A computational study of cooperative binding to multiple SARS-CoV-2 proteins

The Polyanionic Drug Suramin Neutralizes Histones and Prevents Endotheliopathy

Drugs are needed to protect against the neutrophil-derived histones responsible for endothelial injury in acute inflammatory conditions such as trauma and sepsis. Heparin and other polyanions can neutralize histones but challenges with dosing or side effects such as bleeding limit clinical application. In this study, we demonstrate that suramin, a widely available polyanionic drug, completely neutralizes the toxic effects of individual histones, but not citrullinated histones from neutrophil extracellular traps. The sulfate groups on suramin form stable electrostatic interactions with hydrogen bonds in the histone octamer with a dissociation constant of 250 nM. In cultured endothelial cells (Ea.Hy926), histone-induced thrombin generation was significantly decreased by suramin. In isolated murine blood vessels, suramin abolished aberrant endothelial cell calcium signals and rescued impaired endothelial-dependent vasodilation caused by histones. Suramin significantly decreased pulmonary endothelial cell ICAM-1 expression and neutrophil recruitment caused by infusion of sublethal doses of histones in vivo. Suramin also prevented histone-induced lung endothelial cell cytotoxicity in vitro and lung edema, intra-alveolar hemorrhage, and mortality in mice receiving a lethal dose of histones. Protection of vascular endothelial function from histone-induced damage is a novel mechanism of action for suramin with therapeutic implications for conditions characterized by elevated histone levels.

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling

The recent outlook of leishmaniasis as a global public health concern coupled with the reportage of resistance and lack of efficacy of most antileishmanial drugs calls for a concerted effort to find new leads. The study combined In silico and in vitro approaches to identify novel potential synthetic small-molecule inhibitors targeting the Leishmania donovani sterol methyltransferase (LdSMT). The LdSMT enzyme in the ergosterol biosynthetic pathway is required for the parasite’s membrane fluidity, distribution of membrane proteins, and control of the cell cycle. The lack of LdSMT homologue in the human host and its conserved nature among all Leishmania parasites makes it a viable target for future antileishmanial drugs. Initially, six known inhibitors of LdSMT with IC50 < 10 μM were used to generate a pharmacophore model with a score of 0.9144 using LigandScout. The validated model was used to screen a synthetic library of 95,630 compounds obtained from InterBioScreen limited. Twenty compounds with pharmacophore fit scores above 50 were docked against the modelled three-dimensional structure of LdSMT using AutoDock Vina. Consequently, nine compounds with binding energies ranging from −7.5 to −8.7 kcal/mol were identified as potential hit molecules. Three compounds comprising STOCK6S-06707, STOCK6S-84928, and STOCK6S-65920 with respective binding energies of −8.7, −8.2, and −8.0 kcal/mol, lower than 22,26-azasterol (−7.6 kcal/mol), a known LdSMT inhibitor, were selected as plausible lead molecules. Molecular dynamics simulation studies and molecular mechanics Poisson–Boltzmann surface area calculations showed that the residues Asp25 and Trp208 were critical for ligand binding. The compounds were also predicted to have antileishmanial activity with reasonable pharmacological and toxicity profiles. When the antileishmanial activity of the three hits was evaluated in vitro against the promastigotes of L. donovani, mean half-maximal inhibitory concentrations (IC50) of 21.9 ± 1.5 μM (STOCK6S-06707), 23.5 ± 1.1 μM (STOCK6S-84928), and 118.3 ± 5.8 μM (STOCK6S-65920) were obtained. Furthermore, STOCK6S-84928 and STOCK6S-65920 inhibited the growth of Trypanosoma brucei, with IC50 of 14.3 ± 2.0 μM and 18.1 ± 1.4 μM, respectively. The identified compounds could be optimised to develop potent antileishmanial therapeutic agents.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

Comparative highlights on MERS-CoV, SARS-CoV-1, SARS-CoV-2, and NEO-CoV

The severe acute respiratory syndrome (SARS-CoV, now SARS-CoV-1), middle east respiratory syndrome (MERS-CoV), Neo-CoV, and 2019 novel coronavirus (SARS-CoV-2/COVID-19) are the most notable coronaviruses, infecting the number of people worldwide by targeting the respiratory system. All these viruses are of zoonotic origin, predominantly from bats which are one of the natural reservoir hosts for coronaviruses. Thus, the major goal of our review article is to compare and contrast the characteristics and attributes of these coronaviruses. The SARS-CoV-1, MERS-CoV, and COVID-19 have many viral similarities due to their classification, they are not genetically related. COVID-19 shares approximately 79 % of its genome with SARS-CoV-1 and about 50 % with MERS-CoV. The shared receptor protein, ACE2 exhibit the most striking genetic similarities between SARS-CoV-1 and SARS-CoV-2. SARS-CoV primarily replicates in the epithelial cells of the respiratory system, but it may also affect macrophages, monocytes, activated T cells, and dendritic cells. MERS-CoV not only infects and replicates inside the epithelial and immune cells, but it may lyse them too, which is one of the common reasons for MERS's higher mortality rate. The details of infections caused by SARS-CoV-2 and lytic replication mechanisms in host cells are currently mysterious. In this review article, we will discuss the comparative highlights of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and Neo-CoV, concerning their structural features, morphological characteristics, sources of virus origin and their evolutionary transitions, infection mechanism, computational study approaches, pathogenesis and their severity towards several diseases, possible therapeutic approaches, and preventive measures.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Computer-aided discovery, design, and investigation of COVID-19 therapeutics

Coronavirus disease 2019 (COVID-19) pandemic is currently the most serious public health threat faced by mankind. Thus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is being intensively investigated. Several vaccines are now available for clinical use. However, owing to the highly mutated nature of RNA viruses, the SARS-CoV-2 is changing at a rapid speed. Breakthrough infections by SARS-CoV-2 variants have been seen in vaccinated individuals. As a result, effective therapeutics for treating COVID-19 patients is urgently required. With the advance of computer technology, computational methods have become increasingly powerful in the biomedical research and pharmaceutical drug discovery. The applications of these techniques have largely reduced the costs and simplified processes of pharmaceutical drug developments. Intensive and extensive studies on SARS-CoV-2 proteins have been carried out and three-dimensional structures of the major SARS-CoV-2 proteins have been resolved and deposited in the Protein Data Bank. These structures provide the foundations for drug discovery and design using the structure-based computations, such as molecular docking and molecular dynamics simulations. In this review, introduction to the applications of computational methods in the discovery and design of novel drugs and repurposing of existing drugs for the treatments of COVID-19 is given. The examples of computer-aided investigations and screening of COVID-19 effective therapeutic compounds, functional peptides, as well as effective molecules from the herb medicines are discussed.