Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.
Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants.
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DOI:
10.1093/gigascience/giad073
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发表时间:
2022-12-28
期刊:
影响因子:
9.2
通讯作者:
中科院分区:
文献类型:
--
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Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.
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影响因子:
9.8
作者:
Amorosi, Clara J.;Chiasson, Melissa A.;Dunham, Maitreya J.
通讯作者:
Dunham, Maitreya J.
影响因子:
10.7
作者:
Cagiada, Matteo;Johansson, Kristoffer E.;Lindorff-Larsen, Kresten
通讯作者:
Lindorff-Larsen, Kresten
DOI:
10.1126/science.abc0870
发表时间:
2020-09-04
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Chan KK;Dorosky D;Sharma P;Abbasi SA;Dye JM;Kranz DM;Herbert AS;Procko E
通讯作者:
Procko E
影响因子:
7.7
作者:
Diss G;Lehner B
通讯作者:
Lehner B
影响因子:
48
作者:
Alley, Ethan C.;Khimulya, Grigory;Church, George M.
通讯作者:
Church, George M.