Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach

Abstract

The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics—generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.

Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning

“…Many drugs among the FDA-approved drugs and also among the natural product datasets bind with high affinity not only to one target protein but also to another one (conivaptan, paritaprevir, simeprevir, dihydroergotamine, ZINC000027215482, ZINC000252515584, loniflavone, procyanidin). There are already published studies reporting the potential of paritaprevir [ 50 ], simeprevir [ 51 ], dihydroergotamine [ 52 ], procyanidin [ 53 ] against SARS-CoV-2. These compounds deserve special attention.…”

Computational Investigation of Increased Virulence and Pathogenesis of SARS-CoV-2 Lineage B.1.1.7

“…Currently, a major attention is given to identify the location and spread of highly contagious variants of SARS-CoV-2 as it is feared that certain variants can escape the vaccination or develop resistance to drugs 45,46 based therapy. Along this line the variant reported in South Africa and in United Kingdom (referred as B.1.1.7 variant) are reported to be more contagious and are reported to spread at 70% higher rate than the wild variant.…”

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

“…The strength that makes M13 a robust pIC 50 prediction model is the use of experimental data and that a biological target does not need to be identified. The methods reported, such as the ones using a double (Autodock Vina and MM-GBSA) scoring approach [ 58 ] and virtual screening [ 59 ], rely only on in silico methodology, with the flaws these methods have including the requirement of a specific biological target.…”

“…First, the system was relaxed and then equilibrated using a constant number of particles, pressure, and temperature (NVT) for 100 ps at 300 K, using Berendsen thermostat temperature coupling [ 109 ] before starting the dynamic. For the simulation parameters, thermostat coupling was set to 300 K, pressure coupling at 1 Barr and the simulation ran for 40 ns (40,000 steps of 1ps each) [ 58 ].…”