Predicting Nanobody Paratope via Fused Attention Mechanism and Distance-Guided Interaction Learning

The task of nanobody paratope prediction aims to identify the residues on a nanobody that specifically bind to an antigen, which is crucial for understanding the mechanisms of an effective nanobody agent, providing significant implications for drug development accordingly. Although various methods have been proposed based on different strategies, existing methods still face several challenges: overlooking nanobody-specific structural characteristics, neglecting optimization of high-dimensional features extracted from antibody language models, and disregarding the antigen information crucial for nanobody-specific binding. To address these challenges, we propose NanoFADIL, a novel nanobody paratope prediction model that employs a fused attention mechanism combining channel attention and one-dimensional spatial attention to optimize feature representations adaptively, and uses residue-level distance supervision to guide the learning of nanobody-antigen interaction patterns. Specifically, the existing nanobody paratope dataset is augmented by incorporating the sequences of corresponding antigens, allowing the prediction of antigen-specific paratopes on nanobodies. Nanobodies are encoded by the pretrained antibody language model IgT5 to derive meaningful feature representations. High-dimensional nanobody representations are adaptively reweighted across feature channels and residue positions by our fused attention mechanism that combines channel-wise and one-dimensional spatial attention, thereby enabling the model to focus on informative features relevant to nanobody paratope prediction and contextually important residues. During training, residue-level distances between nanobodies and antigens are transformed into supervision signals, guiding the model to learn meaning-ful nanobody-antigen interaction patterns and enhancing the model's predictive capability. Experimental results demonstrate that NanoFADIL achieves superior performance compared to existing methods.