Predictive and therapeutic applications of protein language models

抄録

Protein language models (pLMs) are rapidly emerging as revolutionary artificial intelligence technologies that bring transformative changes to drug discovery and therapeutic research. pLMs acquire rich representational capabilities from large-scale sequence datasets, enabling the solution of various biological problems that were difficult with conventional methods. In this review, we provide a comprehensive overview of various pLMs and their implementations, exploring their potential utility in drug discovery and therapeutic research. First, we systematically classify pLMs based on their architectures and information sources while discussing their development to the present. We also explain recent trends in multimodal approaches that integrate co-evolutionary information, structural information, and functional information, as well as domain-specific models specialized for particular domains such as antibodies and T-cell receptors. We then provide a comprehensive overview of various therapeutic applications of pLMs, including mutation effect prediction, function prediction, and structure prediction. Finally, we discuss future prospects of pLMs toward therapeutic applications and challenges for transforming them into technologies that contribute to actual diseases.