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1.
J Comput Aided Mol Des ; 37(8): 339-355, 2023 08.
Article in English | MEDLINE | ID: covidwho-20244179

ABSTRACT

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Protease Inhibitors/pharmacology , Antiviral Agents/pharmacology , Antiviral Agents/chemistry
2.
Comput Biol Med ; 161: 107004, 2023 07.
Article in English | MEDLINE | ID: covidwho-20243025

ABSTRACT

BACKGROUND: Human neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. METHOD: AutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85-8501 and the highly active BAY-8040 were calculated with sc and tcHNE. RESULTS: The DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1'-S2' subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85-8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85-8501 is likely to be inactive. CONCLUSION: SAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.


Subject(s)
Leukocyte Elastase , Pyrimidinones , Humans , Leukocyte Elastase/chemistry , Leukocyte Elastase/metabolism , Sulfones , Molecular Dynamics Simulation , Quantitative Structure-Activity Relationship , Molecular Docking Simulation
3.
Molecules ; 28(11)2023 May 24.
Article in English | MEDLINE | ID: covidwho-20238192

ABSTRACT

Essential oils (Eos) have demonstrated antiviral activity, but their toxicity can hinder their use as therapeutic agents. Recently, some essential oil components have been used within safe levels of acceptable daily intake limits without causing toxicity. The "ImmunoDefender," a novel antiviral compound made from a well-known mixture of essential oils, is considered highly effective in treating SARS-CoV-2 infections. The components and doses were chosen based on existing information about their structure and toxicity. Blocking the main protease (Mpro) of SARS-CoV-2 with high affinity and capacity is critical for inhibiting the virus's pathogenesis and transmission. In silico studies were conducted to examine the molecular interactions between the main essential oil components in "ImmunoDefender" and SARS-CoV-2 Mpro. The screening results showed that six key components of ImmunoDefender formed stable complexes with Mpro via its active catalytic site with binding energies ranging from -8.75 to -10.30 kcal/mol, respectively for Cinnamtannin B1, Cinnamtannin B2, Pavetannin C1, Syzyginin B, Procyanidin C1, and Tenuifolin. Furthermore, three essential oil bioactive inhibitors, Cinnamtannin B1, Cinnamtannin B2, and Pavetannin C, had significant ability to bind to the allosteric site of the main protease with binding energies of -11.12, -10.74, and -10.79 kcal/mol; these results suggest that these essential oil bioactive compounds may play a role in preventing the attachment of the translated polyprotein to Mpro, inhibiting the virus's pathogenesis and transmission. These components also had drug-like characteristics similar to approved and effective drugs, suggesting that further pre-clinical and clinical studies are needed to confirm the generated in silico outcomes.


Subject(s)
COVID-19 , Oils, Volatile , Humans , SARS-CoV-2 , Antiviral Agents/chemistry , Oils, Volatile/pharmacology , Molecular Docking Simulation , Protease Inhibitors/chemistry , Viral Nonstructural Proteins/metabolism , Peptide Hydrolases/metabolism , Molecular Dynamics Simulation
4.
Pak J Biol Sci ; 26(2): 81-90, 2023 Jan.
Article in English | MEDLINE | ID: covidwho-20236999

ABSTRACT

<b>Background and Objective:</b> The COVID-19, which has been circulating since late 2019, is caused by SARS-CoV-2. Because of its high infectivity, this virus has spread widely throughout the world. Spike glycoprotein is one of the proteins found in SARS-CoV-2. Spike glycoproteins directly affect infection by forming ACE-2 receptors on host cells. Inhibiting glycoprotein spikes could be one method of treating COVID-19. In this study, the antivirus marketed as a database will be repurposed into an antiviral SARS-CoV-2 and the selected compounds will be modified to become organoselenium compounds. <b>Materials and Methods:</b> The research was carried out using <i>in silico</i> methods, such as rigid docking and flexible docking. To obtain information about the interaction between spike glycoprotein and ligands, MOE 2014.09 was used to perform the molecular docking simulation. <b>Results:</b> The analysis of binding energy values was used to select the ten best ligands from the first stage of the molecular docking simulation, which was then modified according to the previous QSAR study to produce 96 new molecules. The second stage of molecular docking simulation was performed with modified molecules. The best-modified ligand was chosen by analyzing the ADME-Tox property, RMSD value and binding energy value. <b>Conclusion:</b> The best three unmodified ligands, Ombitasvir, Elbasvir and Ledipasvir, have a binding energy value of -15.8065, -15.3842 and -15.1255 kcal mol<sup>1</sup>, respectively and the best three modified ligands ModL1, ModL2 and ModL3 has a binding value of -15.6716, -13.9489 and -13.2951 kcal mol<sup>1</sup>, respectively with an RMSD value of 1.7109 Å, 2.3179 Å and 1.7836 Å.


