Protein language model enables fast and sensitive remote homolog detection
Author:
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41587-024-02359-0.pdf
Reference5 articles.
1. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This paper proposed AlphaFold2, a deep learning model to predict protein structure accurately.
2. Buchfink, B. et al. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015). This paper introduced an alignment algorithm that can achieve high sensitivity while being much faster than the gold standards.
3. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). This paper proposed a protein language model trained on a large scale and a structure prediction model that uses only a single sequence.
4. Alexander, L. et al. Protein target highlights in CASP15: analysis of models by structure providers. Proteins 91, 1571–1599 (2023). This paper presented CASP, a challenge aiming at establishing the current state of the art in protein structure prediction.
5. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022). This paper proposed an accelerated method and an accessible platform for protein structure prediction.
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