Structure-based protein design with deep learning. (December 2021)

• Record Type: Journal Article
• Authors: Ovchinnikov, Sergey; Huang, Po-Ssu
• Is Part Of: Current opinion in chemical biology. Volume 65(2021)
• Page Start: 136
• Page End: 144
• Publication Date: 2021-12
• Subjects: Deep learning -- Protein design -- Neural networks -- Protein structure -- Protein structure design -- Protein sequence design; Bioorganic chemistry -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Clinical biochemistry -- Periodicals; Biochemistry -- Periodicals; Chimie bio-organique -- Périodiques; Biologie -- Périodiques; 572.05
• DOI: 10.1016/j.cbpa.2021.08.004
• Languages: English
• ISSNs: 1367-5931

Abstract

Abstract: Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.