From sequence to function through structure: Deep learning for protein design

The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.(c) 2022 The Authors. Published by Elsevier B.V.

Automated summary

The process of designing biomolecules, particularly proteins, is undergoing rapid changes fueled by advancements in artificial intelligence and computational methods. By leveraging natural language processing and computer vision techniques, researchers are able to learn patterns from biological databases and use them to gain insights into mechanistic biology and design biomolecules. However, understanding and applying the latest protein design tools can be complex. To address this, recent advances in deep learning-assisted protein design are documented, and a practical pipeline is presented. Challenges and opportunities in the protein design field are also discussed.