Biology subjects

Jegelka, S.

Publications and source records attributed to Jegelka, S..

2 recordsLinked to original sources

bioRxiv2026

Designing antimicrobials with programmable mechanism and safety

Antimicrobial peptides (AMPs) are a promising solution to antimicrobial resistance, yet generative models for their design cannot control the physicochemical properties and motifs that shape activity and selectivity. Here, we present OmegAMP, a conditional diffusion framework controlling net charge, mean hydrophobicity, and sequence length, supporting de novo, analog, and motif-guided design. Across 204 wet-lab characterized peptides, de novo generation yielded antimicrobials with broad activity against multidrug-resistant Gram-negative isolates. Analog generation converted six inactive prototypes into antimicrobials, with the prototype determining each analog's membrane-disruption mode and mammalian-cell safety. Motif-guided analog generation preserved lipopolysaccharide engagement of active prototypes, and a redesigned non-antimicrobial leucine zipper acquired antimicrobial activity while retaining DNA-perturbing character in vitro. In murine skin and thigh infection models, leads reduced bacterial burden, with a motif-guided DNA-perturbing lead matching the fluoroquinolone control systemically. OmegAMP opens a programmable route to new peptide antibiotics whose mechanism and safety follow from the chosen prototype.

bioinformatics↗

bioRxiv2025

Fast and Accurate Peptide - MHC Structure Prediction via an Equivariant Diffusion Model

Accurate modeling of peptide-MHC (Major Histocompatibility Complex) structures is critical for the development of personalized cancer vaccines and T-cell therapies, as MHC proteins present peptides on the cell surface for immune recognition. Here, we introduce MHC-Diff, a specialized SE(3)-equivariant diffusion model leveraging Geometric Deep Learning to predict the 3D C-alpha atom structures of peptide-MHC complexes with high accuracy. Unlike previous deep learning models that generate a single static structure, our probabilistic approach samples multiple diverse candidates, capturing the inherent flexibility of peptide-MHC binding. Validated on the Pandora benchmark and experimental X-ray crystallography data, MHC-Diff achieves sub-angstrom accuracy, outperforming existing methods by a large margin while matching the inference speed of the fastest available techniques. By enabling rapid and highly accurate structure prediction across diverse peptide lengths and MHC alleles, MHC-Diff provides a powerful new tool for accelerating the design of next-generation cancer vaccines and T-cell therapies.

bioinformatics↗