Quantum biochemistry, molecular docking, and dynamics simulation revealed synthetic peptides induced conformational changes affecting the topology of the catalytic site of SARS-CoV-2 main protease

Quantum biochemical analysis of the binding interactions between a potential inhibitory drug and the Ebola viral glycoprotein

Ebola virus disease (EVD) causes outbreaks and epidemics in West Africa that persist until today. The envelope glycoprotein of Ebola virus (GP) consists of two subunits, GP1 and GP2, and plays a key role in anchoring or fusing the virus to the host cell in its active form on the virion surface. Toremifene (TOR) is a ligand that mainly acts as an estrogen receptor antagonist; however, a recent study showed a strong and efficient interaction with GP. In this context, we aimed to evaluate the energetic affinity features involved in the interaction between GP and toremifene by computer simulation techniques using the Molecular Fractionation Method with Conjugate Caps (MFCC) scheme and quantum-mechanical (QM) calculations, as well as missense mutations to assess protein stability. We identified ASP522, GLU100, TYR517, THR519, LEU186, LEU515 as the most attractive residues in the EBOV glycoprotein structure that form the binding pocket. We divided toremifene into three regions and evaluated that region i was more important than region iii and region ii for the formation of the TOR-GP1/GP2 complex, which might control the molecular remodeling process of TOR. The mutations that caused more destabilization were ARG134, LEU515, TYR517 and ARG559, while those that caused stabilization were GLU523 and ASP522. TYR517 is a critical residue for the binding of TOR, and is highly conserved among EBOV species. Our results may help to elucidate the mechanism of drug action on the GP protein of the Ebola virus and subsequently develop new pharmacological approaches against EVD.Communicated by Ramaswamy H. Sarma.

Investigation of protein-protein interactions and hotspot region on the NSP7-NSP8 binding site in NSP12 of SARS-CoV-2

Background: The RNA-dependent RNA polymerase (RdRp) complex, essential in viral transcription and replication, is a key target for antiviral therapeutics. The core unit of RdRp comprises the nonstructural protein NSP12, with NSP7 and two copies of NSP8 (NSP81 and NSP82) binding to NSP12 to enhance its affinity for viral RNA and polymerase activity. Notably, the interfaces between these subunits are highly conserved, simplifying the design of molecules that can disrupt their interaction. Methods: We conducted a detailed quantum biochemical analysis to characterize the interactions within the NSP12-NSP7, NSP12-NSP81, and NSP12-NSP82 dimers. Our objective was to ascertain the contribution of individual amino acids to these protein-protein interactions, pinpointing hotspot regions crucial for complex stability. Results: The analysis revealed that the NSP12-NSP81 complex possessed the highest total interaction energy (TIE), with 14 pairs of residues demonstrating significant energetic contributions. In contrast, the NSP12-NSP7 complex exhibited substantial interactions in 8 residue pairs, while the NSP12-NSP82 complex had only one pair showing notable interaction. The study highlighted the importance of hydrogen bonds and π-alkyl interactions in maintaining these complexes. Intriguingly, introducing the RNA sequence with Remdesivir into the complex resulted in negligible alterations in both interaction energy and geometric configuration. Conclusion: Our comprehensive analysis of the RdRp complex at the protein-protein interface provides invaluable insights into interaction dynamics and energetics. These findings can guide the design of small molecules or peptide/peptidomimetic ligands to disrupt these critical interactions, offering a strategic pathway for developing effective antiviral drugs.

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

In silico analysis of ACE2 from different animal species provides new insights into SARS-CoV-2 species spillover

Aim: This study aimed to analyze the phylogenetic relationships between the ACE2 of humans and other animals and investigate the potential interaction between SARS-CoV-2 RBD and ACE2 of different species. Materials & methods: The phylogenetic construction and molecular interactions were assessed using computational models. Results & conclusion: Despite the evolutionary distance, 11 species had a perfect fit for the interaction between their ACE2 and SARS-CoV-2 RBD (Chinchilla lanigera, Neovison vison, Rhinolophus sinicus, Emballonura alecto, Saccopteryx bilineata, Numida meleagris). Among them, the avian N. meleagris was reported for the first time in this study as a probable SARS-CoV-2 host due to the strong molecular interactions. Therefore, predicting potential hosts for SARS-CoV-2 for understanding the epidemiological cycle and proposal of surveillance strategies.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Synthetic antiviral peptides: a new way to develop targeted antiviral drugs

The global concern over emerging and re-emerging viral infections has spurred the search for novel antiviral agents. Peptides with antiviral activity stand out, by overcoming limitations of the current drugs utilized, due to their biocompatibility, specificity and effectiveness. Synthetic peptides have been shown to be viable alternatives to natural peptides due to several difficulties of using of the latter in clinical trials. Various platforms have been utilized by researchers to predict the most effective peptide sequences against HIV, influenza, dengue, MERS and SARS. Synthetic peptides are already employed in the treatment of HIV infection. The novelty of this study is to discuss, for the first time, the potential of synthetic peptides as antiviral molecules. We conclude that synthetic peptides can act as new weapons against viral threats to humans.

Neutralizing Effect of Synthetic Peptides toward SARS-CoV-2

The outbreak caused by SARS-CoV-2 has taken many lives worldwide. Although vaccination has started, the development of drugs to either alleviate or abolish symptoms of COVID-19 is still necessary. Here, four synthetic peptides were assayed regarding their ability to protect Vero E6 cells from SARS-CoV-2 infection and their toxicity to human cells and zebrafish embryos. All peptides had some ability to protect cells from infection by SARS-CoV-2 with the D614G mutation. Molecular docking predicted the ability of all peptides to interact with and induce conformational alterations in the spike protein containing the D614G mutation. PepKAA was the most effective peptide, by having the highest docking score regarding the spike protein and reducing the SARS-CoV-2 plaque number by 50% (EC₅₀) at a concentration of 0.15 mg mL^-1. Additionally, all peptides had no toxicity to three lines of human cells as well as to zebrafish larvae and embryos. Thus, these peptides have potential activity against SARS-CoV-2, making them promising to develop new drugs to inhibit cell infection by SARS-CoV-2.

Computational approach, scanning electron and fluorescence microscopies revealed insights into the action mechanisms of anticandidal peptide Mo-CBP3-PepIII

AIMS

The Candida genus is composed of opportunistic pathogens that threaten public health. Given the increase in resistance to current drugs, it is necessary to develop new drugs to treat infections by these pathogens. Antimicrobial peptides are promising alternative molecules with low cost, broad action spectrum and low resistance induction. This study aimed to clarify the action mechanisms of synthetic peptides against Candida albicans.

MAIN METHODS

The mode of action of the anticandidal peptides Mo-CBP₃-PepIII were analyzed through molecular dynamics and quantum biochemistry methods against Exo-β-1,3-glucanase (EXG), vital to cell wall metabolism. Furthermore, scanning electron (SEM) and fluorescence (FM) microscopies were employed to corroborate the in silico data.

KEY FINDINGS

Mo-CBP₃-PepIII strongly interacted with EXG (-122.2 kcal mol^-1) at the active site, higher than the commercial inhibitor pepstatin. Also, molecular dynamics revealed the insertion of Mo-CBP₃-PepIII into the yeast membrane. SEM analyses revealed that Mo-CBP₃-PepIII induced cracks and scars of the cell wall and FM analyses confirmed the pore formation on the Candida membrane.

SIGNIFICANCE

Mo-CBP₃-PepIII has strong potential as a new drug with a broad spectrum of action, given its different mode of action compared to conventional drugs.