Structure-based Protein Design with Deep Learning
By Sergey Ovchinnikov, Po-Ssu Huang. 2021
95 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- J. Dauparas
- David Baker
- Ivan V. Anishchenko
- Sergey Ovchinnikov
- Minkyung Baek
- Frank DiMaio
- Kevin Kaichuang Yang
- Niccoló Zanichelli
In Materials Chemistry
- Sergey Ovchinnikov
- David Baker
- Frank DiMaio
- Ivan V. Anishchenko
- J. Dauparas
- Minkyung Baek
- Kevin Kaichuang Yang
- Niccoló Zanichelli
In Molecular Biology
- Sergey Ovchinnikov
- David Baker
- Frank DiMaio
- Ivan V. Anishchenko
- J. Dauparas
- Kevin Kaichuang Yang
- Minkyung Baek
- Bruno E. Correia
Citations in the corpus
- Scaffolding protein functional sites using deep learning (2022, background): “Despite a recent surge of interest in using machine learning to design protein sequences (42–49), the design of protein structure is relatively underexplored, likely due to the difficulty of efficiently representing and learning structure (50).”
- Deep learning methods for designing proteins scaffolding functional sites (2021, background): “Despite a recent surge of interest in using machine learning to design protein sequences (36–43), the design of protein structure is relatively underexplored, likely due to the difficulty of efficiently representing and learning structure (44).”
- Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “See Ovchinnikov & Huang (2021) for a more comprehensive review of fixed-backbone protein design approaches.”
- A Survey on Generative Diffusion Models (2022, background): “3) Diffusion Models on Non-Euclidean Space: Discrete Space Deep generative models have made considerable strides in various domains, such as natural language processing [119], [120], multi-modal learning [49], [121], and AI for science [122], [123].”