通过蛋白质折叠的变化理解大规模测序数据集。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
David Shorthouse, Harris Lister, Gemma S Freeman, Benjamin A Hall
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引用次数: 0

摘要

高质量、低成本测序技术的发展为了解基因变异如何改变疾病中的细胞行为创造了巨大的机会。然而,观察到的变异的高度多样性使人们注意到,需要对意义不确定的变异对表型的突变影响进行预测建模。这在临床上尤为重要,因为它具有指导临床诊断和患者治疗的潜在价值。最近的计算建模突显了突变诱导的蛋白质错误折叠作为蛋白质或结构域功能丧失的常见机制的重要性,这得益于使大型计算筛选变得可行的方法的发展。在此,我们回顾了这种方法最近在不同基因上的应用,以及它们如何促进和支持了后续研究。我们将进一步讨论该方法的发展,以及该方法在越来越多的高通量实验方法中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding large scale sequencing datasets through changes to protein folding.

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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