Structure-based pharmacophore modeling, virtual screening approaches to identifying the potent hepatitis C viral protease and polymerase novel inhibitors.

Hepatitis C is an infectious disease that leads to acute and chronic liver illnesses. Currently, there are no effective vaccines against this deadly virus. Direct acting antiviral (DAA) drugs are given in the combination with ribavirin and pegylated interferon which lead to adverse effects. Through in silico analysis, the structure-based docking study was performed against NS3/4A protease and NS5B polymerase proteins of HCV. In the current study, multiple e-pharmacophore-based virtual screening methods such as HTVS, SP, and XP were carried out to screen natural compounds and enamine databases. Our result outcomes revealed that CID AE-848/13196185 and CID AE-848/36959205 compounds show good binding interactions with protease protein. In addition, CID 15081408 and CID 173568 show better binding interactions with the polymerase protein. Further to validate the docking results, we performed molecular dynamics simulation for the top hit compounds bound with protease and polymerase proteins to illustrate conformational differences in the stability compared with the active site of the cocrystal inhibitor. Thus, the current study emphasizes these compounds could be an effective drug to treat HCV.

Epitope-based immunoinformatics approach on RNA-dependent RNA polymerase (RdRp) protein complex of Nipah virus (NiV).

Persistent outbreaks of Nipah virus (NiV) with severe case fatality throw a major challenge on researchers to develop a drug or vaccine to combat the disease. With little knowledge of its molecular mechanisms, we utilized the proteome data of NiV to evaluate the potency of three major proteins (phosphoprotein, polymerase, and nucleocapsid protein) in the RNA-dependent RNA polymerase complex to count as a possible candidate for epitope-based vaccine design. Profound computational analysis was used on the above proteins individually to explore the T-cell immune properties like antigenicity, immunogenicity, binding to major histocompatibility complex class I and class II alleles, conservancy, toxicity, and population coverage. Based on these predictions the peptide 'ELRSELIGY' of phosphoprotein and 'YPLLWSFAM' of nulceocapsid protein were identified as the best-predicted T-cell epitopes and molecular docking with human leukocyte antigen-C (HLA-C*12:03) molecule was effectuated followed by validation with molecular dynamics simulation. The B-cell epitope predictions suggest that the sequence positions 421 to 471 in phosphoprotein, 606 to 640 in polymerase and 496 to 517 in nucleocapsid protein are the best-predicted regions for B-cell immune response. However, the further experimental circumstance is required to test and validate the efficacy of the subunit peptide for potential candidacy against NiV.

Pharmacophore feature prediction and molecular docking approach to identify novel anti-HCV protease inhibitors.

Discovering a potential drug for HCV treatment is a challenging task in the field of drug research. This study initiates with computational screening and modeling of promising ligand molecules. The foremost modeling method involves the identification of novel compound and its molecular interaction based on pharmacophore features. A total of 197 HCV compounds for NS3/4A protein target were screened for our study. The pharmacophore models were generated using PHASE module implemented in Schrodinger suite. The pharmacophore features include one hydrogen bond acceptor, one hydrogen bond donor, and three hydrophobic sites. As a result, based on mentioned hypothesis the model ADHHH.159 corresponds to the CID 59533233. Furthermore, docking was performed using maestro for all the 197 compounds. Among these, the CID 59533313 and 59533233 possess the best binding energy of -11.75 and -10.40 kcal/mol, respectively. The interactions studies indicated that the CID complexed with the NS3/4A protein possess better binding affinity with the other compounds. Further the compounds were subjected to calculate the ADME properties. Therefore, it can be concluded that these two compounds could be a potential alternative drug for the development of HCV.

Deciphering molecular properties and docking studies of hepatitis C and non-hepatitis C antiviral inhibitors - A computational approach.

BACKGROUND: Hepatitis C is an infectious liver disease with high mortality rate which is caused by Hepatitis C virus. Several treatment methods have been applied to combat this deadly virus including interferons, vaccine and direct acting antivirals (DAAs). However, the later shows promising effects in HCV treatment with lower adverse effect. Specifically, the DAAs target the non-structural proteins (NS3 and NS5B). PURPOSE: The objective of the present study is to hypothesize an alternative antiviral inhibitor for HCV from the available other antivirals. METHODS: Computation of 2D molecular descriptors for the selected antiviral inhibitors followed by clustering the descriptor features. The closely clustered compounds were subjected to the interaction studies against the HCV target protein to validate the cluster result. RESULTS AND DISCUSSION: The clustering result showed that indinavir (HIV inhibitor) and AT130 (HBV inhibitor) molecule are close to the HCV inhibitor. The indinavir complexed with NS3 protein shows -5.33kcal/mol and AT-130 complexed with NS5B protein possess the binding energy of -8.87kcal/mol. The docking interaction study indicated a better binding affinity than other viral inhibitors. CONCLUSION: From the descriptor based feature similarity analysis and the interaction study, it can be concluded that indinavir and AT-130 could be a potential alternative agent for HCV treatment.

Non-canonical pathway network modelling and ubiquitination site prediction through homology modelling of NF-κB.

Given the fact that NF-κB stays as a dormant molecule in the cytoplasm in steady state, one common step in all the metabolic activities comprising NF-κB is its activation. Consequently there are two pathways of interest related to NF-κB activation: Canonical and alternate. Both the pathways involve ubiquitination of its repressors, that is to say ubiquitination of I-κB by NEMO/IKK-α/IKK-ß complex in case of NF-κB1 and that of p100 by IKK-α homodimer in case of NF-κB2. This paper attempts to figure out the ubiquitination sites in alternate pathway of NF-κB activation using a purely computational approach. We initiated the work by acquiring the genes involved in NF kappa B alternate pathway through Agilent literature search. For this we employed the Cytoscape and STRING database. Secondly, the MSA was built using the sequences obtained through BLAST search, and the results were used to update the original sequence list, which was further refined using HMMER. Structural alignment was achieved via Modeller libraries. The final model has been refined using loop_model and asses_dope functions of Modeller. Ubiquitination site is predicted to be comprised of residues 'SPECLDLLVDS' between sites 178 and 188, both positions inclusive. Unlike the classical pathway, due to absence of parallel studies for p100/RelB, a quality match could not be performed, but future studies are in pipeline to replicate the methodology for NF-κB1 activation and compare the results with existing observations. The study can be used to understand the cofactors involved and ubiquitination sites employed during the activation process during drug designing activities. The methodology can be easily scaled and adapted for classical pathway as well.