Subject(s)
COVID-19 , Organoselenium Compounds , Humans , SARS-CoV-2 , Ligands , Molecular Docking Simulation , Antiviral Agents/pharmacology , Molecular Dynamics Simulation
5.
J Am Chem Soc ; 145(24): 13204-13214, 2023 06 21.
Article in English | MEDLINE | ID: covidwho-20236265

ABSTRACT

We report the results of computational modeling of the reactions of the SARS-CoV-2 main protease (MPro) with four potential covalent inhibitors. Two of them, carmofur and nirmatrelvir, have shown experimentally the ability to inhibit MPro. Two other compounds, X77A and X77C, were designed computationally in this work. They were derived from the structure of X77, a non-covalent inhibitor forming a tight surface complex with MPro. We modified the X77 structure by introducing warheads capable of reacting with the catalytic cysteine residue in the MPro active site. The reaction mechanisms of the four molecules with MPro were investigated by quantum mechanics/molecular mechanics (QM/MM) simulations. The results show that all four compounds form covalent adducts with the catalytic cysteine Cys 145 of MPro. From the chemical perspective, the reactions of these four molecules with MPro follow three distinct mechanisms. The reactions are initiated by a nucleophilic attack of the thiolate group of the deprotonated cysteine residue from the catalytic dyad Cys145-His41 of MPro. In the case of carmofur and X77A, the covalent binding of the thiolate to the ligand is accompanied by the formation of the fluoro-uracil leaving group. The reaction with X77C follows the nucleophilic aromatic substitution SNAr mechanism. The reaction of MPro with nirmatrelvir (which has a reactive nitrile group) leads to the formation of a covalent thioimidate adduct with the thiolate of the Cys145 residue in the enzyme active site. Our results contribute to the ongoing search for efficient inhibitors of the SARS-CoV-2 enzymes.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Cysteine , Molecular Dynamics Simulation , Protease Inhibitors/pharmacology , Protease Inhibitors/chemistry , Antiviral Agents/pharmacology , Molecular Docking Simulation
6.
J Chem Phys ; 158(21)2023 Jun 07.
Article in English | MEDLINE | ID: covidwho-20235913

ABSTRACT

We present a hybrid, multi-method, computational scheme for protein/ligand systems well suited to be used on modern and forthcoming massively parallel computing systems. The scheme relies on a multi-scale polarizable molecular modeling, approach to perform molecular dynamics simulations, and on an efficient Density Functional Theory (DFT) linear scaling method to post-process simulation snapshots. We use this scheme to investigate recent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2 virus. We assessed the reliability and the coherence of the hybrid scheme, in particular, by checking the ability of MM and DFT to reproduce results from high-end ab initio computations regarding such inhibitors. The DFT approach enables an a posteriori fragmentation of the system and an investigation into the strength of interaction among identified fragment pairs. We show the necessity of accounting for a large set of plausible protease/inhibitor conformations to generate reliable interaction data. Finally, we point out ways to further improve α-ketoamide inhibitors to more strongly interact with particular protease domains neighboring the active site.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Ligands , Reproducibility of Results , Protease Inhibitors/pharmacology , Protease Inhibitors/chemistry , Coronavirus 3C Proteases , Molecular Dynamics Simulation , Catalytic Domain , Molecular Docking Simulation
7.
J Chem Theory Comput ; 19(11): 3359-3378, 2023 Jun 13.
Article in English | MEDLINE | ID: covidwho-20233230

ABSTRACT

We subject a series of five protein-ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 µs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10 µs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study.


