Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling
This addresses the challenge of designing biocompatible materials for biomedical and energy applications, though it appears incremental as it builds on existing datasets and methods.
The researchers tackled the problem of screening peptide sequences for specific self-assembly morphologies by introducing PepMorph, a pipeline that generates novel peptides prone to aggregate into fibrillar or spherical shapes with 83% accuracy in intended morphology generation.
Peptide self-assembly prediction offers a powerful bottom-up strategy for designing biocompatible, low-toxicity materials for large-scale synthesis in a broad range of biomedical and energy applications. However, screening the vast sequence space for categorization of aggregate morphology remains intractable. We introduce PepMorph, an end-to-end peptide discovery pipeline that generates novel sequences that are not only prone to aggregate but self-assemble into a specified fibrillar or spherical morphology. We compiled a new dataset by leveraging existing aggregation propensity datasets and extracting geometric and physicochemical isolated peptide descriptors that act as proxies for aggregate morphology. This dataset is then used to train a Transformer-based Conditional Variational Autoencoder with a masking mechanism, which generates novel peptides under arbitrary conditioning. After filtering to ensure design specifications and validation of generated sequences through coarse-grained molecular dynamics simulations, PepMorph yielded 83% accuracy in intended morphology generation, showcasing its promise as a framework for application-driven peptide discovery.