activity of PrAMPs against Pseudomonas aeruginosa and Escherichia coli, revealing ion-dependent changes in their mode of action.

This study shows human antimicrobial peptides like LL-37 activate bacterial PhoQ-PhoP signaling independently of their bactericidal effects, revealing a new mechanism of bacterial response to host peptides.

PepSeek identifies functional peptides using a combination of large language models and specialized models, achieving high accuracy in antimicrobial peptide discovery.

This paper develops an antibody-drug conjugate targeting Neisseria gonorrhoeae that uses bacterial immune evasion to selectively kill drug-resistant strains.

The paper develops a time-resolved single-cell pipeline to analyze antimicrobial peptides in E. coli, revealing two classes with opposing functional trade-offs in their mechanisms and efficacy against biofilms.

AIPAMPDS designs and screens antimicrobial peptides using AI, identifying 10 lead peptides with high antimicrobial activity and low hemolysis.

The paper uses deep learning to identify novel antimicrobial peptides from proteasomal cleavage products, finding 21 sequence-diverse peptides with potential therapeutic applications against drug-resistant pathogens.

This paper introduces AMPCLGPT, a model that generates antimicrobial peptides and predicts their MIC values using contrastive learning and pre-training.

AMP-Atlas uses AI to predict antimicrobial peptides from sequences, outperforming existing methods and identifying new candidates in human bacteria.

This study identifies and characterizes antimicrobial peptides in human colostrum using LC-HRMS and bioinformatics, revealing novel peptides with potential antifungal activity.

This study investigates the structural and functional properties of citropin 1.3, an antimicrobial peptide from the Southern bell frog, revealing its ability to form distinct fibrillar structures and interact with membranes and cells.

This study shows SEVI fibrils have antibacterial activity against urogenital pathogens and are induced by bacterial surface molecules.

This study identifies a synthetic peptide, PAN4, with antimicrobial, stress-tolerant, and anti-tumor properties in Drosophila, showing potential for improving honeybee health and resilience.

This paper introduces ApexDuo, a deep learning model that generates peptides with both cell-penetrating and antimicrobial properties, and demonstrates its effectiveness against intracellular pathogens.

This study investigates protamine's antifungal activity against Candida species, finding it inhibits growth and causes membrane rupture and ROS induction.

This study shows that helical peptide polymers enhance antibiotic efficacy against multidrug-resistant bacteria by 2-256-fold while maintaining low toxicity.

The paper performs a meta-analysis of 8.6 million antimicrobial peptides to identify class-specific therapeutic candidates using machine learning and structural analysis.

The paper develops bacteriocin cocktails using cell-free synthesis to target and eliminate antibiotic-resistant bacteria, demonstrating effectiveness in vivo.

The paper evaluates anti-Candida and antibiofilm activity of epinecidin-1 and its variants, finding variant-2 most effective with reduced MICs and biofilm disruption.

The paper develops a model to predict MIC values of peptides against E. coli using machine learning and a web server for peptide design.

This study evaluates the antimicrobial efficacy of frog skin peptides against bacteria and fungi, finding Magainin 2 and Bombesin effective against Bacillus subtilis, Escherichia coli, and Trichoderma harzianum.

This study uses a mutasynthetic library to identify optimized darobactin derivatives that effectively treat bacterial lung infections in mice.

This study identifies and characterizes TaW662, an antifungal peptide from wheat, showing inhibition of Blumeria graminis f. sp. tritici growth and potential as a fungicide.

This paper demonstrates that BAM complex-targeting peptides, particularly dimeric forms, exhibit bactericidal activity against multidrug-resistant P. aeruginosa and A. baumannii.

AMP-Diffusion generates functional antimicrobial peptides using a latent diffusion model integrated with the ESM-2 protein language model, showing alignment with experimentally validated AMPs in pseudo-perplexity and physicochemical properties.

The paper maps sequence-function relationships in proline-rich antimicrobial peptides using SAMP-Dep, identifying mutational patterns and epistasis that influence activity and specificity.

This study designs gramicidin-derived antimicrobial peptides using molecular dynamics simulations and evaluates their efficacy based on structural and conductance properties.