Data supplementary for NBsTem webserver (http://www.nbscal.online/).

Nanobodies (VHHs) as effective biorecognition elements, have been widely applied in medicine, environment, agriculture and food fields. The thermostability is a crucial factor for its preservation, transportation and application. To address technical challenge from the scarcity of experimental thermostability data for nanobodies, this study innovatively develops two complementary models inferred from an experimental melting temperature Tm and a novel theoretical Qclass associated with conformation stability to realize reliable prediction of the nanobody thermostability by combining extensive molecular dynamics simulation, antibody language model and fused deep learning framework. The Tm prediction model NBsTem_Tm achieves an external test Pearson of 0.83 and MAE of 2.30 °C, significantly outperforming three competitive models reported. The four-classification model NBsTem_Q achieves accuracy of 0.84 for the external test set. With them, thermostability of the INDI database with tens of millions of unexplored nanobodies are first time evaluated, indicating that about 12% nanobodies have high thermostability (Tm higher than 65 °C and simultaneously being Qclass IV). A universal application strategy based on NBsTem model is further proposed to screen nanobodies as desired biorecognition elements. Finally, a user-friendly webserver NBsTem is developed, which can be served as an effective analysis tool for nanobody design and application.