Identification of novel NAD(P)H dehydrogenase [quinone] 1 antagonist using computational approaches.
01 Feb 2020-Journal of Biomolecular Structure & Dynamics (Taylor & Francis)-Vol. 38, Iss: 3, pp 682-696
TL;DR: The results of induced fit docking and prime/MM-GBSA suggest that leads AN-153/J117103 and AT-138/KB09997 binding with the catalytic site suggests potential of this compound to treat cancer.
Abstract: NAD(P)H: quinone oxidoreductase 1 (NQO1) inhibitors are proved as promising therapeutic agents against cancer. This study is to determine potent NAD(P)H-dependent NQO1 inhibitors with new scaffold. Pharmacophore-based three-dimensional (3D) QSAR model has been built based on 45 NQO1 inhibitors reported in the literature. The structure-function correlation coefficient graph represents the relationship between phase activity and phase predicted activity for training and test sets. A QSAR model statistics shows the excellent correlation of the generated model. Pharmacophore hypothesis (AARR) yielded a statistically significant 3D QSASR model with a correlation coefficient of r2 = 0.99 as well as an excellent predictive power. From the analysis of pharmacophore-based virtual screening using by SPEC database, 4093 hits were obtained and were further filtered using virtual screening filters (HTVS, SP, XP) through structure based molecular docking. Based on glide energy and docking score, seven lead compounds show better binding affinity compared to the co-crystal inhibitor. The results of induced fit docking and prime/MM-GBSA suggest that leads AN-153/J117103 and AT-138/KB09997 binding with the catalytic site. Further, to understanding the stability of identified lead compounds MD simulations were done. The lead AN-153/J117103 showed the strong binding stable of the protein-ligand complex. Also the computed drug likeness reveals potential of this compound to treat cancer. AbbreviationsNQO1NAD(P)H-quinine oxidoreductase 1CPHcommon pharmacophore hypothesisPLSpartial least squireHBDhydrogen bond donorSDstandard deviationXPextra precisionIFDinduced fit dockingMM-GBSAmolecular mechanics generalized born surface areaMDSmolecular dynamics simulationRMSDroot mean square deviationRMSFroot mean square fluctuationRMSEroot mean square errorADMEabsorption distribution metabolism excretionsCommunicated by Ramaswamy H. Sarma.
Topics: Pharmacophore (61%), Docking (molecular) (53%), NAD(P)H Dehydrogenase (Quinone) (52%), Virtual screening (52%), Quantitative structure–activity relationship (50%)
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TL;DR: NQO1 emerges as a good model to investigate loss of function mechanisms in genetic diseases as well as to improve strategies to discriminate between neutral and pathogenic variants in genome-wide sequencing studies.
Abstract: NAD(P)H quinone oxidoreductase 1 (NQO1) is a multi-functional protein that catalyses the reduction of quinones (and other molecules), thus playing roles in xenobiotic detoxification and redox balance, and also has roles in stabilising apoptosis regulators such as p53. The structure and enzymology of NQO1 is well-characterised, showing a substituted enzyme mechanism in which NAD(P)H binds first and reduces an FAD cofactor in the active site, assisted by a charge relay system involving Tyr-155 and His-161. Protein dynamics play important role in physio-pathological aspects of this protein. NQO1 is a good target to treat cancer due to its overexpression in cancer cells. A polymorphic form of NQO1 (p.P187S) is associated with increased cancer risk and certain neurological disorders (such as multiple sclerosis and Alzheimer´s disease), possibly due to its roles in the antioxidant defence. p.P187S has greatly reduced FAD affinity and stability, due to destabilization of the flavin binding site and the C-terminal domain, which leading to reduced activity and enhanced degradation. Suppressor mutations partially restore the activity of p.P187S by local stabilization of these regions, and showing long-range allosteric communication within the protein. Consequently, the correction of NQO1 misfolding by pharmacological chaperones is a viable strategy, which may be useful to treat cancer and some neurological conditions, targeting structural spots linked to specific disease-mechanisms. Thus, NQO1 emerges as a good model to investigate loss of function mechanisms in genetic diseases as well as to improve strategies to discriminate between neutral and pathogenic variants in genome-wide sequencing studies.
41 citations
Journal Article•
TL;DR: Comparisons between pancreatic adenocarcinoma, pancreatic cancer cell lines, normal pancreas, and chronic pancreatitis have identified genes that are selectively expressed in the neoplastic epithelium of pancreatic adsorption.
