ArticleAntibodies (Basel, Switzerland)2025
Computational Prediction of Single-Domain Immunoglobulin Aggregation Propensities Facilitates Discovery and Humanization of Recombinant Nanobodies.
Article in Antibodies (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
objectivesSingle-domain immunoglobulins are small protein modules with specific affinities. Among them, the variable domains of heavy chains of heavy-chain-only antibodies (VHH) as the antigen-binding fragment of heavy-chain-only antibodies (also termed nanobodies) have been widely investigated for their applicability, e.g., therapeutics and immunodiagnostics. However, despite their advantageous biochemical and biophysical characteristics, protein aggregation throughout recombinant synthesis is a serious drawback in the development of nanobodies with application perspectives. Therefore, we aimed to develop a computational method to predict the aggregation propensity of VHH antibodies for the selection of promising candidates in early discovery.
methodsWe employed a deep learning-based structure prediction for VHHs and derived from it likely biophysical and biochemical properties of the framework region 2 with relevance for aggregation. A total of 106 nanobody variants were produced by recombinant expression and characterized for their aggregation behavior using size exclusion chromatography (SEC).
resultsQuantitative characteristics of framework region 2 patches were combined into a function that defines an aggregation score (AS) predicting the aggregation propensities of VHH variants. AS was evaluated for its capability to forecast recombinant VHH aggregation by experimentally studying VHH Fc-fusion proteins for their aggregation. We observed a clear correlation between the calculated aggregation score and the actual aggregation propensities of biochemically characterized VHHs Fc-fusion proteins. Moreover, we implemented an easily accessible pipeline of software modules to design nanobodies with desired solubility properties.
conclusionsAI-based prediction of VHH structures, followed by analysis of framework region 2 properties, can be used to predict the aggregation propensities of VHHs, providing a convenient and efficient tool for selecting stable recombinant nanobodies.
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