Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patter.2020.100142 | PATTERNS |
Keywords | DocType | Volume |
deep generative model,deep learning,protein design,protein folding,representation learning | Journal | 1 |
Issue | ISSN | Citations |
9 | 2666-3899 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gao Wenhao | 1 | 2 | 1.75 |
| Sai Pooja Mahajan | 2 | 1 | 1.04 |
| Jeremias Sulam | 3 | 1 | 1.72 |
| Jeffrey J Gray | 4 | 99 | 9.82 |