Biomolecular structure is commonly predicted from sequence as a single structural model, yet many molecules function through conformational ensembles that reorganize with their surroundings. Whether such structural responsiveness can be learned jointly from sequence and environmental context remains unresolved. Here, we use short peptides as an experimentally tractable system to test this principle. We assembled a large experimental multi-environment dataset for peptides’ secondary structure, comprising more than 1,500 peptides and more than 5,500 peptide–environment observations across aqueous, co-solvent, and membrane-mimicking conditions. We developed ApexFold, an environment-conditioned AI framework that combines sequence representations with physicochemical descriptors of the surrounding medium to predict circular-dichroism-derived fractions of α-helical, β-like, and unstructured conformations. In two later-collected panels excluded from model development, ApexFold captured the direction and magnitude of environment-induced structural redistribution and the peptide-specific degree of plasticity, while outperforming solvent-agnostic and composition-based baselines. Static structural references, which return a single conformation, cannot represent these condition-dependent response profiles. These results establish structural responsiveness as a learnable property of sequence and environment, extending biomolecular prediction beyond static structure toward predicting—and ultimately designing—how molecules respond to the contexts in which they function.
M. Torres, Hanqun Cao, César de la Fuente-Núñez· bioRxiv· 0 citations
Methicillin-resistant Staphylococcus aureus (MRSA) remains a major clinical challenge, particularly intracellular MRSA infections are difficult to treat because antimicrobial agents must combine stability, host-cell access and bacterial target engagement. Cyclotides offer highly stable cyclic scaffolds for peptide engineering, but their use as intracellular antimicrobial protein inhibitors remains largely unexplored. Here, we engineered a cyclotide-grafted derivative of the antimicrobial peptide KTR by inserting it into the MCoTI-I scaffold, generating the cyclic construct MCo-KTR2. Molecular docking and molecular dynamics suggested potential interactions between MCo-KTR2 and the resistance-associated penicillin-binding protein PBP2a. Site-directed mutagenesis and fluorescence polarization assays indicated that specific residues contribute to binding in vitro. Although MCo-KTR2 displayed lower activity than linear KTR in standard MIC assays, cyclotide grafting increased serum stability by more than 30-fold and enhanced cellular uptake, colocalising with cytosolic S. aureus during infection. These properties were associated with improved activity against intracellular bacteria without detectable cytotoxicity or haemolytic activity. Furthermore, MCo-KTR2 showed higher antibacterial activity when combined with the membrane-active compound Visomitin as well as in combination with vancomycin and gentamicin. Together, these findings identify cyclotide grafting as a strategy to improve peptide stability and intracellular delivery, and support MCo-KTR2 as a scaffold for further optimization against intracellular MRSA infections.
Álvaro Mourenza, Jesús Llano-Verdeja, Pablo Castañera et al.· Molecular Biomedicine· 0 citations
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
A. Crysler, César de la Fuente-Núñez· Cell Host and Microbe· 1 citation