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Designing antimicrobials with programmable mechanism and safety

OmegAMP generates antimicrobial peptides with controlled physicochemical properties and safety profiles, showing activity against drug-resistant bacteria in vitro and in vivo.

Peptide structural plasticity is predictable from sequence and environment

ApexFold predicts peptide secondary structure changes across environments using sequence and physicochemical descriptors, outperforming static predictors.

Ancient human mitochondrial genomes encode antimicrobial peptides

The paper identifies antimicrobial peptides encoded in ancient human mitochondrial genomes and validates their activity against bacteria.

A Conditional Variational Autoencoder with QSAR-Guided Surrogate-Weighted Fine-Tuning and Cross-Entropy Optimization for Targeted Antimicrobial Peptide Generation

This paper introduces a conditional variational autoencoder for antimicrobial peptide generation, combining QSAR data and cross-entropy optimization to overcome limitations in generative models.

A generative artificial intelligence approach for antibiotic optimization

This paper introduces APEXGO, a generative AI framework for optimizing antimicrobial peptides, achieving high success rates in enhancing antimicrobial activity and outperforming existing methods.

Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis

The paper integrates kidney and urine proteomics with peptidomics and machine learning to identify encrypted antimicrobial peptides as functional effectors in human pyelonephritis.

Modification-aware AI enables terminal chemical modifications for peptide design and discovers potent antimicrobials

This study introduces Termini, an AI framework for designing antimicrobial peptides with terminal modifications, achieving high hit rates and therapeutic potential in vitro and in vivo.

Designing AI-programmable therapeutics with the EDEN family of foundation models

This paper introduces the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

Molecular response to the non-lytic peptide bac7 (1-35) triggers disruption of Klebsiella pneumoniae biofilm

This paper investigates the anti-biofilm mechanism of bac7 (1-35) against Klebsiella pneumoniae, showing it disrupts bacterial membranes and ribosomal function, leading to biofilm collapse and reduced dissemination.

Integrative machine learning predicts activating kinase mutations for precision oncology

Kinome-AI predicts activating kinase mutations using machine learning, achieving high accuracy without requiring structural inputs for new mutations.

Atom-level backbone engineering preserves peptide function while enhancing stability

This study evaluates four peptide backbone modifications to enhance stability while preserving function, finding that azapeptides maintain activity and offer improved stability.

A deep reinforcement learning platform for antibiotic discovery

This paper introduces a deep reinforcement learning framework for designing new antibiotics, producing potent peptides with broad-spectrum activity against bacteria.

Generative latent diffusion language modeling yields anti-infective synthetic peptides

This study introduces AMP-Diffusion, a generative model for designing antimicrobial peptides, which produces candidates with broad-spectrum antibacterial activity and low toxicity.

Discovery of antibiotics in the archaeome using deep learning

This study uses deep learning to analyze archaeal proteomes, identifying 12,623 potential antimicrobial molecules, with 93% showing in vitro activity and one candidate effective in vivo against bacterial infections.

Venomics AI: a computational exploration of global venoms for antibiotic discovery

This study identifies 53 potent antimicrobial venom-derived peptides using computational methods and experimental validation, showing promise for combating antibiotic-resistant infections.

Defining the importance of the arginine loop region of protegrin-1 for antimicrobial activity towards colistin-resistant Klebsiella pneumoniae.

The paper investigates how mutations in protegrin-1 affect its antimicrobial activity against colistin-resistant Klebsiella pneumoniae, finding structural changes have a greater impact than charge reduction.

Protease-Resistant Azapeptide GLP-1 Analogue Improves Metabolic Control in Diet-Induced Obesity

Proteasome-derived antimicrobial peptides discovered via deep learning

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.

Improving functional protein generation via foundation model-derived latent space likelihood optimization

The paper improves functional protein generation by optimizing likelihood in both amino acid sequence and latent space, outperforming existing models in AMP and MDH generation.

Key-cutting machine: A novel optimization framework for tailored protein and peptide design

The paper introduces KCM, an optimization framework for protein and peptide design that outperforms generative models by enabling precise backbone geometry control with minimal computational resources.

Design of multimodal antibiotics using deep learning

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.

Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties

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.

Dual quorum-sensing control of purine biosynthesis drives pathogenic fitness of Enterococcus faecalis

This study identifies two quorum-sensing systems in E. faecalis that regulate purine biosynthesis, enhancing its pathogenicity in macrophages and murine infections.

AllTheBacteria: a community resource empowers biology and discovers novel peptide antibiotics

A uniformly processed set of 2,440,377 bacterial and archaeal genomes with search tools and annotations, plus an AI-mined peptide set yielding ATB20, active in a mouse skin infection model.

Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection.

The paper examines genetic variation and natural selection at SARS-CoV-2 host genes in diverse populations and links selected variants to clinical phenotypes in COVID-19.

Tough Hydrogel-Based Biocontainment of Engineered Organisms for Continuous, Self-Powered Sensing and Computation

This paper develops a hydrogel-based containment system for genetically modified microorganisms to enable safe, retrievable, and functional biosensing applications.

Engineering Phage Host-Range and Suppressing Bacterial Resistance Through Phage Tail Fiber Mutagenesis

This paper develops a method to engineer phage tail fibers to alter host range and suppress bacterial resistance, showing effectiveness in vitro and in vivo.