Probing the Formation, Structure and Free Energy Relationships of M Protein Dimers of SARS-CoV-2

Evaluating Molecular Mechanism of Viral Inhibition of Aerosolized Smart Nano-Enabled Antiviral Therapeutic (SNAT) on SARS-CoV-2-Infected Hamsters

Smart Nano-enabled Antiviral Therapeutic (SNAT) is a promising nanodrug that previously demonstrated efficacy in preclinical studies to alleviate SARS-CoV-2 pathology in hamsters. SNAT comprises taxoid (Tx)-decorated amino (NH2)-functionalized near-atomic size positively charged silver nanoparticles (Tx-[NH2-AgNPs]). Herein, we aimed to elucidate the molecular mechanism of the viral inhibition and safety of aerosolized SNAT treatment in SARS-CoV-2-infected golden Syrian hamsters. High-resolution transmission electron microscopy (HR-TEM) coupled with energy dispersive spectroscopy (EDS) and ELISAs showed SNAT binds directly to the SARS-CoV-2 virus by interacting with intact spike (S) protein, specifically to S2 subunit. SNAT (≥1 µg/mL) treatment significantly lowered SARS-CoV-2 infections of Calu-3 cells. Extraction-free whole transcriptome assay was used to detect changes in circulatory micronome in hamsters treated intranasally with SNAT (two doses of 10 µg/mL of 2 mL each administered 24 h apart). Uninfected hamsters treated with SNAT had altered circulatory concentrations of 18 microRNAs (8 miRNAs upregulated, 10 downregulated) on day 3 post-treatment compared to uninfected controls. SNAT-induced downregulation of miR-141-3p and miR-200b-3p may reduce viral replication and inflammation by targeting Ythdf2 and Slit2, respectively. Further, SNAT treatment significantly lowered IL-6 expression in infected hamster lungs compared to untreated infected hamsters. Taken together, we demonstrate that SNAT binds directly to SARS-CoV-2 via the S protein to prevent viral entry and propose a model by which SNAT alters the cellular miRNA-directed milieu to promote antiviral cellular processes and neutralize infection. Our results provide insights into the use of low-dose intranasally delivered SNAT in treating SARS-CoV-2 infections in a hamster model.

Spontaneous Dimerization and Distinct Packing Modes of Transmembrane Domains in Receptor Tyrosine Kinases

The insulin receptor (IR) and the insulin-like growth factor-1 receptor (IGF1R) are homodimeric transmembrane glycoproteins that transduce signals across the membrane on binding of extracellular peptide ligands. The structures of IR/IGF1R fragments in apo and liganded states have revealed that the extracellular subunits of these receptors adopt Λ-shaped configurations to which are connected the intracellular tyrosine kinase (TK) domains. The binding of peptide ligands induces structural transitions in the extracellular subunits leading to potential dimerization of transmembrane domains (TMDs) and autophosphorylation in TKs. However, the activation mechanisms of IR/IGF1R, especially the role of TMDs in coordinating signal-inducing structural transitions, remain poorly understood, in part due to the lack of structures of full-length receptors in apo or liganded states. While atomistic simulations of IR/IGF1R TMDs showed that these domains can dimerize in single component membranes, spontaneous unbiased dimerization in a plasma membrane having physiologically representative lipid composition has not been observed. We address this limitation by employing coarse-grained (CG) molecular dynamics simulations to probe the dimerization propensity of IR/IGF1R TMDs. We observed that TMDs in both receptors spontaneously dimerized independent of their initial orientations in their dissociated states, signifying their natural propensity for dimerization. In the dimeric state, IR TMDs predominantly adopted X-shaped configurations with asymmetric helical packing and significant tilt relative to the membrane normal, while IGF1R TMDs adopted symmetric V-shaped or parallel configurations with either no tilt or a small tilt relative to the membrane normal. Our results suggest that IR/IGF1R TMDs spontaneously dimerize and adopt distinct dimerized configurations.

