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通过饱和诱变数据集分析预测功能决定残基和埋藏残基

Prediction of Function Determining and Buried Residues Through Analysis of Saturation Mutagenesis Datasets.

作者信息

Bhasin Munmun, Varadarajan Raghavan

机构信息

Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.

Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India.

出版信息

Front Mol Biosci. 2021 Mar 11;8:635425. doi: 10.3389/fmolb.2021.635425. eCollection 2021.

Abstract

Mutational scanning can be used to probe effects of large numbers of point mutations on protein function. Positions affected by mutation are primarily at either buried or at exposed residues directly involved in function, hereafter designated as active-site residues. In the absence of prior structural information, it has not been easy to distinguish between these two categories of residues. We curated and analyzed a set of twelve published deep mutational scanning datasets. The analysis revealed differential patterns of mutational sensitivity and substitution preferences at buried and exposed positions. Prediction of buried-sites solely from the mutational sensitivity data was facilitated by incorporating predicted sequence-based accessibility values. For active-site residues we observed mean sensitivity, specificity and accuracy of 61, 90 and 88% respectively. For buried residues the corresponding figures were 59, 90 and 84% while for exposed non active-site residues these were 98, 44 and 82% respectively. We also identified positions which did not follow these general trends and might require further experimental re-validation. This analysis highlights the ability of deep mutational scans to provide important structural and functional insights, even in the absence of three-dimensional structures determined using conventional structure determination techniques, and also discuss some limitations of the methodology.

摘要

突变扫描可用于探究大量点突变对蛋白质功能的影响。受突变影响的位置主要是位于埋藏残基处或直接参与功能的暴露残基处,以下称为活性位点残基。在缺乏先前结构信息的情况下,区分这两类残基并非易事。我们整理并分析了一组十二个已发表的深度突变扫描数据集。分析揭示了埋藏和暴露位置处突变敏感性和取代偏好的差异模式。通过纳入基于预测序列的可及性值,有助于仅从突变敏感性数据预测埋藏位点。对于活性位点残基,我们分别观察到平均敏感性、特异性和准确性为61%、90%和88%。对于埋藏残基,相应的数字分别为59%、90%和84%,而对于暴露的非活性位点残基,这些数字分别为98%、44%和82%。我们还确定了不符合这些一般趋势且可能需要进一步实验重新验证的位置。该分析突出了深度突变扫描即使在没有使用传统结构测定技术确定三维结构的情况下,也能提供重要的结构和功能见解的能力,并讨论了该方法的一些局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a87/7991590/9dcf6f261e56/fmolb-08-635425-g001.jpg

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