Protein design via deep learning

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Automated summary

This review explores the recent advances in deep learning-based protein design methods and compares them with conventional knowledge-based approaches through notable cases. The development of deep learning in structure-based and sequence-based protein design is described, as well as the applications of deep reinforcement learning in protein design. Future perspectives on design goals, challenges, and opportunities are thoroughly discussed.