Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2.

In viral replication and transcription, the main protease (Mpro) of SARS-CoV-2 plays an important role and appears to be a vital target for drug design. In Mpro, there is a Cys-His catalytic dyad, and ligands that interact with the Cys145 assumed to be an effective approach to inhibit the Mpro. In this study, approximately 1400 cysteine-focused ligands were screened to identify the best candidates that can act as potent inhibitors against Mpro. Our results show that the selected ligands strongly interact with the key Cys145 and His41 residues. Covalent docking was performed for the selected candidates containing the acrylonitrile group, which can form a covalent bond with Cys145. All atoms molecular dynamics (MD) simulation was performed on the selected four inhibitors including L1, L2, L3 and L4 to validate the docking interactions. Our results were also compared with a control ligand, α-ketoamide (11r). Principal component analysis on structural and energy data obtained from the MD trajectories shows that L1, L3, L4 and α-ketoamide (11r) have structural similarity with the apo-form of the Mpro. Quantitative structure-activity relationship method was employed for pattern recognition of the best ligands, which discloses that ligands containing acrylonitrile and amide warheads can show better performance. ADMET analysis displays that our selected candidates appear to be safer inhibitors. Our combined studies suggest that the best cysteine focused ligands can help to design an effective lead drug for COVID-19 treatment. Communicated by Ramaswamy H. Sarma.

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2.

The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2.

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment.

Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.

Antiviral Peptides as Promising Therapeutics against SARS-CoV-2.

Over 50 peptides, which were known to inhibit SARS-CoV-1, were computationally screened against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. Based on the binding affinity and interaction, 15 peptides were selected, which showed higher affinity compared to the α-helix of the human ACE2 receptor. Molecular dynamics simulation demonstrated that two peptides, S2P25 and S2P26, were the most promising candidates, which could potentially block the entry of SARS-CoV-2. Tyr489 and Tyr505 residues present in the "finger-like" projections of the RBD were found to be critical for peptide interaction. Hydrogen bonding and hydrophobic interactions played important roles in prompting peptide-protein binding and interaction. Structure-activity relationship indicated that peptides containing aromatic (Tyr and Phe), nonpolar (Pro, Gly, Leu, and Ala), and polar (Asn, Gln, and Cys) residues were the most significant contributors. These findings can facilitate the rational design of selective peptide inhibitors targeting the spike protein of SARS-CoV-2.

Molecular and Spectroscopic Insights of a Choline Chloride Based Therapeutic Deep Eutectic Solvent.

In this study, atomic level interactions of a 1:1 choline chloride (ChCl)/acetylsalicylic acid (ASA) therapeutic deep eutectic solvent (THDES) has been investigated by combining the molecular dynamics (MD), density functional theory (DFT), and spectroscopic (Raman and IR) techniques. Atom-atom radial distribution functions (RDFs) based on MD simulation reveal that hydrogen bonds are formed between Cl-···HOCh+ and Cl-···HOCOOH of the THDES, where Cl- works as a bridge between ASA and Ch+. Cation-anion electrostatic attractions are disrupted by highly interconnected hydrogen bonds. Cluster conformers of the THDES are isolated from MD simulation and optimized using ωB97XD/6-311++G(d,p) level of theory, in which the strongest H bonds are found among OHCh+···Cl- (2.37 Å) and Cl-···HOCOOH(2.40 Å). Charge transfer calculations, using CHEPLG and NBO analysis, disclose that the charge of Cl- is reduced in the cluster structures and transferred to Ch+ and ASA. Further analyses are conducted using experimental and computed spectroscopic data. These confirm the formation of the THDES as peaks for -COOH, -COOR, and -OH functional groups of ASA and ChCl are either get broadened or disappeared in the spectra of the cluster conformers. Moreover, principal component analysis (PCA) assists to understand the feature of the simulated data and confirms the formation of the THDES. Solvent selectivity triangle (SST) of solvatochromic parameters also demonstrate that this THDES has some important properties similar to ionic liquids and common deep eutectic solvent.

Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models.

