Emerging structure-based computational methods to screen the exploding accessible chemical space

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

A molecular docking exploration of the large extracellular loop of tetraspanin CD81 with small molecules

Tetraspanin CD81 is a transmembrane protein used as a co-receptor by different viruses and implicated in some cancer and inflammatory diseases. The design of therapeutic small molecules targeting CD81 lags behind monoclonal antibodies and peptides but different synthetic and natural products binding to CD81 have been identified. We have investigated the interaction between synthetic compounds and CD81, considering both the cholesterol-bound full-length receptor and a truncated protein corresponding to the large extracellular loop (LEL) of the tetraspanin. They represent the closed and open conformations of the protein, respectively. Stable complexes were characterized with bi-aryl compounds (notably the quinolinone-benzothiazole 6) and atypical molecules bearing a 1-amino-boraadamantane scaffold well adapted to interact with CD81 (5a-d). In each case, the mode of binding to CD81 was analyzed, the binding sites identified and the molecular contacts determined. The narrow intra-LEL binding site of CD81 can accommodate the elongated bi-aryl 6 but not a series of isosteric compounds with a bis(bicyclic) scaffold. The bora-adamantane derivatives appeared to bind well to CD81, but essentially to the external surface of the protein loop. The binding selectivity of the compounds was assessed comparing binding to the LEL of tetraspanins CD81, CD9 and Tspan15. A net preference for CD81 over CD9 was evidenced, but the LEL of Tspan15 also provided a suitable binding site for the compounds, notably for the bora-adamantane derivatives. This work provides an aid to the identification and design of tetraspanin-binding small molecules, underlining the distinct behavior of the open and closed conformation of the protein for drug binding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00203-6.

Melatonin drugs inhibit SARS-CoV-2 entry into the brain and virus-induced damage of cerebral small vessels

COVID-19 is a complex disease with short- and long-term respiratory, inflammatory and neurological symptoms that are triggered by the infection with SARS-CoV-2. Invasion of the brain by SARS-CoV-2 has been observed in humans and is postulated to be involved in post-COVID state. Brain infection is particularly pronounced in the K18-hACE2 mouse model of COVID-19. Prevention of brain infection in the acute phase of the disease might thus be of therapeutic relevance to prevent long-lasting symptoms of COVID-19. We previously showed that melatonin or two prescribed structural analogs, agomelatine and ramelteon delay the onset of severe clinical symptoms and improve survival of SARS-CoV-2-infected K18-hACE2 mice. Here, we show that treatment of K18-hACE2 mice with melatonin and two melatonin-derived marketed drugs, agomelatine and ramelteon, prevents SARS-CoV-2 entry in the brain, thereby reducing virus-induced damage of small cerebral vessels, immune cell infiltration and brain inflammation. Molecular modeling analyses complemented by experimental studies in cells showed that SARS-CoV-2 entry in endothelial cells is prevented by melatonin binding to an allosteric-binding site on human angiotensin-converting enzyme 2 (ACE2), thus interfering with ACE2 function as an entry receptor for SARS-CoV-2. Our findings open new perspectives for the repurposing of melatonergic drugs and its clinically used analogs in the prevention of brain infection by SARS-CoV-2 and COVID-19-related long-term neurological symptoms.

SINAPs: A Software Tool for Analysis and Visualization of Interaction Networks of Molecular Dynamics Simulations

As long as the structural study of molecular mechanisms requires multiple molecular dynamics reflecting contrasted bioactive states, the subsequent analysis of molecular interaction networks remains a bottleneck to be fairly treated and requires a user-friendly 3D view of key interactions. Structural Interaction Network Analysis Protocols (SINAPs) is a proprietary python tool developed to (i) quickly solve key interactions able to distinguish two protein states, either from two sets of molecular dynamics simulations or from two crystallographic structures, and (ii) render a user-friendly 3D view of these key interactions through a plugin of UCSF Chimera, one of the most popular open-source viewing software for biomolecular systems. Through two case studies, glucose transporter-1 (GLUT-1) and A2A adenosine receptor (A2AR), SINAPs easily pinpointed key interactions observed experimentally and relevant for their bioactivities. This very effective tool was thus applied to identify the amino acids involved in the molecular enzymatic mechanisms ruling the activation of an immunomodulator drug candidate, P28 glutathione-S-transferase (P28GST). SINAPs is freely available at https://github.com/ParImmune/SINAPs.