Identification of novel anti cancer agents by applying insilico methods for inhibition of TSPO protein

Cancer is a genomic disease characterised as impaired cellular energy metabolism. Cancer cells derive most of their energy from oxidative phosphorylation unlike normal ones during cell progression TSPO protein present in external mitochondrial membrane, is involved in various cellular functions like Cell proliferation, mitochondrial respiration, synthesis of steroids and also participates in import of cholesterol into the inner mitochondrial membrane from outside of the membrane of mitochondria.The 3D model of TSPO protein is built using comparative homology modelling techniques and validated by proSA, Ramachandran plot and ERRAT in the present work. Active site prediction is carried out using SiteMap and literature, which allows the prediction of the important binding pockets for the identification of putative active site. New molecular entities as TSPO inhibitors were obtained from Virtual screening using MS Spectrum databank in Schrodinger suite and were prioritised based on Glide Score. Docking was performed using Autodock to identify molecules with different scaffolds and were prioritised based on binding energy and RMSD values. Qikprop is used to calculate pharmacokinetic properties of the screened molecules which are found to be in permissible range as possible novel inhibitors of TSPO protein to supress cell proliferation.     

Deciphering potential inhibitors targeting THI4 of Fusarium solani sp. to combat fungal keratitis: An integrative computational approach

Mycotic keratitis is a fungal infection of corneal epithelium. It is more common in tropical and subtropical countries and is one of the leading causes of blindness. Many of the antifungal drugs have been less effective in treating this condition and certain drugs which are efficient and yet limited in use due to its extreme side effects. Hence, in this study an attempt is made to identify potential and least toxic antifungal inhibitors that targets thiamine thiazole synthase, a novel target for suppressing Fusarium solani subsp.pisi (Nectria haematococca MPVI) infections, to combat mycotic keratitis. Integrative computational approaches involving model refinement, molecular dynamics simulation and High throughput virtual screening (HTVS) were applied through integrative multi precision mode in order to identify potential inhibitors. Moreover, machine learning approach was also implemented to prioritize potential inhibitors that are ophthalmic adaptive, as well as antifungal molecule. From the NCI and Maybridge datasets, for HTVS only 209,872 compounds that surpassed ligand property filtration were considered, which resulted in 209 compounds after XP docking. Among the top 5 compounds from XP docking, on cumulative analysis only 2-(1-hydroxyethyl)-1,3-thiazole-4-carboxamide was prioritized as the most potential hit, as it showed higher order of significance in terms of binding affinity, structural stability and therapeutic relevance for the treatment of Mycotic keratitis. Thus, widening the scope for novel antifungal therapy in ophthalmic infections.     

Molecular Basis of Inhibitory Mechanism of Naltrexone and Its Metabolites through Structural and Energetic Analyses

Naltrexone is a potent opioid antagonist with good blood–brain barrier permeability, targeting different endogenous opioid receptors, particularly the mu-opioid receptor (MOR). Therefore, it represents a promising candidate for drug development against drug addiction. However, the details of the molecular interactions of naltrexone and its derivatives with MOR are not fully understood, hindering ligand-based drug discovery. In the present study, taking advantage of the high-resolution X-ray crystal structure of the murine MOR (mMOR), we constructed a homology model of the human MOR (hMOR). A solvated phospholipid bilayer was built around the hMOR and submitted to microsecond (µs) molecular dynamics (MD) simulations to obtain an optimized hMOR model. Naltrexone and its derivatives were docked into the optimized hMOR model and submitted to µs MD simulations in an aqueous membrane system. The MD simulation results were submitted to the molecular mechanics–generalized Born surface area (MMGBSA) binding free energy calculations and principal component analysis. Our results revealed that naltrexone and its derivatives showed differences in protein–ligand interactions; however, they shared contacts with residues at TM2, TM3, H6, and TM7. The binding free energy and principal component analysis revealed the structural and energetic effects responsible for the higher potency of naltrexone compared to its derivatives.     

QSAR,Molecular Docking,MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm–multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R² = 0.69–0.87, F = 403.46–292.11, etc., internal validation parameters; Q²_LOO = 0.69–0.86, Q²_LMO = 0.69–0.86, CCC_cv = 0.82–0.93, etc., or external validation parameters Q²_F1 = 0.64–0.82, Q²_F2 = 0.63–0.82, Q²_F3 = 0.65–0.81, R²_ext = 0.65–0.83 including RMSE_tr < RMSE_cv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.     

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking,MD Simulation and MMGBSA

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm–multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R² = 0.83–0.81, F = 61.22–67.96, internal validation parameters such as Q²_LOO = 0.79–0.77, Q²_LMO = 0.78–0.76, CCC_cv = 0.89–0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound’s binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.     

A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level

What factors favor protein folding? This is a textbook question. Parsing the experimental free energies of folding/unfolding into diverse enthalpic and entropic components of solute and solvent favoring or disfavoring folding is not an easy task. In this study, we present a computational protocol for estimating the free energy contributors to protein folding semi‐quantitatively using ensembles of unfolded and native states generated via molecular dynamics simulations. We tested the methodology on 35 proteins with diverse structural motifs and sizes and found that the calculated free energies correlate well with experiment (correlation coefficient ∼ 0.85), enabling us to develop a consensus view of the energetics of folding. As a more sensitive test of the methodology, we also investigated the free energies of folding of an additional 33 single point mutants and obtained a correlation coefficient of 0.8. A notable observation is that the folding free energy components appear to carry signatures of the fold (SCOP classification) of the protein. © 2017 Wiley Periodicals, Inc.     

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking,molecular dynamics simulation and MMGBSA calculation approaches

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved _PEC₅₀ value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.