论文标题
与深层暹罗网络的糖基化尖峰（S）糖基化尖峰中HR1结构域对潜在肽配体的一声筛选
One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep siamese network
论文作者
论文摘要
新颖的冠状病毒（2019-NCOV）被宣布为新的国际健康出现，尚未确定具体的药物。目前正在评估几种方法，例如蛋白酶和糖基化的峰值蛋白抑制剂，这些方法概述了冠状病毒和宿主细胞之间主要融合位点的概述。尽管如此，在糖基化尖峰（S）蛋白上的Heptad重复1（HR1）结构域是该区域的可突变性较小，然后是新抑制剂药物的最令人鼓舞的靶标。与其他药物相比，所提出方法的新颖性在于对2019-NCOV VIRUS进行深神经网络的精确训练。暹罗神经网络（SNN）已经接受了培训以扩大整个2019-NCOV蛋白序列，例如HIV-1和HIV-1和埃博拉病毒。通过这种方式，当前的深度学习系统在2019-NCOV蛋白结构之间具有对肽链接的精确知识，而对其他作品的不同，并未在未获得2019年NCOV的任何配体肽信息的公共数据集中进行了琐碎的培训。 Suddenly, the SNN shows a sensitivity of $83\%$ of peptide affinity classification, where $3027$ peptides on SATPdb bank have been tested towards the specific region HR1 of 2019-nCoV exhibiting an affinity of $93\%$ for the peptidyl-prolyl cis-trans isomerase (PPIase) peptide. PPIASE和HR1之间的这种亲和力可以为研究的新范围开放，因为几篇科学论文已经表明，PPIASE的主要抑制剂CSA免疫抑制药物抑制了不同COV病毒的繁殖包括SARS-COV和MERS-COV。最后，为了确保科学可重复性，代码和数据已在以下链接上公开：https：//github.com/bionick87/2019-ncov
The novel coronavirus (2019-nCoV) has been declared to be a new international health emergence and no specific drug has been yet identified. Several methods are currently being evaluated such as protease and glycosylated spike (S) protein inhibitors, that outlines the main fusion site among coronavirus and host cells. Notwithstanding, the Heptad Repeat 1 (HR1) domain on the glycosylated spike (S) protein is the region with less mutability and then the most encouraging target for new inhibitors drugs.The novelty of the proposed approach, compared to others, lies in a precise training of a deep neural network toward the 2019-nCoV virus. Where a Siamese Neural Network (SNN) has been trained to distingue the whole 2019-nCoV protein sequence amongst two different viruses family such as HIV-1 and Ebola. In this way, the present deep learning system has precise knowledge of peptide linkage among 2019-nCoV protein structure and differently, of other works, is not trivially trained on public datasets that have not been provided any ligand-peptide information for 2019-nCoV. Suddenly, the SNN shows a sensitivity of $83\%$ of peptide affinity classification, where $3027$ peptides on SATPdb bank have been tested towards the specific region HR1 of 2019-nCoV exhibiting an affinity of $93\%$ for the peptidyl-prolyl cis-trans isomerase (PPIase) peptide. This affinity between PPIase and HR1 can open new horizons of research since several scientific papers have already shown that CsA immunosuppression drug, a main inhibitor of PPIase, suppress the reproduction of different CoV virus included SARS-CoV and MERS-CoV. Finally, to ensure the scientific reproducibility, code and data have been made public at the following link: https://github.com/bionick87/2019-nCoV
