DeepSAP: An Integrated Generative and Predictive Deep-Learning Framework for Controllable Design of Self-Assembling Peptides

Clicks: 33
ID: 331649
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #173 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Self-assembling peptides (SAPs), particularly short sequences whose amino acid composition dictates their spontaneous organisation, are emerging as versatile building blocks in biomedicine and bio-optoelectronic materials. As the thermodynamic and kinetic behaviours of peptide self-assembly are affected by intrinsic factors, such as amino acid sequence, length, and hydrophobicity, the controllable design of SAPs with tailored properties is essential to achieve specific structures and functions for various application scenarios. However, existing computational approaches, which primarily focus on prediction or heuristic screening, lack conditional generative capability of SAPs. Results We present DeepSAP, an end-to-end framework integrating conditional peptide generation with predictive screening for property-guided design of SAPs. By pretraining on large-scale peptide datasets and fine-tuning on limited SAP-specific data, the generative SAPGEN model transfers general sequence knowledge to the SAPs domain, enabling the conditional generation of SAPs in low-data regimes. Subsequently, we constructed a lightweight predictive model, SAPBRF, to further screen the generated peptides. Comparative analyses demonstrated that SAPGEN enabled reliable and precise control of the physicochemical properties of the generated sequence, outperformed baseline generative models in terms of efficiency and quality across multiple design tasks. The SAPBRF predictor achieved efficient and accurate screening with high predictive performance. Finally, the complete DeepSAP framework demonstrated strong generalisation under unseen physicochemical regimes, where peptides generated and screened by the pipeline consistently exhibited stable self-assembly behavior, as further verified by molecular dynamics simulations. Our findings indicate that DeepSAP enables the conditional generation of SAPs with desired properties, holding the potential for tailored design of functional biomaterials. Availability and Implementation Source code for the DeepSAP model training and evaluation is available at: https://github.com/Paul-hihihopes/Deep_SAP_Design and at DOI: 10.5281/zenodo.21522690. Supplementary Information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7220845987 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuanming Song, Yuhan Wu, Yuqiu Jiang, Li He, Shoubo Zhao, 邓小元 Xiaoyuan Deng
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag744
URL
https://doi.org/10.1093/bioinformatics/btag744
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.