Subject(s)
COVID-19 , Molecular Dynamics Simulation , Humans , SARS-CoV-2 , Ligands , Binding Sites , Proteins/chemistry , Molecular Docking Simulation
8.
Int J Mol Sci ; 24(10)2023 May 16.
Article in English | MEDLINE | ID: covidwho-20232996

ABSTRACT

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of -8.645 kcal·mol-1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC50 values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Molecular Docking Simulation , Angiotensin-Converting Enzyme 2 , Ligands , Molecular Dynamics Simulation , Antiviral Agents/pharmacology , Antiviral Agents/chemistry , Protein Binding
9.
J Chem Inf Model ; 63(11): 3601-3613, 2023 06 12.
Article in English | MEDLINE | ID: covidwho-20232259

ABSTRACT

The SARS-CoV-2 main protease (Mpro) is a crucial enzyme for viral replication and has been considered an attractive drug target for the treatment of COVID-19. In this study, virtual screening techniques and in vitro assays were combined to identify novel Mpro inhibitors starting from around 8000 FDA-approved drugs. The docking analysis highlighted 17 promising best hits, biologically characterized in terms of their Mpro inhibitory activity. Among them, 7 cephalosporins and the oral anticoagulant betrixaban were able to block the enzyme activity in the micromolar range with no cytotoxic effect at the highest concentration tested. After the evaluation of the degree of conservation of Mpro residues involved in the binding with the studied ligands, the ligands' activity on SARS-CoV-2 replication was assessed. The ability of betrixaban to affect SARS-CoV-2 replication associated to its antithrombotic effect could pave the way for its possible use in the treatment of hospitalized COVID-19 patients.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Antiviral Agents/pharmacology , Antiviral Agents/chemistry , Drug Repositioning , Ligands , Protease Inhibitors/pharmacology , Protease Inhibitors/chemistry , Molecular Docking Simulation , Molecular Dynamics Simulation
10.
Int J Biol Macromol ; 244: 125096, 2023 Jul 31.
Article in English | MEDLINE | ID: covidwho-20231041

ABSTRACT

Baricitinib is a Janus Kinase (JAK) inhibitor that is primarily used to treat moderately to severely active rheumatoid arthritis in adults and has recently been reported for the treatment of patients with severe COVID-19. This paper describes the investigation of the binding behavior of baricitinib to human α1-acid glycoprotein (HAG) employing a variety of spectroscopic techniques, molecular docking and dynamics simulations. Baricitinib can quench the fluorescence from amino acids in HAG through a mix of dynamic and static quenching, according to steady-state fluorescence and UV spectra observations, but it is mainly static quenching at low concentration. The binding constant (Kb) of baricitinib to HAG at 298 K was at the level of 104 M-1, indicating a moderate affinity of baricitinib to HAG. Hydrogen bonding and hydrophobic interactions conducted the main effect, according to thermodynamic characteristics, competition studies between ANS and sucrose, and molecular dynamics simulations. For the change in HAG conformation, the results of multiple spectra showed that baricitinib was able to alter the secondary structure of HAG as well as increase the polarity of the microenvironment around the Trp amino acid. Furthermore, the binding behavior of baricitinib to HAG was investigated by molecular docking and molecular dynamics simulations, which validated experimental results. Also explored is the influence of K+, Co2+, Ni2+, Ca2+, Fe3+, Zn2+, Mg2+ and Cu2+plasma on binding affinity.


