Thermostability Prediction Powered by Synergistic Deep Learning at Experimental and Theoretical Levels for Nanobodies.

Nanobodies have emerged as powerful biorecognition elements in various fields, while thermostability is a key factor in practical application. Experimental screening remains costly and low throughput, while the scarcity of thermostability data poses a significant challenge for machine learning application. In this work, we innovatively propose a dual-scale synergistic deep learning strategy to improve prediction reliability, including a NBsTem_Tm model trained on 514 experimental melting temperature (Tm) data and a NBsTem_Q model with a theoretical indicator inferred from conformation changes of extensive MD simulations on 704 nanobody structures. Their synergy can alleviate the scarcity of experimental data and the risk of low generalization inherent in small-data NBsTem_Tm models to unseen samples. The two models are constructed by integrating the antibody language model into a joint deep learning architecture to sufficiently learn the feature embedding at different levels. Consequently, NBsTem_Tm achieves a Pearson value of 0.83 on the external test, significantly outperforming three reported competitive models. NBsTem_Q obtains accuracy of 0.84, also exhibiting applicable potential. In addition, the two models can be applied for nanobodies with missing residues, thus being robust to wide application. Benefiting from the two synergistic models, a more reliable screening criterion (Tm > 65 °C and Qclass IV) is proposed for determining highly thermostable nanobodies. The dual-scale framework coupled with a proposed screening strategy is further applied to explore the INDI database with tens of millions of unexplored nanobodies to fill the absence of its thermostability property, discovering approximately 12% thermostable nanobodies. Finally, a user-friendly web server NBsTem is developed to serve as a high-throughput screening platform for nanobody design and development.