Chen Bolin, Han Yourui, Shang Xuequn, Zhang Shenggui
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, China.
Front Pharmacol. 2022 Feb 21;13:813391. doi: 10.3389/fphar.2022.813391. eCollection 2022.
The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread all over the world. Since currently no effective antiviral treatment is available and those original inhibitors have no significant effect, the demand for the discovery of potential novel SARS-CoV-2 inhibitors has become more and more urgent. In view of the availability of the inhibitor-bound SARS-CoV-2 Mpro and PLpro crystal structure and a large amount of proteomics knowledge, we attempted using the existing coronavirus inhibitors to synthesize new ones, which combined the advantages of similar effective substructures for COVID-19 treatment. To achieve this, we first formulated this issue as a non-equidimensional inhibitor clustering and a following cluster center generating problem, where three essential challenges were carefully addressed, which are 1) how to define the distance between pairwise inhibitors with non-equidimensional molecular structure; 2) how to group inhibitors into clusters when the dimension is different; 3) how to generate the cluster center under this non-equidimensional condition. To be more specific, a novel matrix Kronecker product (, )-norm was first defined to induce the distance (, ) between two inhibitors. Then, the hierarchical clustering approach was conducted to find similar inhibitors, and a novel iterative algorithm-based Kronecker product (, )-norm was designed to generate individual cluster centers as the drug candidates. Numerical experiments showed that the proposed methods can find novel drug candidates efficiently for COVID-19, which has provided valuable predictions for further biological evaluations.
由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的新型冠状病毒病（COVID-19）已在全球蔓延。由于目前尚无有效的抗病毒治疗方法，且那些原始抑制剂效果不显著，发现潜在新型SARS-CoV-2抑制剂的需求变得越来越迫切。鉴于已有的与抑制剂结合的SARS-CoV-2 Mpro和PLpro晶体结构以及大量蛋白质组学知识，我们尝试利用现有的冠状病毒抑制剂合成新的抑制剂，将类似有效亚结构的优势结合起来用于COVID-19治疗。为实现这一目标，我们首先将此问题表述为一个非等维抑制剂聚类以及后续的聚类中心生成问题，其中仔细解决了三个关键挑战，即1）如何定义具有非等维分子结构的成对抑制剂之间的距离；2）当维度不同时如何将抑制剂分组为聚类；3）如何在这种非等维条件下生成聚类中心。更具体地说，首先定义了一种新颖的矩阵克罗内克积（，）范数来诱导两个抑制剂之间的距离（，）。然后，采用层次聚类方法来寻找相似的抑制剂，并设计了一种基于迭代算法的新颖克罗内克积（，）范数来生成作为候选药物的各个聚类中心。数值实验表明，所提出的方法能够有效地为COVID-19找到新型候选药物，为进一步的生物学评估提供了有价值的预测。