Subject(s)
COVID-19 , Janus Kinase Inhibitors , Humans , Molecular Docking Simulation , Protein Binding , Orosomucoid/chemistry , COVID-19 Drug Treatment , Molecular Dynamics Simulation , Protein Structure, Secondary , Thermodynamics , Binding Sites , Spectrometry, Fluorescence
11.
Int J Biol Macromol ; 242(Pt 4): 125153, 2023 Jul 01.
Article in English | MEDLINE | ID: covidwho-20230938

ABSTRACT

The SARS-CoV-2 spike protein (S) represents an important viral component that is required for successful viral infection in humans owing to its essential role in recognition of and entry to host cells. The spike is also an appealing target for drug designers who develop vaccines and antivirals. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. MD simulations found that the higher affinity of SARS-CoV-2-S to ACE2 is linked to its unique residues that add extra electrostatic and van der Waal interactions in comparison to the SARS-CoV S. This illustrates the spread potential of the pandemic SARS-CoV-2 relative to the epidemic SARS-CoV. Different mutations at the S-ACE2 interface, which is believed to increase the transmission of the new variants, affected the behavior and binding interactions in different simulations. The contributions of glycans to the opening of S were revealed via simulations. The immune evasion of S was linked to the spatial distribution of glycans. This help the virus to escape the immune system recognition. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. This will pave the way to us preparing for the next pandemic as the computational tools are tailored to help fight new challenges.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Molecular Dynamics Simulation , Protein Binding , Angiotensin-Converting Enzyme 2/chemistry , Polysaccharides
12.
Comput Biol Med ; 162: 107116, 2023 Aug.
Article in English | MEDLINE | ID: covidwho-20230879

ABSTRACT

The re-emergence of monkeypox (MPX), in the era of COVID-19 pandemic is a new global menace. Regardless of its leniency, there are chances of MPX expediting severe health deterioration. The role of envelope protein, F13 as a critical component for production of extracellular viral particles makes it a crucial drug target. Polyphenols, exhibiting antiviral properties have been acclaimed as an effective alternative to the traditional treatment methods for management of viral diseases. To facilitate the development of potent MPX specific therapeutics, herein, we have employed state-of-the-art machine learning techniques to predict a highly accurate 3-dimensional structure of F13 as well as identify binding hotspots on the protein surface. Additionally, we have effectuated high-throughput virtual screening methodology on 57 potent natural polyphenols having antiviral activities followed by all-atoms molecular dynamics (MD) simulations, to substantiate the mode of interaction of F13 protein and polyphenol complexes. The structure-based virtual screening based on Glide SP, XP and MM/GBSA scores enables the selection of six potent polyphenols having higher binding affinity towards F13. Non-bonded contact analysis, of pre- and post- MD complexes propound the critical role of Glu143, Asp134, Asn345, Ser321 and Tyr320 residues in polyphenol recognition, which is well supported by per-residue decomposition analysis. Close-observation of the structural ensembles from MD suggests that the binding groove of F13 is mostly hydrophobic in nature. Taken together, this structure-based analysis from our study provides a lead on Myricetin, and Demethoxycurcumin, which may act as potent inhibitors of F13. In conclusion, our study provides new insights into the molecular recognition and dynamics of F13-polyphenol bound states, offering new promises for development of antivirals to combat monkeypox. However, further in vitro and in vivo experiments are necessary to validate these results.


Subject(s)
COVID-19 , Monkeypox , Humans , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Antiviral Agents/chemistry , Molecular Dynamics Simulation , Polyphenols , Pandemics , Molecular Docking Simulation
13.
J Chem Inf Model ; 63(11): 3404-3422, 2023 06 12.
Article in English | MEDLINE | ID: covidwho-2326028

ABSTRACT

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.


Subject(s)
COVID-19 , Molecular Dynamics Simulation , Humans , Molecular Docking Simulation , SARS-CoV-2/metabolism , Ligands , COVID-19 Drug Treatment , Protease Inhibitors/chemistry
14.
J Mol Model ; 29(6): 183, 2023 May 22.
Article in English | MEDLINE | ID: covidwho-2325832

