Predicting interacting hotspots for nanobodies' binding using triplets of residues

Abstract

Protein-protein interactions (PPI) are fundamental to cellular signaling, forming robust networks that govern critical biological processes such as immune response, cell growth, and signal transduction. Nanobody-based therapies have emerged as a key strategy for modulating PPIs, offering exceptional potential due to their high specificity, stability, and ability to access challenging epitopes on PPI interfaces inside cells. The rational design of nanobodies relies mainly on understanding and predicting their binding regions, particularly the residues that contribute the most to the binding energy (binding hotspots). Existing computational methods do not fully provide a scalable solution for hotspot identification in nanobody design, leaving a critical gap in the rational design of these therapeutics. Here, we present a scalable and structure-aware algorithm for hotspot prediction in nanobody design. The algorithm queries a curated database of triplets of interacting residues obtained from ~20,000 non-redundant PDB structures. We showed that these triplets contain structural and energetic information, being able to assess the stability effect of residue variations in protein structures, Pearson R = 0.63 (MSE = 1.58 kcal/mol). More important than effects on stability is the ability of the algorithm to predict binding hotspots of protein-protein generic complexes and more specifically in complexes containing nanobodies. HotspotPred reached an accuracy of 0.73 for hotspot residue identification in a protein interaction dataset of 1160 Alanine mutants and correctly identified in 63.4% of the cases we predicted at least 2 residues on the binding surface.