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:
生物学2区
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--
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评估氨基酸变异的影响一直是研究蛋白质功能和解释基因组数据的关键挑战。像深度突变扫描(DMS)这样的高通量实验方法可以测量靶蛋白中大量变体的影响,但由于DMS研究尚未对所有蛋白质进行,研究人员还通过计算对DMS数据进行建模,以估计预测因子的变体影响。在这项研究中,我们扩展了基于线性回归的预测,探讨是否纳入数据丙氨酸扫描(AS),广泛使用的低通量诱变方法,将改善预测结果。为了评估我们的模型,我们收集了146个AS数据集,映射到22种不同蛋白质的54个DMS数据集。我们发现,改进的模型性能取决于DMS和AS检测的兼容性,并且改进的规模与DMS和AS结果之间的相关性密切相关。
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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