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Unsupervised inference of protein fitness landscape from deep mutational scan.
Molecular Biology and Evolution ( IF 10.7 ) Pub Date : 2020-08-08 , DOI: 10.1093/molbev/msaa204
Jorge Fernandez-de-Cossio-Diaz 1, 2 , Guido Uguzzoni 3 , Andrea Pagnani 3, 4, 5
Affiliation  

Abstract
The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype–fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.


中文翻译:

从深度突变扫描中无监督地推断蛋白质适应性状况。

摘要
在高通量突变扫描中筛选大型组合文库的最新技术进步加深了我们对适应性蛋白质进化的理解,并增强了其在蛋白质设计中的应用。然而,大量可能的基因型需要适当的计算方法进行数据分析,突变效应的预测以及优化序列的产生。我们描述了一种计算方法,该方法在筛选实验的多个回合中对样品进行测序后,提供了基因型与适应性关系的模型。我们在五次大规模突变扫描中测试了该方法,从而准确预测了突变对适应性的影响。推断的适应度景观对实验和采样噪声具有鲁棒性,并且在更宽的序列空间探索和更高的适应性变量预测方面表现出很高的概括能力。我们调查了上位性的作用,并表明推断的模型提供了有关分子折叠中3D接触的结构信息。
更新日期:2020-08-08
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