论文标题
通过顺序蒙特卡洛评估突变对COVID-19峰蛋白结构的影响
Assessing the impacts of mutations to the structure of COVID-19 spike protein via sequential Monte Carlo
论文作者
论文摘要
蛋白质在促进2019年小说冠状病毒的传染性方面起着关键作用。特定的尖峰蛋白使该病毒能够与人类细胞结合，因此对其三维结构的透彻理解对于开发有效的治疗干预措施至关重要。但是，由于突变，其结构可能会随着时间的流逝而继续发展。在本文中，我们使用数据科学的角度来研究由于其氨基酸序列中持续的突变引起的潜在结构影响。为此，我们确定了蛋白质的关键段，并采用顺序的蒙特卡洛采样方法来检测不同氨基酸序列低能构象空间的可能变化。这种计算方法可以进一步了解这种蛋白质结构并补充实验室的努力。
Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. In this paper, we use a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of low-energy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.
