Variant effect predictions capture some aspects of deep mutational scanning experiments
Variant effect predictions capture some aspects of deep mutational scanning experiments
复制标题
变异效应预测捕捉了深度突变扫描实验的某些方面
DOI：
10.1186/s12859-020-3439-4
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发表时间：
2019
影响因子：
3
通讯作者：
B. Rost
中科院分区：
文献类型：
--
作者：
Jonas Reeb;T. Wirth;B. Rost
深度突变扫描（DMS）研究通过系统地和全面地测定单个氨基酸变体（SAV;也称为错义突变或非同义单核苷酸变体-错义SNV或nsSNV）对特定蛋白质的影响来利用序列变异的突变景观。我们从22个不同的DMS实验中组装了SAV注释，并将效应分数归一化以评估变体效应预测方法。三个训练传统的变体效应数据（PolyPhen-2，SIFT，SNAP 2），一个回归方法优化DMS数据（Envision），和一个天真的预测使用同源物的保守信息。在一组32，981个SAV上，所有方法都捕获了实验效果评分的某些方面，尽管不同。传统的方法，如SNAP 2，与测量值的相关性略高，并且可以更好地分类二进制状态（有效或中性）。Envision似乎能更好地估计效果的精确程度。最令人惊讶的是，在许多情况下，使用PSI-BLAST的简单朴素的保守方法优于其他方法。所有方法捕获的有益效果（功能获得）显著劣于有害效果（功能丧失）。对于少数具有多个独立实验测量值的蛋白质，实验差异很大，但彼此之间的一致性比预测更高。DMS为理解蛋白质序列空间的动力学提供了一种新的强有力的实验手段。一如既往，充满希望的新开端必须克服挑战。虽然我们的研究结果表明，DMS将是至关重要的，以改善变异效应预测方法，数据的多样性阻碍了简化和推广。
Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants – missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
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影响因子：
8.8
作者：
Brenan L;Andreev A;Cohen O;Pantel S;Kamburov A;Cacchiarelli D;Persky NS;Zhu C;Bagul M;Goetz EM;Burgin AB;Garraway LA;Getz G;Mikkelsen TS;Piccioni F;Root DE;Johannessen CM
通讯作者：
Johannessen CM
影响因子：
5.6
作者：
Procko E;Hedman R;Hamilton K;Seetharaman J;Fleishman SJ;Su M;Aramini J;Kornhaber G;Hunt JF;Tong L;Montelione GT;Baker D
通讯作者：
Baker D
影响因子：
5.6
作者：
Roscoe, Benjamin P.;Thayer, Kelly M.;Zeldovich, Konstantin B.;Fushman, David;Bolon, Daniel N. A.
通讯作者：
Bolon, Daniel N. A.
影响因子：
9.8
作者：
Ioannidis, Nilah M.;Rothstein, Joseph H.;Sieh, Weiva
通讯作者：
Sieh, Weiva
用于蛋白质工程和设计的深度测序方法。
影响因子：
6.8
作者：
Wrenbeck,EmilyE;Faber,MatthewS;Whitehead,TimothyA
通讯作者：
Whitehead,TimothyA