Optimization of a sarbecovirus llama nanobody–antigen binding interface via a combined computational and phage display protein engineering approach - Research Article

Sep 29, 2026

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Single-domain antibodies from camelids (VHHs) offer therapeutic advantages but can have limited binding breadth and escape vulnerability due to smaller paratopes. A combined computational and experimental protein engineering strategy is used to broaden VHH-72 reactivity against SARS-CoV-1 RBD, SARS-CoV-2, and Delta by enlarging the binding site with second-site interactions. Protein-ligand interface design (ProtLID) guides a restricted-diversity phage library, yielding a VHH-72 triple mutant with enhanced affinity for heterologous RBDs.

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Optimization of a sarbecovirus llama nanobody–antigen binding interface via a combined computational and phage display protein engineering approach

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RESEARCH ARTICLE BIOPHYSICS AND COMPUTATIONAL BIOLOGY Optimization of a sarbecovirus llama nanobody–antigen binding interface via a combined computational and phage display protein engineering approach Kevin E. Ramosa,1, Prithviraj Nandigramib,1, Ahmad Najafianb , Jonathan R. Laia,2, and Andras Fiserb,2 Affiliations are included on p. 10. Edited by Andrej Sali, University of California San Francisco, San Francisco, CA; received December 19, 2024; accepted June 3, 2025 Single-domain antibodies, such as variable heavy domain of heavy chain (VHH) domains from camelids, are an attractive platform for therapeutic purposes. VHHs have a smaller size than traditional antibodies, which harbors certain advantages such as tissue penetration or accessing epitopes that may be shielded by protein domains. However, the smaller size of VHHs typically involves a smaller binding site, which can limit the scope of related antigens they are able to bind and, in the specific case of antiviral VHHs, render them susceptible to escape by a single point mutation. Here, we present a combined computational and experimental protein engineering approach to broaden the reactivity of SARS-CoV-1 receptorbinding domain (RBD)-specific VHH-72 for SARS-CoV-2 and Delta. Our strategy focuses on increasing the size of the binding site by imparting“second site” interactions toward heterologous antigens. We utilized the residue-based pharmacophore modeling approach, Protein-ligandinterface design (ProtLID), to identify potential productive side chain interactions in this second site and then encoded the ProtLID-predicted restricted diversity into a VHH-72-based phage library, which we then screened for cross-reactive SARS-CoV-1/2 RBD clones. Based on sequence analysis from the resulting population of functional VHH clones, we identified aVHH-72 triple mutant (T60P.D61P.D100eY), which maintained high affinity for SARS-CoV-1 RBD but was enhanced by 18-and 20-fold for RBDs from SARS-CoV-2 and Delta relative toWTVHH-72. These results highlight the potential of computationally customizing phage display diversity for single-domain binding proteins and provide a strategy for designing extension of protein binding interfaces. protein engineering | antibody engineering | residue-based pharmacophore | phage display Structure-based protein engineering is a powerful approach for improving or optimizing antibody binding affinity and, in some cases, breadth. Antibody (Ab)-combining sites can be complementarily explored by computational and experimental techniques. Experimentally, phage, yeast, and ribosome display of antibody libraries allow rapid screening of 108 to 1014 combinations of mutants simultaneously. However, even the larger end of these library sizes can sample only a small amount of the diverse sequence space required to fully randomize 15.6 ± 4.7 positions (the average size of Ab paratopes) (1), which encompasses ~2 × 1020 theoretical amino acid combinations. Nevertheless, phage display in particular has been successfully employed to develop protective antibodies against hemagglutinin of influenza virus (2, 3) or against HIV-1 (4) . Utilization of computational tools can augment experimental selection from combinatorial libraries (5–7) . One beneficial strategy is to computationally restrict sequence diversity to residues that are likely to participate in productive interactions, thereby reducing the sequence space to be explored. Computational algorithms have the capacity to evaluate a vast number of variants in silico, surpassing what can be practically achieved through experimentation. For example, computational protein design techniques have been combined with low-resolution docking and yeast display experiments to identify low nanomolar (KD ~22 to 38 nM) affinity binders to H1N1 influenza hemagglutinin (8, 9) . Such approaches can further be refined using deep sequencing technology (10) or employing deep learning–based protein design approaches (11

FAQs

Why is VHH-72 susceptible to viral escape and limited antigen breadth?

VHHs are smaller than conventional antibodies, which typically reduces binding-site size and constrains the antigen scope. This smaller paratope can also make antiviral VHHs vulnerable to escape via single point mutations.

What computational method was used to design the improved binding interface?

The Protein-ligand interface design (ProtLID) algorithm was used with a residue-based pharmacophore modeling approach to identify productive second-site residue interactions.

How did the engineered nanobody perform against different coronavirus RBDs?

A VHH-72 triple mutant maintained high affinity for SARS-CoV-1 RBD while showing 18- and 20-fold enhanced affinity for SARS-CoV-2 and Delta RBDs compared with the wild-type VHH-72.

Sep 29, 2026

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