ABSTRACT

CONTEXT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 infection and responsible for millions of victims worldwide, remains a significant threat to public health. Even after the development of vaccines, research interest in the emergence of new variants is still prominent. Currently, the focus is on the search for effective and safe drugs, given the limitations and side effects observed for the synthetic drugs administered so far. In this sense, bioactive natural products that are widely used in the pharmaceutical industry due to their effectiveness and low toxicity have emerged as potential options in the search for safe drugs against COVID-19. Following this line, we screened 10 bioactive compounds derived from cholesterol for molecules capable of interacting with the receptor-binding domain (RBD) of the spike protein from SARS-CoV-2 (SC2Spike), responsible for the virus's invasion of human cells. Rounds of docking followed by molecular dynamics simulations and binding energy calculations enabled the selection of three compounds worth being experimentally evaluated against SARS-CoV-2. METHODS: The 3D structures of the cholesterol derivatives were prepared and optimized using the Spartan 08 software with the semi-empirical method PM3. They were then exported to the Molegro Virtual Docking (MVD®) software, where they were docked onto the RBD of a 3D structure of the SC2Spike protein that was imported from the Protein Data Bank (PDB). The best poses obtained from MVD® were subjected to rounds of molecular dynamics simulations using the GROMACS software, with the OPLS/AA force field. Frames from the MD simulation trajectories were used to calculate the ligand's free binding energies using the molecular mechanics - Poisson-Boltzmann surface area (MM-PBSA) method. All results were analyzed using the xmgrace and Visual Molecular Dynamics (VMD) software.


Subject(s)
Biological Products , COVID-19 , Humans , SARS-CoV-2 , Biological Products/pharmacology , Molecular Dynamics Simulation , Databases, Protein , Molecular Docking Simulation , Antiviral Agents/pharmacology
15.
J Phys Chem B ; 127(20): 4396-4405, 2023 05 25.
Article in English | MEDLINE | ID: covidwho-2324522

ABSTRACT

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is considered as a key target for the design and development of COVID-19 drugs and inhibitors. Due to their unique structure and properties, ionic liquids (ILs) have many special interactions with proteins, showing great potential in biomedicine. Nevertheless, few research studies have been carried out on ILs and the spike RBD protein. Here, we explore the interaction of ILs and the RBD protein through large-scale molecular dynamics simulations (4 µs in total). It was found that IL cations with long alkyl chain lengths (nchain) could spontaneously bind to the cavity region of the RBD protein. The longer the alkyl chain is, the stabler the cations bind to the protein. The binding free energy (ΔG) had the same trend, peaking at nchain = 12 with -101.19 kJ/mol. The cationic chain lengths and their fit to the pocket are decisive factors that influence the binding strength of cations and proteins. The cationic imidazole ring has a high contact frequency with phenylalanine and tryptophan, and the hydrophobic residues phenylalanine, valine, leucine, and isoleucine are the most interacting residues with side chains of cations. Meanwhile, through analysis of the interaction energy, the hydrophobic and π-π interactions are the main contributors to the high affinity between cations and the RBD protein. In addition, the long-chain ILs would also act on the protein through clustering. These studies not only provide insights into the molecular interaction between ILs and the RBD of SARS-CoV-2 but also contribute to the rational design of IL-based drugs, drug carriers, and selective inhibitors as a therapeutic for SARS-CoV-2.


Subject(s)
COVID-19 , Ionic Liquids , Humans , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Ionic Liquids/chemistry , Molecular Dynamics Simulation , Protein Binding , Cations , Phenylalanine/metabolism
16.
J Enzyme Inhib Med Chem ; 38(1): 2212327, 2023 Dec.
Article in English | MEDLINE | ID: covidwho-2323671

ABSTRACT

Both receptor-binding domain in spike protein (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human neuropilin-1 (NRP1) are important in the virus entry, and their concomitant inhibition may become a potential strategy against the SARS-CoV-2 infection. Herein, five novel dual S-RBD/NRP1-targeting peptides with nanomolar binding affinities were identified by structure-based virtual screening. Particularly, RN-4 was found to be the most promising peptide targeting S-RBD (Kd = 7.4 ± 0.5 nM) and NRP1-BD (the b1 domain of NRP1) (Kd = 16.1 ± 1.1 nM) proteins. Further evidence in the pseudovirus infection assay showed that RN-4 can significantly inhibit the SARS-CoV-2 pseudovirus entry into 293 T cells (EC50 = 0.39 ± 0.09 µM) without detectable side effects. These results suggest that RN-4, a novel dual S-RBD/NRP1-targeting agent, holds potential as an effective therapeutic to combat the SARS-CoV-2 infection.


