Deep Learning in Protein Structural Modeling and Design

By Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray. 2020

191 citations recorded by the source corpus.

Summary Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the “sequence → structure → function” paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

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

1. Marinka Zitnik
2. Tianfan Fu
3. Jimeng Sun
4. Jure Leskovec
5. Kexin Huang
6. Cao Xiao
7. Bruno E. Correia
8. Wenhao Gao

In Materials Chemistry

1. Tianfan Fu
2. Jimeng Sun
3. Bruno E. Correia
4. Freyr Sverrisson
5. Marinka Zitnik
6. Cao Xiao
7. Wenhao Gao
8. Jure Leskovec

In Molecular Biology

1. Marinka Zitnik
2. Kexin Huang
3. Jure Leskovec
4. Cao Xiao
5. Jeffrey A. Ruffolo
6. Michael M. Bronstein
7. Jimeng Sun
8. Sai Pooja Mahajan

Citations in the corpus

• Fast end-to-end learning on protein surfaces (2020, methodology): “Deep learning methods have increasingly been applied to a broad range of problems in protein science [21], with the particularly notorious success of DeepMind’s AlphaFold to predict 3D protein structure from sequence [37].”
• Improved antibody structure prediction by deep learning of side chain conformations (2021, methodology): “Machine learning methods improve overall structure prediction and docking [9].”
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “Machine learning has already proved useful in the search of antibiotics [136], polypharmacy [176], drug repurposing for emerging diseases [47], protein folding and design [64, 41], and biomolecular interactions [177, 3, 55, 39].”