Near infrared spectroscopy (NIRS) is often used during the tablet coating process to assess coating thickness. As the coating process proceeds, the increase and decrease in NIRS signal from both the coating formulation and tablet core has been related to coating thickness. Partial least-squares models are often generated relating NIRS spectra to reference coating thickness measurements for in-line and/or at-line monitoring of the coating process. This study investigated the effect of the reference coating thickness measurements on the accuracy of the model. The two primary reference techniques used were weight gain-based coating thickness and terahertz-based coating thickness. Most NIRS coating thickness models currently use weight gain-based reference values; however, terahertz-time-of-flight spectroscopy (THz-TOF) offers a more direct reference coating thickness measurement. Results showed that the accuracy of the NIRS coating thickness model significantly improved when terahertz-based coating thickness measurements were used as reference when compared to weight gain-based coating thickness measurements. Therefore, the application of THz-TOF as a reference method is further demonstrated.

Prediction of Deleterious Non-synonymous SNPs of Human STK11 Gene by Combining Algorithms, Molecular Docking, and Molecular Dynamics Simulation.

Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz-Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

Determining the effect of photodegradation on film coated nifedipine tablets with terahertz based coating thickness measurements.

Film coating of nifedipine tablets is commonly performed to reduce photo-degradation. The coating thickness of these tablets is a primary dictating factor of photo-stability. Terahertz spectroscopy enables accurate measurement of coating thickness. This study identifies a method to determine an end-point of a photo-protective coating process by using coating thickness measurements from terahertz time of flight spectroscopy (THz-TOF). For this method, nifedipine tablets, at different coating thicknesses, were placed in a photostability chamber. The illumination conditions of the coated tablets were adjusted based on the time duration of these tablets inside the chamber. A multiple linear regression model was developed with the coating thickness estimates from THz-TOF and illumination conditions information to predict the amount of drug remaining after photo-degradation (percent label claim). The prediction error of this model was 1.03% label claim in the range of 88.4-100.6% label claim. According to this model, acceptable levels of photo-protection in illumination conditions of up to approximately 700,000 lx hours was achieved at the end of the coating process (approximately 50 µm coating thickness) performed in this study. These results suggest THz-TOF as a viable process analytical technology tool for process understanding and end-point determination of a photo-protective coating process.

Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses.

To develop a robust quantitative calibration model for spectroscopy, different sources of variability that are not directly related to the components of interest should be included in the calibration samples; this variability should be similar to that which is anticipated during validation and routine operation. Moisture content of pharmaceutical samples can vary as a function of supplier, storage conditions, geographic origin or seasonal variation. Additionally, some pharmaceutical operations (e. g., wet granulation) cause exposure of excipients and API to water. Although water is a weak Raman scatterer, moisture variability has an indirect effect on analytical model performance. Because many pharmaceutical components have intrinsic fluorescent characteristics (with broad spectral features), moisture variability may cause spectral artifacts in the form of baseline variation associated with fluorescence quenching. This work investigates the deleterious effects of water quenching on quantitative prediction accuracy of a multivariate calibration algorithm for Raman spectroscopy. To demonstrate this, a formulation composed of acetaminophen, lactose, microcrystalline cellulose, HPMC and magnesium stearate was used. Tablets were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (5 levels), and excipient ratios (3 levels) to generate tablets for calibration and testing. Tablet moisture variation was introduced by placing samples in different humidity chambers. Significant spectral effects arising from fluorescence were identified in the Raman spectra and due to moisture variation: the fluorescence related spectral variability caused substantial degradation of the prediction performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model.

A robust quantitative near infrared modeling approach for blend monitoring.

This study demonstrates a material sparing Near-Infrared modeling approach for powder blend monitoring. In this new approach, gram scale powder mixtures are subjected to compression loads to simulate the effect of scale using an Instron universal testing system. Models prepared by the new method development approach (small-scale method) and by a traditional method development (blender-scale method) were compared by simultaneously monitoring a 1kg batch size blend run. Both models demonstrated similar model performance. The small-scale method strategy significantly reduces the total resources expended to develop Near-Infrared calibration models for on-line blend monitoring. Further, this development approach does not require the actual equipment (i.e., blender) to which the method will be applied, only a similar optical interface. Thus, a robust on-line blend monitoring method can be fully developed before any large-scale blending experiment is viable, allowing the blend method to be used during scale-up and blend development trials.