School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
Department of Computer Science, College of Engineering, Design and Physical Sciences and Synthetic Biology Theme, Institute of Environment, Health and Societies, Brunel University London, Uxbridge, London, UK.
Bioinformatics. 2018 Jan 15;34(2):207-214. doi: 10.1093/bioinformatics/btx515.
A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant.
We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset.
The DFS code is available under GPL at https://fornililab.github.io/dfs/.
Supplementary data are available at Bioinformatics online.
蛋白质中的有害氨基酸变化可以通过第二位置拯救突变来补偿。这些补偿机制可以通过药物来模拟。特别是，拯救突变的位置可用于鉴定可被小分子靶向的蛋白质区域，以重新激活受损的突变体。
我们提出了第一个用于检测拯救位点的通用计算方法。通过通过施加力来模拟突变的效果，双力扫描（DFS）方法确定了使蛋白质结构对致病性突变影响最具弹性的第二位置残基。我们将 DFS 预测与包含实验验证和推测进化相关拯救位点的两个数据集进行了比较。预测与实验数据之间存在非常好的一致性。实际上，DFS 正确预测了 p53 中几乎一半的拯救位点，而剩余的 65％的位点与 DFS 预测结果接触。在进化数据集的其他蛋白质中也发现了类似的结果。
DFS 代码可在 https://fornililab.github.io/dfs/ 下根据 GPL 获得。
补充数据可在Bioinformatics 在线获得。
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