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.
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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.
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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.
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40 citations
References
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TL;DR: Most critical QSAR modeling routines that are regarded as best practices in the field are examined, including procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries.
Abstract: After nearly five decades "in the making", QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever growing collections of biologically active chemical compounds. This review examines most critical QSAR modeling routines that we regard as best practices in the field. We discuss these procedures in the context of integrative predictive QSAR modeling workflow that is focused on achieving models of the highest statistical rigor and external predictive power. Specific elements of the workflow consist of data preparation including chemical structure (and when possible, associated biological data) curation, outlier detection, dataset balancing, and model validation. We especially emphasize procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries. Finally, we present several examples of successful applications of QSAR models for virtual screening to identify experimentally confirmed hits.
1,362 citations
"Identification of novel NAD(P)H deh..." refers background in this paper
...Tropsha (2010) stated that high r2 is a necessary but not sufficient condition for a QSAR model....
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TL;DR: This work uses explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules and shows that predictions can be improved by using a semiempirical charge assignment method with an implicit bond charge correction.
Abstract: The accurate prediction of protein−ligand binding free energies is a primary objective in computer-aided drug design. The solvation free energy of a small molecule provides a surrogate to the desolvation of the ligand in the thermodynamic process of protein−ligand binding. Here, we use explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules. We also compare the performance of absolute solvation free energies obtained using the OPLS_2005 force field with two other commonly used small molecule force fields—general AMBER force field (GAFF) with AM1-BCC charges and CHARMm-MSI with CHelpG charges. Using the OPLS_2005 force field, we obtain high correlation with experimental solvation free energies (R2 = 0.94) and low average unsigned errors for a majority of the functional groups compared to AM1-BCC/GAFF or CHelpG/CHARMm-MSI. However, ...
1,229 citations
"Identification of novel NAD(P)H deh..." refers methods in this paper
...Ligprep module was used to convert 2 D to 3D structures, and minimization was performed using OPLS 2005 force field (Shivakumar et al., 2010), with implicit GB/SA solvent model....
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TL;DR: PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.
Abstract: We introduce PHASE, a highly flexible system for common pharmacophore identification and assessment, 3D QSAR model development, and 3D database creation and searching. The primary workflows and tasks supported by PHASE are described, and details of the underlying scientific methodologies are provided. Using results from previously published investigations, PHASE is compared directly to other ligand-based software for its ability to identify target pharmacophores, rationalize structure-activity data, and predict activities of external compounds.
974 citations
"Identification of novel NAD(P)H deh..." refers background or methods in this paper
...PHASE 6.6 (Dixon et al., 2006) contains a built-in set of six pharmacophoric features, hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), negatively ionizable (N), positively ionizable (P), and aromatic ring (R)....
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...All the minimized conformers were filtered using a relative energy window of 10 kcal mol 1 and a minimum atom deviation of 1.0 Å (Dixon et al., 2006; Dixon, Smondyrev, & Rao, 2006)....
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...6 (Dixon et al., 2006) contains a built-in set of six pharmacophoric features, hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), negatively ionizable (N), positively ionizable (P), and aromatic ring (R)....
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TL;DR: Because of a broader antiviral spectrum, better tolerance, and less potential for emergence of resistance than is seen with the M2 inhibitors, the neuraminidase inhibitors represent an important advance in the treatment of influenza.
683 citations
"Identification of novel NAD(P)H deh..." refers methods in this paper
...We used nact = nact_tot initially (act_tot is the total number of active compounds in the training set); nsites was varied from six to three until a minimum of one hypothesis was found and scored successfully (Steindl & Langer, 2004; Tawari, Bag, & Degani, 2008)....
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TL;DR: This review focuses on detoxification reactions catalyzed by NQO1 and its role in antioxidant defense via the generation of antioxidant forms of ubiquinone and vitamin E.
597 citations
"Identification of novel NAD(P)H deh..." refers background in this paper
...The high levels of NQO1 expression in many human solid tumors compared to normal tissue explain their selective activation within tumor cells (Dong et al., 2010; Ross et al., 2000)....
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...Elevated NQO1 concentrations have also been found within ovary, thyroid, and adrenal gland (Logsdon et al., 2003; Ross et al., 2000)....
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