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bioRxiv · 10.64898/2026.04.16.718906

Integrating glycosylation in de novo protein design with ReGlyco Binder Design Filter

Singh, O.·Fadda, E.
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Abstract

Artificial Intelligence (AI)-based methods for 3D protein structure prediction are revolutionising structural biology, rapidly giving users a 3D perspective on any molecular architecture and protein-protein interaction (PPI) virtually on-demand. Regardless of the inherent limitations of the various methods to date, the continuous improvement of the algorithms, and the broad availability of open access (OA) web servers, software packages and databases are bound to accelerate biopharmaceuticals discovery. Within this context, the development of computational pipelines for the de novo design of target-specific protein binders is especially exciting. As it stands, these processes are still rather inefficient and expensive, rapidly outputting thousands of designs relatively quickly, which translate into meagre yields. Here we show how the explicit integration of glycosylation as a filter in the 3D de novo design pipeline of biologics can significantly improve efficiency and reduce laboratory costs with minimal additional computational resources. As a proof-of-concept, we used the GlycoShape database and ReGlyco tools (https://glycoshape.org) to filter the results of an open competition launched by Adaptyv Bio in October 2025 for the design of binders as inhibitors against the heavily glycosylated Nipah virus glycoprotein (NiV-G) (https://proteinbase.com/competitions/adaptyv-nipah-competition). Screening of the 1,201 selected designs in block with ReGlyco allows users to eliminate 20% of the confirmed non-binders from the pool in approximately 3 hours on a dual-core CPU. Refinement with a rotamer search enabled flags 11% of non-binders prior to experiment and catches all but 5 confirmed binders from the given predicted 3D complexes. The availability of alternative binding poses improves this score. We complement this analysis with a demo OA colab notebook (https://colab.research.google.com/github/Ojas-Singh/GlycoShape-Resources/blob/main/colab/ReGlyco_Binder_Design_filter.ipynb) to illustrate our workflow. In this demo users can design mini binders against human erythropoietin (hEPO) by integrating GlycoShape resources with the RFdiffusion3 (RFD3) pipeline from the Institute for Protein Design (IDP).

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Singh, O., Fadda, E.. 2026-04-17. Integrating glycosylation in de novo protein design with ReGlyco Binder Design Filter. https://doi.org/10.64898/2026.04.16.718906

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