Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling

• URL: http://arxiv.org/abs/2509.02060v2
• Date: Thu, 04 Sep 2025 08:13:53 GMT
• Title: Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling
• Authors: Nuno Costa, Julija Zavadlav,
• Abstract summary: PepMorph is an end-to-end peptide discovery pipeline.<n>It generates sequences prone to aggregate but self-assemble into a specified fibrillar or spherical morphology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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