The Frontier Exploration of Algorithm Innovation and Experimental Verification in Intelligent Protein Design

Intelligent protein design is a frontier topic in the cross field of modern biotechnology and AI. Through the combination of algorithm innovation and experimental verification, it breaks through the limitations of traditional protein design. In this paper, the progress of algorithm innovation in intelligent protein design is summarized, especially the application of advanced algorithms such as deep learning, generative model and reinforcement learning in protein structure prediction, function optimization and interaction analysis. Taking DeepThermoNet, a deep learning algorithm, as an example, the effect of protein mutant designed by DeepThermonet in improving the thermal stability of β -glucosidase was verified by experiments. The results showed that the mutant designed by the algorithm group was significantly better than the mutant designed by the traditional method in melting temperature (Tm) and enzyme activity retention rate. The experimental verification not only proves the effectiveness of the algorithm design, but also optimizes the algorithm model through feedback, forming a closed loop of "algorithm design-experimental verification-model optimization". This paper further discusses the interactive relationship between algorithm innovation and experimental verification, looks forward to the future development direction of intelligent protein design, including interdisciplinary integration, new algorithm development and data resource expansion, and points out the limitations of current research and the key direction of future work. Intelligent protein design is expected to provide new theoretical and technical support for drug research and development, biocatalyst development and biomaterial design, and promote innovation and development in related fields.