Abstract
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 (p, m)-norm was first defined to induce the distance D p (A, B) between two inhibitors. Then, the hierarchical clustering approach was conducted to find similar inhibitors, and a novel iterative algorithm–based Kronecker product (p, m)-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.
Highlights
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has shockingly spread and caused huge social and economic destruction (Jin et al, 2020)
Hope to use existing coronavirus inhibitors with similar structures to synthesize new inhibitors that have comprehensive advantages and may be effective against COVID-19. We model this as a nonequidimensional inhibitor clustering and the following cluster center generating problem
main protease (Mpro) and papain-like protease (PLpro) crystal structure and a large amount of proteomics knowledge, we hope to synthesize inhibitors with similar structures or functions to discover a new inhibitor which may has comprehensive advantages. We model it as the discovery problem of the cluster center and propose a novel approach to discover some new inhibitors by finding cluster centers of known coronavirus inhibitors, such as SARS-CoV
Summary
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has shockingly spread and caused huge social and economic destruction (Jin et al, 2020). Two viral protease are the prerequisite enzymes of the viral replication and maturation which are raised upon proteolytic cleavage of pp1a and pp1b: one is main protease (Mpro) (Main protease Mpro called chymotrypsin-like protease 3CLpro) and another is the papain-like protease (PLpro) enzymes (Lin et al, 2018; Ghosh et al, 2020; Elmezayen et al, 2021; Joshi et al, 2021) Both proteases are essential for SARS-CoV-2 viral replication and, can be considered as drug-able targets (Ghosh et al, 2020)
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