Machine learning for antimicrobial peptide identification and design.

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues.

Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance.

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning and deep learning assist in pathogen detection, resistance prediction, and drug discovery. These tools improve antibiotic stewardship and identify effective compounds such as antimicrobial peptides and small molecules.

Small and Versatile Cyclotides as Anti-infective Agents.

Plants provide an abundant source of potential therapeutic agents, including a diverse array of compounds, such as cyclotides, which are peptides known for their antimicrobial activity. Cyclotides are multifaceted molecules with a wide range of biological activities. Their unique topology forms a head-to-tail cyclic structure reinforced by a cysteine knot, which confers chemical and thermal stability.

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

A variety of deep generative models have been adopted to perform functional protein generation. Compared to 3D protein design, sequence-based generation methods, which aim to generate amino acid sequences with desired functions, remain a major approach for functional protein generation due to the abundance and quality of protein sequence data, as well as the relatively low modeling complexity for training. Although these models are typically trained to match protein sequences from the training data, exact matching of every amino acid is not always essential.

Microproteins: emerging roles as antibiotics.

Recent advances in computational prediction and experimental techniques have detected previously unknown microproteins, particularly in the human microbiome. These small proteins, produced by diverse microbial species, are emerging as promising candidates for new antibiotics.