The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data
Open accessAbstract
SUMMARY Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant’s molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This data set was never used for hyperparameter tuning or model training, and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision’s performance improves as more large-scale mutagenesis data is incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm and yeast proteomes (https://envision.gs.washington.edu/).
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“Experiments can reveal a variant’s molecular effect, and recent advances in multiplex assays have enabled the assessment of large numbers of variants (Fowler and Fields, 2014; Gasperini et al., 2016).”
Self-citationMutation effects predicted from sequence co-variation
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Cited by
CADD: predicting the deleteriousness of variants throughout the human genome
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., & Kircher, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1016 · Cited by 3,534
Self-citationRives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280
Metadata
- Authors
- Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., & Fowler, D. M.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 6
- Issue
- 1
- Pages
- 116-124.e3
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W2774216375
- Cited by
- 209
- References
- 10
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Gray, V. E., Hause, R. J., Luebeck, J., Shendure, J., & Fowler, D. M. (2018). Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data. Cell Systems. https://doi.org/10.1016/j.cels.2017.11.003
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