The chemical structure of PF-07321332, the first orally available Covid-19 clinical candidate, has recently been revealed by Pfizer. No information has been provided about the interaction pattern between PF-07321332 and its biomolecular counterpart, the SARS-CoV-2 main protease (M pro ). In the present work, we exploited Supervised Molecular Dynamics (SuMD) simulations to elucidate the key features that characterise the interaction between this drug candidate and the protease, emphasising similarities and differences with other structurally related inhibitors such as Boceprevir and PF-07304814. The structural insights provided by SuMD will hopefully be able to inspire the rational discovery of other potent and selective protease inhibitors.

Coronavirus SARS-CoV-2 is a recently discovered single-stranded RNA betacoronavirus, responsible for a severe respiratory disease known as coronavirus disease 2019, which is rapidly spreading. Chinese health authorities, as a response to the lack of an effective therapeutic strategy, started to investigate the use of lopinavir and ritonavir, previously optimized for the treatment and prevention of HIV/AIDS viral infection. Despite the clinical use of these two drugs, no information regarding their possible mechanism of action at the molecular level is still known for SARS-CoV-2. Very recently, the crystallographic structure of the SARS-CoV-2 main protease (Mpro), also known as C30 Endopeptidase, was published. Starting from this essential structural information, in the present work we have exploited supervised molecular dynamics, an emerging computational technique that allows investigating at an atomic level the recognition process of a ligand from its unbound to the final bound state. In this research, we provided molecular insight on the whole recognition pathway of Lopinavir, Ritonavir, and Nelfinavir, three potential C30 Endopeptidase inhibitors, with the last one taken into consideration due to the promising in-vitro activity shown against the structurally related SARS-CoV protease.

Recent studies have highlighted the key role of Casein kinase 1 δ (CK1δ) in the development of several neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). So far, CK1δ inhibitors are noncovalent ATP competitive ligands and no drugs are currently available for this molecular target, hence the interest in developing new CK1δ inhibitors. The study aims to identify new inhibitors able to bind the enzyme; by a dual approach in silico/in vitro, the virtual screening has been performed on an in-house chemical library, which was previously designed and synthesized for other targets. The work can, therefore, be seen in the scaffold repurposing logic. The proposed strategy has led to the identification of two hits, having a novel scaffold in the landscape of CK1δ inhibitors and with an activity in the micromolar range.

Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the present work, we set up and applied a computational workflow for the identification of putative fragment binders in large virtual databases. To validate the method, the selected compounds were tested in vitro to assess the CK1δ inhibition.

Adenosine receptors have been a promising class of targets for the development of new therapies for several diseases. In recent years, a renewed interest in this field has risen, thanks to the implementation of a novel class of agonists that lack the ribose moiety, once considered essential for the agonistic profile. Recently, an X-ray crystal structure of the A2A adenosine receptor has been solved, providing insights about the receptor activation from this novel class of agonists. Starting from this structural information, we have performed supervised molecular dynamics (SuMD) simulations to investigate the binding pathway of a non-nucleoside adenosine receptor agonist as well as one of three classic agonists. Furthermore, we analyzed the possible role of water molecules in receptor activation.