Discovery of non-peptide GLP-1r natural agonists for enhancing coronary safety in type 2 diabetes patients

This study explores the computational discovery of non-peptide agonists targeting the Glucagon-Like Peptide-1 Receptor (GLP-1R) to enhance the safety of major coronary outcomes in individuals affected by Type 2 Diabetes. The objective is to identify novel compounds that can activate the GLP-1R pathway without the limitations associated with peptide agonists. Type 2 diabetes mellitus (T2DM) is associated with an increased risk of cardiovascular disease (CVD) and mortality, which is attributed to the accumulation of fat in organs, including the heart. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are frequently used to manage T2DM and could potentially offer cardiovascular benefits. Therefore, this study examines non-peptide agonists of GLP-1R to improve coronary safety in type 2 diabetes patients. After rigorous assessments, two standout candidates were identified, with natural compound 12 emerging as the most promising. This study represents a notable advancement in enhancing the management of coronary outcomes among individuals with type 2 diabetes. The computational methodology employed successfully pinpointed potential GLP-1R natural agonists, providing optimism for the development of safer and more effective therapeutic interventions. Although computational methodologies have provided crucial insights, realizing the full potential of these compounds requires extensive experimental investigations, crucial in advancing therapeutic strategies for this critical patient population.Communicated by Ramaswamy H. Sarma.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Biophysical Modeling of SARS-CoV-2 Assembly: Genome Condensation and Budding

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spurred unprecedented and concerted worldwide research to curtail and eradicate this pathogen. SARS-CoV-2 has four structural proteins: Envelope (E), Membrane (M), Nucleocapsid (N), and Spike (S), which self-assemble along with its RNA into the infectious virus by budding from intracellular lipid membranes. In this paper, we develop a model to explore the mechanisms of RNA condensation by structural proteins, protein oligomerization and cellular membrane-protein interactions that control the budding process and the ultimate virus structure. Using molecular dynamics simulations, we have deciphered how the positively charged N proteins interact and condense the very long genomic RNA resulting in its packaging by a lipid envelope decorated with structural proteins inside a host cell. Furthermore, considering the length of RNA and the size of the virus, we find that the intrinsic curvature of M proteins is essential for virus budding. While most current research has focused on the S protein, which is responsible for viral entry, and it has been motivated by the need to develop efficacious vaccines, the development of resistance through mutations in this crucial protein makes it essential to elucidate the details of the viral life cycle to identify other drug targets for future therapy. Our simulations will provide insight into the viral life cycle through the assembly of viral particles de novo and potentially identify therapeutic targets for future drug development.

Electrostatic Map of the SARS-CoV-2 Virion Specifies Binding Sites of the Antiviral Cationic Photosensitizer

Electrostatics is an important part of virus life. Understanding the detailed distribution of charges over the surface of a virus is important to predict its interactions with host cells, antibodies, drugs, and different materials. Using a coarse-grained model of the entire viral envelope developed by D. Korkin and S.-J. Marrink’s scientific groups, we created an electrostatic map of the external surface of SARS-CoV-2 and found a highly heterogeneous distribution of the electrostatic potential field of the viral envelope. Numerous negative patches originate mainly from negatively charged lipid domains in the viral membrane and negatively charged areas on the “stalks” of the spike (S) proteins. Membrane (M) and envelope (E) proteins with the total positive charge tend to colocalize with the negatively charged lipids. In the E protein pentamer exposed to the outer surface, negatively charged glutamate residues and surrounding lipids form a negative electrostatic potential ring around the channel entrance. We simulated the interaction of the antiviral octacationic photosensitizer octakis(cholinyl)zinc phthalocyanine with the surface structures of the entire model virion using the Brownian dynamics computational method implemented in ProKSim software (version r661). All mentioned negatively charged envelope components attracted the photosensitizer molecules and are thus potential targets for reactive oxygen generated in photosensitized reactions.

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

Conserved Targets to Prevent Emerging Coronaviruses

Novel coronaviruses emerged as zoonotic outbreaks in humans in 2003 (SARS), 2012 (MERS), and notably in 2019 (SARS2), which resulted in the COVID-19 pandemic, causing worldwide health and economic disaster. Vaccines provide the best protection against disease but cannot be developed and engineered quickly enough to prevent emerging viruses, zoonotic outbreaks, and pandemics. Antivirals are the best first line of therapeutic defense against novel emerging viruses. Coronaviruses are plus sense, single stranded, RNA genome viruses that undergo frequent genetic mutation and recombination, allowing for the emergence of novel coronavirus strains and variants. The molecular life cycle of the coronavirus family offers many conserved activities to be exploited as targets for antivirals. Here, we review the molecular life cycle of coronaviruses and consider antiviral therapies, approved and under development, that target the conserved activities of coronaviruses. To identify additional targets to inhibit emerging coronaviruses, we carried out in silico sequence and structure analysis of coronavirus proteins isolated from bat and human hosts. We highlight conserved and accessible viral protein domains and residues as possible targets for the development of viral inhibitors. Devising multiple antiviral therapies that target conserved viral features to be used in combination is the best first line of therapeutic defense to prevent emerging viruses from developing into outbreaks and pandemics.