Molecular Multi-target Approach on COVID-19 for Designing Novel Chemicals

Abstract

The pandemic situation that emerged due to the SARS-CoV-2 virus has dismayed the social and economic set up badly, and still no cure is available to combat this situation. To spread all the scientific outcomes and data around the world, all the major scientific journals and databases are providing open access to their content across the globe. Due to the focused research, a considerable amount of multidimensional data has been generated in a very short time to understand the fundamental behavior of the virus, which is useful for novel therapeutic development. Many countries are working on vaccine development; however progress towards the novel drug pipeline is also parallelly going on to reduce the mortality rate. From a ∼30kb size viral genome, many different experimentally characterized and validated proteins are identified followed by 3D structure determination to explore the antiviral targets more rapidly, which is included in this chapter. Proteases are always found as promising antiviral target proteins and in the case of SARS-CoV-2, cysteine proteases such as 3CLpro (chymotrypsin-like protease) and PLpro (papain-like protease) serve the same. Both cysteine proteases are involved in the cleavage process of polypeptide coming out from the RNA genome of the virus, and this cleavage assists in the replication apparatus complex formation necessary for the viral existence. To target proteases, many molecular mechanics-based computational screening protocols have been applied to find the inhibitors against them; however, due to the inherent limitation (selectivity) in the approach, no major success has been achieved yet. In the present study, we have reviewed different computational methods applied in search of inhibitors against the targets of COVID-19. In addition, using our method CliquePharm, we have attempted to generate the multi-target specific ab-initio pharmacophore models to search for the specific novel inhibitors from a database of antiviral chemicals. Apo-structures of both cysteine proteases are used to design the different sizes and types of the pharmacophore models, which are further utilized to screen the locally setup chemical database to identify the multi-class specific inhibitors. With the help of this study, we were not only able to explore the commonly available interactions within the selected proteases to capture the multi-targeted drugs but also this approach will suppress the chance of any resistance against the designed inhibitors.