Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

Due to the lack of efficient therapeutic options and clinical trial limitations, the FDA-approved drugs can be a good choice to handle Coronavirus disease (COVID-19). Many reports have enough evidence for the use of FDA-approved drugs which have inhibitory potential against target proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we utilized a structure-based drug design approach to find possible drug candidates from the existing pool of FDA-approved drugs and checked their effectiveness against the SARS-CoV-2. We performed virtual screening of the FDA-approved drugs against the main protease (Mpro) of SARS-CoV-2, an essential enzyme, and a potential drug target. Using well-defined computational methods, we identified Glecaprevir and Maraviroc (MVC) as the best inhibitors of SARS-CoV-2 Mpro. Both drugs bind to the substrate-binding pocket of SARS-CoV-2 Mpro and form a significant number of non-covalent interactions. Glecaprevir and MVC bind to the conserved residues of substrate-binding pocket of SARS-CoV-2 Mpro. This work provides sufficient evidence for the use of Glecaprevir and MVC for the therapeutic management of COVID-19 after experimental validation and clinical manifestations.

Protein-ligand interaction is an imperative subject in structure-based drug design and protein function prediction process. Molecular docking is a computational method which predicts the binding of a ligand molecule to the particular receptor. It predicts the binding pose, strength and binding affinity of the molecules using various scoring functions. Molecular docking and molecular dynamics simulations are widely used in combination to predict the binding modes, binding affinities and stability of different protein-ligand systems. With advancements in algorithms and computational power, molecular dynamics simulation is now a fundamental tool to investigative bio-molecular assemblies at atomic level. These methods in association with experimental support have been of great value in modern drug discovery and development. Nowadays, it has become an increasingly significant method in drug discovery process. In this review, we focus on protein-ligand interactions using molecular docking, virtual screening and molecular dynamics simulations. Here, we cover an overview of the available methods for molecular docking and molecular dynamics simulations, and their advancement and applications in the area of modern drug discovery. The available docking software and their advancement including application examples of different approaches for drug discovery are also discussed. We have also introduced the physicochemical foundations of molecular docking and simulations, mainly from the perception of bio-molecular interactions.

Graphical abstract

Exploring protein–ligand interactions is a subject of immense interest, as it provides deeper insights into molecular recognition, mechanism of interaction and subsequent functions. Predicting an accurate model for a protein–ligand interaction is a challenging task. Molecular docking is a computational method used for predicting the preferred orientation, binding conformations and the binding affinity of a ligand to a macromolecular target, especially protein. It has been applied in ‘virtual high-throughput screening’ of chemical libraries containing millions of compounds to find potential leads in drug design and discovery. Here, we have developed InstaDock, a free and open access Graphical User Interface (GUI) program that performs molecular docking and high-throughput virtual screening efficiently. InstaDock is a single-click GUI that uses QuickVina-W, a modified version of AutoDock Vina for docking calculations, made especially for the convenience of non-bioinformaticians and for people who are not experts in using computers. InstaDock facilitates onboard analysis of docking and visual results in just a single click. To sum up, InstaDock is the easiest and more interactive interface than ever existing GUIs for molecular docking and high-throughput virtual screening. InstaDock is freely available for academic and industrial research purposes via https://hassanlab.org/instadock.

Microtubule affinity-regulating kinase 4 (MARK4) has recently been identified as a potential drug target for several complex diseases including cancer, diabetes and neurodegenerative disorders. Inhibition of MARK4 activity is an appealing therapeutic option to treat such diseases. Here, we have performed structure-based virtual high-throughput screening of 100,000 naturally occurring compounds from ZINC database against MARK4 to find its potential inhibitors. The resulted hits were selected, based on the binding affinities, docking scores and selectivity. Further, binding energy calculation, Lipinski filtration and ADMET prediction were carried out to find safe and better hits against MARK4. Best 10 compounds bearing high specificity and binding efficiency were selected, and their binding pattern to MARK4 was analyzed in detail. Finally, 100 ns molecular dynamics simulation was performed to evaluate; the dynamics stability of MARK4-compound complex. In conclusion, these selected natural compounds from ZINC database might be potential leads against MARK4, and can further be exploited in drug design and development for associated diseases.

Microtubule affinity-regulating kinase (MARK4) plays a key role in Alzheimer’s disease (AD) development as its overexpression is directly linked to increased tau phosphorylation. MARK4 is a potential drug target of AD and is thus its structural features are employed in the development of new therapeutic molecules. Donepezil (DP) and rivastigmine tartrate (RT) are acetylcholinesterase (AChE) inhibitors and are used to treat symptomatic patients of mild to moderate AD. In keeping with the therapeutic implications of DP and RT in AD, we performed binding studies of these drugs with the MARK4. Both DP and RT bound to MARK4 with a binding constant (K) of 107 M−1. The temperature dependency of binding parameters revealed MARK−DP complex to be guided by static mode while MARK−RT complex to be guided by both static and dynamic quenching. Both drugs inhibited MARK4 with IC50 values of 5.3 μM (DP) and 6.74 μM (RT). The evaluation of associated enthalpy change (ΔH) and entropy change (ΔS) implied the complex formation to be driven by hydrogen bonding making it seemingly strong and specific. Isothermal titration calorimetry further advocated a spontaneous binding. In vitro observations were further complemented by the calculation of binding free energy by molecular docking and interactions with the functionally-important residues of the active site pocket of MARK4. This study signifies the implications of AChE inhibitors, RT, and DP in Alzheimer’s therapy targeting MARK4.

A continual rise in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection causing coronavirus disease (COVID-19) has become a global threat. The main problem comes when SARS-CoV-2 gets mutated with the rising infection and becomes more lethal for humankind than ever. Mutations in the structural proteins of SARS-CoV-2, i.e., the spike surface glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N), and replication machinery enzymes, i.e., main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) creating more complexities towards pathogenesis and the available COVID-19 therapeutic strategies. This study analyzes how a minimal variation in these enzymes, especially in S protein at the genomic/proteomic level, affects pathogenesis. The structural variations are discussed in light of the failure of small molecule development in COVID-19 therapeutic strategies. We have performed in-depth sequence- and structure-based analyses of these proteins to get deeper insights into the mechanism of pathogenesis, structure-function relationships, and development of modern therapeutic approaches. Structural and functional consequences of the selected mutations on these proteins and their association with SARS-CoV-2 virulency and human health are discussed in detail in the light of our comparative genomics analysis.