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

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.