Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.

Physicochemical characterization of paclitaxel prodrugs with cytochrome 3A4 to correlate solubility and bioavailability implementing molecular docking and simulation studies.

Prodrugs are biologically inactive drug molecules that may be developed through rational drug design with an objective to improve a drug's pharmaceutical and pharmacokinetic properties. Paclitaxel, a highly potent anticancer drug, is directed against many cancers like breast cancer, ovarian cancer, lung cancer, head and neck tumors, non-small cell lung cancer, and Kaposi's sarcoma, etc. Along with its excellent antitumor activity the drug had a major limitation of low water solubility. To overcome this limitation of this nanomolar active drug many prodrugs were formed in the past. Though increase in the solubility of the drug was obtained but that may or may not account for its increase in bioavailability. CYP3A4 liver enzymes are responsible for the metabolism of fifty percent of the drugs and are major metabolizing enzyme for paclitaxel. Phosphate prodrugs are well known to account the insolubility of many drugs and thus increasing their bioavailability also. In this study, we calculated the ADMET properties of a dataset of twenty phosphate prodrugs of paclitaxel. On the basis of reflection of three favourable properties, ten prodrugs were chosen for further docking studies against CYP3A4. Finally, three prodrugs showing unfavourable binding affinities were selected for Molecular Dynamics Simulations and from this in-silico study we identified that all the three selected prodrugs were unstable as compared to the paclitaxel. The instability of these prodrugs showed their lesser interaction with the CYP3A4 and hence contributing more towards its bioavailability. Thus the three suggested prodrugs those were studied in-silico for oral bioavailability can be further validated for gastrointestinal cancer.Communicated by Ramaswamy H. Sarma.

A pathway map of signaling events triggered upon SARS-CoV infection.

Severe acute respiratory syndrome coronaviruses (SARS-CoVs) caused worldwide epidemics over the past few decades. Extensive studies on various strains of coronaviruses provided a basic understanding of the pathogenesis of the disease. Presently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is leading a global pandemic with unprecedented challenges. This is the third coronavirus outbreak of this century. A signaling pathway map of signaling events induced by SARS-CoV infection is not yet available. In this study, we present a literature-annotated signaling pathway map of reactions induced by SARS-CoV infected cells. Multiple signaling modules were found to be orchestrated including PI3K-AKT, Ras-MAPK, JAK-STAT, Type 1 IFN and NFκB. The signaling pathway map of SARS-CoV consists of 110 molecules and 101 reactions mediated by SARS-CoV proteins. The pathway reaction data are available in various community standard data exchange formats including Systems Biology Graphical Notation (SBGN). The pathway map is publicly available through the GitHub repository and data in various formats can be freely downloadable.

Structurally modified compounds of hydroxychloroquine, remdesivir and tetrahydrocannabinol against main protease of SARS-CoV-2, a possible hope for COVID-19: Docking and molecular dynamics simulation studies.

Now a days, more than 200 countries faces the health crisis due to epidemiological disease COVID-19 caused by SARS-CoV-2 virus. It will cause a very high impact on world's economy and global health sector. Earlier the structure of main protease (Mpro) protein was deposited in the RCSB protein repository. Hydroxychloroquine (HCQ) and remdesivir were found to effective in treatment of COVID-19 patients. Here we have performed docking and molecule dynamic (MD) simulation study of HCQ and remdesivir with Mpro protein which gave promising results to inhibit Mpro protein in SARS-CoV-2. On the basis of results obtained we designed structurally modified 18 novel derivatives of HCQ, remdesivir and tetrahydrocannabinol (THC) and carried out docking studies of all the derivatives. From the docking studies six molecules DK4, DK7, DK10, DK16, DK17 and DK19 gave promising results and can be use as inhibitor for Mpro of SARS-CoV-2 to control COVID-19 very effectively. Further, molecular dynamics simulation of one derivative of HCQ and one derivative of tetrahydrocannabinol showing excellent docking score was performed along with the respective parent molecules. The two derivatives gave excellent docking score and higher stability than the parent molecule as validated with molecular dynamics (MD) simulation for the binding affinities towards Mpro of SARS-CoV-2 thus represented as strong inhibitors at very low concentration.

Identification of novel small molecules against GSK3ß for Alzheimer's disease using chemoinformatics approach.

Alzheimer's disease is a rapidly increasing neurodegenerative disease. It is a multifactorial disease and also a global threat. Several enzymes are implicated in the disease in which Glycogen Synthase Kinase 3 beta is a key enzyme to increase the disease progression by the hyperphosphorylation of the tau protein. We have used an integrative chemoinformatics and pharmacokinetics approach for the identification of novel small molecules. We have retrieved a subset from the ZINC database (n = 5,36,709) and screened against GSK3ß in four steps. From here top 298 potent compounds were selected and employed for their pharmacokinetics analysis. We had seen that 29 compounds showed the key characteristics to be a novel drug candidate therefore, all these compounds were employed for redocking studies using Autodock Vina and Autodock. This analysis revealed that four compounds were showing good binding affinity. All these four compounds were employed for MDS analysis of 100 ns From here using a bunch of MD analyses we have found that out of four compounds GSK3ß-ZINC21011059 and GSK3ß-ZINC21011066 act as a stable protein-ligand complex. Therefore we proposed ZINC21011059 and ZINC21011066 can serve as a novel compounds against GSK3ß and predicted scaffold can be used for further optimization towards the improvement of isoform selectivity, and warranting further investigations towards their in vitro and in vivo validation of the bioactivity.

Development of QSPR Strategy for the Solubility Prediction.

INTRODUCTION: QSPR modelling is one of the major computational tools used to correlate molecular characteristics with physiochemical properties of molecules. In present work, QSPR models are formed using AIC and VIF multicollinearity indicators for descriptors selection taking solubility data of Paclitaxel prodrugs. Geometry optimization of these Paclitaxel prodrugs was performed at the PM6 and AM1levels using Gaussian software. METHODS: Four descriptor groups such as 2D Autocorrelation, CATS_3D, WHIM, GETAWAY provided initial QSPR models with moderate accuracy for both the optimized geometry datasets. The descriptors from two descriptor-groups which were showing reasonable correlation (Q2) were combined to form improved models. The selection of descriptors was performed in multiple steps to determine optimal models which contain five and four descriptors for PM6 and AM1 optimized geometry datasets respectively. The R2 & Q2 values are 0.86 & 0.83 and 0.87 & 0.86 for PM6 and AM1 geometries respectively. RESULTS: The models formed shows comparable results with the earlier reported results. The proposed protocol is also implemented on Huuskonen small dataset and the final QSPR model contains only two descriptors. On this smaller dataset, the QSPR model gives the R2 and Q2 values 0.87 and 0.85, respectively which is comparable to the results of Paclitaxel prodrugs. CONCLUSION: Our approach can be applicable to different datasets and it can assist the synthesis of molecule with better solubility. These QSPR models can be implemented for predicting the aqueous solubility of unknown Paclitaxel prodrugs.