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Daily Signal May 29, 2026 · 10 min read

Issue #119: Integrating glycosylation in <i>de novo</i> protein design with ReGlyco Binder Design Filter

Protein Design Digest #119: Integrating glycosylation in <i>de novo</i> protein design with ReGlyc…

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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).

Why this matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

Also Worth Reading

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design

AlphaFold has transformed structural biology and spawned an ecosystem of derivative tools for protein design, binding prediction, and drug discovery. However, whether AlphaFold has learned generalizable biophysical principles versus template-based pattern matching remains unclear—a distinction critical for applications beyond its training context. Here, we perform a systematic adversarial evaluation of AlphaFold 3 using point and deletion mutations across 200 proteins. Remarkably, predicted structures remain invariant to mutations of up to 40% of residues—including deliberately destabilizing substitutions—and to deletions of 10%. Notably, this invariance holds even for experimentally validated fold-switching proteins that are known to adopt alternative conformations in response to such mutations, despite the fact that these proteins are small and monomeric—precisely the category where AlphaFold is expected to perform best. Confidence metrics prove unreliable, as they select the most accurate structure at most 35% of the time and correlate with the structural quality of the best available training set template. This suggests that AlphaFold’s uncertainty estimates reflect template availability more than biophysical reasoning. ESMFold exhibits greater, though still imperfect, mutational sensitivity, suggesting superior sequence-structure coupling. These findings indicate that AlphaFold may rely heavily on memorized templates rather than biophysical reasoning, with profound implications for the reliability of AlphaFold-based protein design, drug discovery, and modeling workflows.

Exploring the mechanism of saffron in treating viral myocarditis using network pharmacology and molecular docking.

Viral myocarditis (VM) is a cardiovascular disorder that can lead to heart failure and cardiogenic shock. Saffron, a traditional Chinese medicinal herb, has shown therapeutic potential against VM in numerous studies. However, the mechanisms through which saffron exerts its effects on VM remain poorly understood. Thus, this study aimed to elucidate the active compounds, molecular targets, and signaling pathways involved in saffron’s therapeutic action against VM by employing network pharmacology and molecular docking approaches. The active compounds and corresponding targets of saffron were retrieved from the Traditional Chinese Medicine Systems Pharmacology database. VM-associated targets were sourced from the GeneCards database. Overlapping targets between saffron and VM were then identified. Protein-protein interaction networks were established and analyzed utilizing the STRING platform and Cytoscape software to determine core targets. Furthermore, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were carried out utilizing Bioconductor in R to explore the potential biological activities and signaling pathways through which saffron may act against VM. Finally, molecular docking and model visualization were carried out using AutoDock Tools and PyMOL open-source software. From the database, we identified 4 active compounds in saffron with potential effects against VM: crocetin, isorhamnetin, kaempferol, and quercetin. A total of 60 corresponding targets were observed, with TNF, IL-6, IL-1β, CXCL8, and JUN emerging as core targets. Kyoto encyclopedia of genes and genomes enrichment analysis revealed 155 regulatory signaling pathways, among which the TNF, AGE-RAGE, and IL-17 signaling pathways, lipid metabolism, and atherosclerosis were the most prominent. Molecular docking results indicated that quercetin showed the strongest binding affinity toward IL-1β and CXCL8. The therapeutic effect of saffron against VM is not driven by a single factor, but rather involves multiple active compounds, targets, and signaling pathways.

Unraveling the anti-neuroinflammatory mechanisms of Cervus cucumis polypeptide injection in Alzheimer’s disease: insights from network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation.

Objective Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with increasing global prevalence, in which neuroinflammation serves as a critical pathological driver exacerbating cognitive decline. While current therapies offer limited symptomatic relief, multi-target strategies are urgently needed. Cervus cucumis polypeptide injection (CCPI), a traditional Chinese medicine (TCM) formulation, has demonstrated anti-inflammatory properties; however, its mechanisms of action against AD remain unclear. This study aimed to elucidate the anti-AD potential mechanisms of CCPI using an integrated approach combining network pharmacology, molecular docking, molecular dynamics (MD) simulation, and experimental validation. Methods Active components and corresponding targets of CCPI were retrieved from the TCMSP database, while AD-related targets were collected from Genecards, OMIM, and DrugBank. Potential therapeutic targets were identified by intersecting drug and disease targets, followed by protein-protein interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and MD simulations were performed to evaluate interactions between potential active components and key targets. In vitro experiments were conducted on Aβ 25-35 -induced BV2 microglial cells to assess cell viability (CCK-8 assay), inflammatory cytokine levels (ELISA), and protein expression (Western blot) related to the neuroinflammation pathway and microglial polarization. Results A total of 28 active components and 50 common targets of CCPI for AD treatment were identified. Linoleic acid (LA) was determined to be a potential active component, with IL-6 as the key target based on PPI network topology. Molecular docking and MD simulation confirmed a stable binding affinity between LA and IL-6. KEGG analysis revealed significant enrichment in the HIF-1 signaling pathway, particularly the IL-6/STAT3/VEGF signaling pathway. In vitro , CCPI treatment significantly enhanced cell viability and attenuated the pro-inflammatory response, as evidenced by reduced levels of IL-6, IL-1β, and TNF-α, decreased the expression of the pro-inflammatory marker iNOS. Concurrently, it elevated the expression of the anti-inflammatory/repair-associated marker CD206. Western blot analysis further verified that CCPI suppressed IL-6/STAT3 activation while upregulating VEGF expression. Additionally, LA alone significantly reduced IL-6 levels and STAT3 phosphorylation, decreased the expression of iNOS, and increased the expression of CD206, with therapeutic efficacy comparable to CCPI. Conclusion CCPI exerts neuroprotective effects in AD models by regulating the IL-6/STAT3/VEGF pathway, downregulating the expression of the inflammation-related iNOS protein, upregulating the expression of the CD206 protein associated with anti-inflammatory and reparative functions, remodeling the functional state of microglia, inhibiting their pro-inflammatory responses, and enhancing their reparative functions. Its potential active component, LA, likely mediates this effect by stably binding to and inhibiting IL-6, thus suppressing the downstream STAT3 phosphorylation that drives inflammatory activation.

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