AlphaFold2 and the future of structural biology
By Patrick Cramer. 2021
312 citations recorded by the source corpus.
Subfields: 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 Materials Chemistry
- Damiano Piovesan
- Björn Wallner
- Silvio C. E. Tosatto
- Yoshua Bengio
- Yi Zhao
- Zhenyu Yang
- Stefano Ermon
- Runsheng Chen
In Molecular Biology
- Stefano Ermon
- Damiano Piovesan
- Silvio C. E. Tosatto
- Björn Wallner
- Christopher R'e
- Yoshua Bengio
- Yi Zhao
- Zhenyu Yang
Citations in the corpus
- AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, methodology): “AF2 has rapidly become the go-to method for protein structure prediction for both monomers and multi-mer prediction pipelines (Cramer 2021).”
- AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “66,67 Secondly, these predicted structures may also be helpful for structure determination of large protein assemblies by cryo-EM, which usually needs structures of the component proteins or their domains as a starting point for fi tting to the cryo-EM densities.”
- AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors (2022, background): “Among these proteins, GPCR is one of the most important classes in structural biology and drug development [17, 50].”
- Intrinsic protein disorder and conditional folding in AlphaFoldDB (2022, background): “This is leading to a paradigm shift for structural biology due to the sudden availability of orders of magnitude more protein structures.(4,5) AlphaFoldDB expands the impact further by allowing interested researchers to browse predictions for proteins in several major model organisms.”
- Improved multimer prediction using massive sampling with AlphaFold in CASP15 (2023, methodology): “AlphaFold has rapidly emerged as the preferred method for 29 protein structure prediction (Cramer, 2021).”
- Hyena Hierarchy: Towards Larger Convolutional Language Models (2023, background): “Large Transformers have enabled a number of breakthrough advances in modeling language, vision, audio, biology and numerous other domains (Vaswani et al., 2017), (Dosovitskiy et al., 2020), (Radford et al., 2022), (Cramer, 2021).”