Antimicrobial Peptides and Activities

This study identifies antifungal peptides from MtDef5 and shows their potential as bio-fungicides against gray mold and anthracnose diseases.

The paper investigates how marsupial neonates use cathelicidin antimicrobial peptides for immune protection through genomic clustering and enhancer sharing, showing their effectiveness in a sepsis model.

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

This study identifies the distribution of SbmA/BacA and BclA antimicrobial peptide transporters across bacteria, showing convergent evolution and their role in host-microbe interactions.

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 identifies novel antimicrobial peptides from the UniProtKB/Swiss-Prot database using AMPlify, prioritizing 40 for chicken applications and testing 38 for antimicrobial activity.

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 designs bifunctional antimicrobial peptides using deep learning, discovering 16 peptides with both antibacterial and antiviral activity, including two validated against drug-resistant bacteria and viruses.

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.

The paper introduces a bacteria-centric AMP predictor that identifies more bacterial antimicrobial peptides from real genomes than fake ones, outperforming existing predictors.

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 shows that lasso peptides sviceucin and siamycin I reduce virulence and restore vancomycin effectiveness against resistant bacteria by targeting histidine kinases.

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.

The paper develops a deep learning pipeline to identify antimicrobial peptides from the gut microbiome of Blattella germanica, finding two AMPs with low toxicity and antimicrobial activity.

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 different disulfide bond pairings in chickpea NCR13 peptides lead to distinct antifungal activities and modes of action.

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

This paper identifies novel class II bacteriocins in Streptococcus salivarius using bioinformatics and synthetic biology, revealing 50 candidates active against Gram-positive pathogens.

This paper identifies a peptide, P92, that targets AbOmpA of A. baumannii, reducing bacterial pathogenicity without inhibiting growth, showing efficacy in multiple infection models.

The paper identifies twelve new class IIb microcins from seven Enterobacteriaceae species and validates their activity against Gram-negative pathogens and plant pathogens.

SAMP identifies antimicrobial peptides using an ensemble model with new features, outperforming existing methods on multiple datasets.

This study evaluates the antimicrobial and antibiofilm activity of human recombinant H1 histones against Pseudomonas aeruginosa, showing efficacy and safety in vitro and in vivo.

This study tests peptides from non-immune proteins for antimicrobial and immunomodulatory effects, finding potent activity against bacteria and immunomodulation in vitro and in mouse models.

ProT-Diff generates novel antimicrobial peptides using a protein language model and diffusion model, producing 34 active peptides against bacteria with 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.