Subject(s)
COVID-19 , Molecular Dynamics Simulation , Humans , SARS-CoV-2 , Neuropilin-1 , Peptides/pharmacology , Protein Binding
17.
J Chem Inf Model ; 63(11): 3438-3447, 2023 06 12.
Article in English | MEDLINE | ID: covidwho-2323668

ABSTRACT

A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called "tinyIFD". The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.


Subject(s)
COVID-19 , Humans , Ligands , Workflow , Molecular Docking Simulation , SARS-CoV-2 , Protease Inhibitors/chemistry , Molecular Dynamics Simulation
18.
J Chem Inf Model ; 63(11): 3521-3533, 2023 06 12.
Article in English | MEDLINE | ID: covidwho-2322490

ABSTRACT

Nirmatrelvir is an orally available inhibitor of SARS-CoV-2 main protease (Mpro) and the main ingredient of Paxlovid, a drug approved by the U.S. Food and Drug Administration for high-risk COVID-19 patients. Recently, a rare natural mutation, H172Y, was found to significantly reduce nirmatrelvir's inhibitory activity. As the COVID-19 cases skyrocket in China and the selective pressure of antiviral therapy builds in the US, there is an urgent need to characterize and understand how the H172Y mutation confers drug resistance. Here, we investigated the H172Y Mpro's conformational dynamics, folding stability, catalytic efficiency, and inhibitory activity using all-atom constant pH and fixed-charge molecular dynamics simulations, alchemical and empirical free energy calculations, artificial neural networks, and biochemical experiments. Our data suggest that the mutation significantly weakens the S1 pocket interactions with the N-terminus and perturbs the conformation of the oxyanion loop, leading to a decrease in the thermal stability and catalytic efficiency. Importantly, the perturbed S1 pocket dynamics weaken the nirmatrelvir binding in the P1 position, which explains the decreased inhibitory activity of nirmatrelvir. Our work demonstrates the predictive power of the combined simulation and artificial intelligence approaches, and together with biochemical experiments, they can be used to actively surveil continually emerging mutations of SARS-CoV-2 Mpro and assist the optimization of antiviral drugs. The presented approach, in general, can be applied to characterize mutation effects on any protein drug targets.


Subject(s)
COVID-19 , Humans , SARS-CoV-2/genetics , SARS-CoV-2/metabolism , Artificial Intelligence , Protease Inhibitors/chemistry , Antiviral Agents/chemistry , Molecular Dynamics Simulation , Mutation , Drug Resistance , Molecular Docking Simulation
19.
Phys Chem Chem Phys ; 25(18): 12882-12890, 2023 May 10.
Article in English | MEDLINE | ID: covidwho-2316511

ABSTRACT

The global spread of the new coronavirus COVID-19 has seriously affected human health and has caused a large number of deaths. Using molecular dynamics (MD) simulations to study the microscopic dynamic behavior of the virion provides an important means to study the pathogenic mechanism. In this work, we develop an ultra-coarse-grained (UCG) model of the SARS-CoV-2 virion from the authentic cryo-electron microscopy data, which enables MD simulation of the entire virion within microseconds. In addition, a hybrid all-atom and UCG (AA/UCG) virion model involving an all-atom spike protein is developed for the investigation of the spike protein interactions. A comparison of the conformational changes for the spike proteins as simulated in the hybrid model and that isolated in solution as in the free form reveals that the former is completely different from the latter. The simulation results demonstrate the necessity for the development of multiscale models to study the functions of proteins in the biomolecular complexes.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/metabolism , Cryoelectron Microscopy , Spike Glycoprotein, Coronavirus/metabolism , Molecular Dynamics Simulation , Virion/metabolism , Virion/ultrastructure
20.
Biomed Res Int ; 2023: 5469258, 2023.
Article in English | MEDLINE | ID: covidwho-2315143

ABSTRACT

SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3 (Nonstructural protein 3) and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/metabolism , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Antiviral Agents/chemistry , Molecular Docking Simulation , Pandemics , Ligands , Angiotensin-Converting Enzyme 2/metabolism , Viral Nonstructural Proteins/chemistry , Molecular Dynamics Simulation , Phytochemicals/pharmacology , Phytochemicals/therapeutic use
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