Abstract: The molecular basis of pancreatic cancer is not understood. Previous attempts to determine the specific genes expressed in pancreatic cancer have been hampered by similarities between adenocarcinoma and chronic pancreatitis. In the current study, microarrays (Affymetrix) were used to profile gene expression in pancreatic adenocarcinoma (10), pancreatic cancer cell lines (7), chronic pancreatitis (5), and normal pancreas (5). Molecular profiling indicated a large number of genes differentially expressed between pancreatic cancer and normal pancreas but many fewer differences between pancreatic cancer and chronic pancreatitis, likely because of the shared stromal influences in the two diseases. To specifically identify genes expressed in neoplastic epithelium, we selected genes more highly expressed (>2-fold, p < 0.01) in adenocarcinoma compared with both normal pancreas and chronic pancreatitis and which were also highly expressed in pancreatic cancer cell lines. This strategy yielded 158 genes, of which 124 were not previously associated with pancreatic cancer. Quantitative-reverse transcription-PCR for two molecules, S100P and 14-3-3sigma, validated the microarray data. Support for the success of the neoplastic cell gene expression identification strategy was obtained by immunocytochemical localization of four representative genes, 14-3-3sigma, S100P, S100A6, and beta4 integrin, to neoplastic cells in pancreatic tumors. Thus, comparisons between pancreatic adenocarcinoma, pancreatic cancer cell lines, normal pancreas, and chronic pancreatitis have identified genes that are selectively expressed in the neoplastic epithelium of pancreatic adenocarcinoma. These data provide new insights into the molecular pathology of pancreatic cancer that may be useful for detection, diagnosis, and treatment.
25 citations
References
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Abstract: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described In the discovery setting 'the rule of 5' predicts that poor absorption or permeation is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight (MWT) is greater than 500 and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415) Computational methodology for the rule-based Moriguchi Log P (MLogP) calculation is described Turbidimetric solubility measurement is described and applied to known drugs High throughput screening (HTS) leads tend to have higher MWT and Log P and lower turbidimetric solubility than leads in the pre-HTS era In the development setting, solubility calculations focus on exact value prediction and are difficult because of polymorphism Recent work on linear free energy relationships and Log P approaches are critically reviewed Useful predictions are possible in closely related analog series when coupled with experimental thermodynamic solubility measurements
12,161 citations
TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Abstract: Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose Errors in geometry for the top-ranked pose are less than 1 A in nearly ha
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...This workflow contains three accuracy level of docking (high-throughput virtual screening [HTVS], standard precision [SP], and extra precision [XP] (Friesner et al., 2004)....
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TL;DR: Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
Abstract: Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of “druglike” decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both “early” and “global” enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand−protei...
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"Identification of novel NAD(P)H deh..." refers methods in this paper
...This workflow contains three accuracy level of docking (high-throughput virtual screening [HTVS], standard precision [SP], and extra precision [XP] (Friesner et al., 2004)....
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TL;DR: Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
Abstract: A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.
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"Identification of novel NAD(P)H deh..." refers methods in this paper
...All the computational analyses were carried using different modules available in Schrodinger software (Friesner et al., 2006)....
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TL;DR: MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics or Monte Carlo simulations, including the Poisson-Boltzmann Model and several implicit solvation models.
Abstract: MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson–Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, rele...
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"Identification of novel NAD(P)H deh..." refers methods in this paper
...The Prime/MM-GBSA (Miller et al., 2012) method based on the docking complex was used to calculate the binding free energy (DGbind) of each ligand, using the following equation (Lyne, Lamb, & Saeh, 2006) DGbind ¼ DEMM þ DGSOL þ DGSA where DEMM is the difference in the minimized energies between the…...
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...In order to predict the binding mode and free energy for the best-docked complex leads, Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) calculations were applied, which also substantiated the profound inhibitory nature of the leads against NQO1....
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...Further, ADMET and Prime/MM-GBSA calculations were performed for the best hits....
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...The Prime/MM-GBSA (Miller et al., 2012) method based on the docking complex was used to calculate the binding free energy (DGbind) of each ligand, using the following equation (Lyne, Lamb, & Saeh, 2006) DGbind ¼ DEMM þ DGSOL þ DGSA where DEMM is the difference in the minimized energies between the NQO1-inhibitor complex and the sum of the energies of the unbound NQO1 and inhibitor....
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...The Prime/MM-GBSA (Miller et al., 2012) method based on the docking complex was used to calculate the binding free energy (DGbind) of each ligand, using the following equation (Lyne, Lamb, & Saeh, 2006)...
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