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

Artificial Intelligence (AI)-based methods for 3D protein structure prediction are revolutionising structural biology ^1–7 , providing novel templates for experimental data refinement and an on demand 3D perspective on any molecular architecture and protein-protein interaction (PPI). Regardless of the inherent limitations of the various approaches available to date, the continuous improvement of the algorithms, the broad availability of open access (OA) web servers ^3,8 , software packages ^6,9 and databases ^10,11 are bound to accelerate the discovery and optimization of novel biopharmaceuticals ^12,13 . Within this context, the development of computational pipelines for the de novo design of target-specific protein binders ^12,14,15 is especially exciting. As it stands, these processes are still rather inefficient ¹⁶ 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 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 ) ¹⁷ to filter the results of a recent open competition launched by Adaptyv Bio 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, refined with the new ReGlyco Rotamer tool, flagged 11% of non-binders prior to experiment in approximately 3 hours on a dual-core CPU. We complement this analysis with a demo 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 ¹⁸ from the Institute for Protein Design (IDP).