Diff-SelfA-PepGSI: A Diffusion-Based Framework for De Novo Generation, Screening, and Morphological Identification of Self-Assembled Peptide Sequences

Abstract

Peptide self-assembly is a ubiquitous molecular phenomenon in which short amino acid sequences spontaneously organize into diverse nanostructures. These assemblies exhibit distinct morphologies closely linked to their biological functions and potential applications, making them attractive candidates for biomaterials in drug delivery, regenerative medicine, and diagnostics. However, designing and predicting peptide sequences with specific self-assembled morphologies remains highly challenging due to the complex interplay of noncovalent interactions, including hydrogen bonding, hydrophobic effects, and π–π stacking. Although deep learning has enabled accurate prediction of peptide self-assembly propensity, rational de novo design of peptides with targeted assembly behavior remains largely unexplored. In this study, we propose Diff-SelfA-PepGSI, a novel pipeline for the morphology-specific design of self-assembling peptides. This three-stage framework comprises: (i) generation, in which a diffusion-based generative model produces candidate self-assembling peptide sequences; (ii) screening, in which pretrained protein language models extract deep semantic features and a deep discriminative network performs sequence optimization and property screening; and (iii) identification, in which a dual-branch structure extracts features and a dedicated classifier identifies the specific nanostructures formed. By integrating sequence generation, predictive screening, and morphology identification, this workflow establishes a proof-of-concept de novo design pipeline that links peptide sequences directly to their target morphologies. Diff-SelfA-PepGSI demonstrates superior performance compared to state-of-the-art models in both screening and identification tasks. Experimental validation preliminarily demonstrates the feasibility of the de novo design pipeline, successfully generating self-assembling peptides such as LAPFLA. Under the reported experimental conditions, 4 of 11 generated candidates (36.4%) exhibited the target morphology predicted by the model. Within the challenging context of de novo morphology-specific peptide design, this success rate supports the potential of Diff-SelfA-PepGSI as an early stage self-assembling peptide generation platform.