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Kumar Konidala K, Bommu U, Pabbaraju N. Integration of in silico methods to determine endocrine-disrupting tobacco pollutants binding potency with steroidogenic genes: comprehensive QSAR modeling and ensemble docking strategies. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:65806-65825. [PMID: 35501431 DOI: 10.1007/s11356-022-20443-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/21/2022] [Indexed: 06/14/2023]
Abstract
A myriad of tobacco-associated chemicals may have possibilities to developmental/reproductive axis and endocrine-disruption impacts. Mostly they breach the biotransformation of cholesterol in mitochondria by interfering with steroidogenic pathway genes, prompting to adverse effects in steroid biosynthesis; however, studies are scanty. The quantitative structure-activity relationship (QSAR) modeling and comparative docking strategies were used to understand structural features of dataset compounds that influence developmental/reproductive toxicity and estrogen and androgen receptor-binding abilities, and to predict binding levels of toxicants with steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1) active sites. Developed QSAR models presented good robustness and predictive ability that were determined from the applicability domain and, clustering and classification of chemicals by performing self-organizing maps. Accordingly, the exorbitant amount of polycyclic aromatic hydrocarbons (PAHs) and a limited number of other chemicals including N-nitrosamines and nicotine was represented as potential developmental/reproductive toxicants as well as estrogen and androgen receptor binders. From the docking analysis, hydrogen bonding, nonpolar, atomic π-stacking, and π-cation interactions were found between PAHs (bay and fjord structural pockets) and functional hotspot residues of StAR and CYP11A1, which strengthened the subtle structural changes at domains. These govern barrier effects to cholesterol binding and/or locking cholesterol to complicate its ejection from the Ω1 loop of StAR, and further mitigates steroid biosynthesis through cholesterol by CYP11A1; therefore, they are presumably considered as block-cluster mechanisms. These outcomes are significant to be hopeful to estimate developmental/reproductive toxicity and endocrine-disruption activities of other environmental pollutants, and could be useful for further assessment to discover binding mechanisms of PAHs with other steroidogenesis pathway genes.
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Affiliation(s)
| | - Umadevi Bommu
- Department of Zoology, Sri Venkateswara University, Tirupati, 517502 AP, India
| | - Neeraja Pabbaraju
- Department of Zoology, Sri Venkateswara University, Tirupati, 517502 AP, India.
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Kranthi Kumar K, Yugandhar P, Uma Devi B, Siva Kumar T, Savithramma N, Neeraja P. Applications of in silico methods to analyze the toxicity and estrogen receptor-mediated properties of plant-derived phytochemicals. Food Chem Toxicol 2019; 125:361-369. [DOI: 10.1016/j.fct.2018.12.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 12/17/2018] [Accepted: 12/20/2018] [Indexed: 12/16/2022]
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3
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Vračko M, Drgan V. Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2017; 28:801-813. [PMID: 29156996 DOI: 10.1080/1062936x.2017.1398184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 10/25/2017] [Indexed: 06/07/2023]
Abstract
Methods for clustering and measures of similarity of chemical structures have become an important supporting tool in chemoinformatics. They represent the basis for categorization of chemicals and read-across, where a molecular property is estimated from 'similar molecules'. This study proposes a clustering scheme within the given dataset with respect to a reference dataset. The scheme was applied on two datasets ToxCast_AR_Agonist and ToxCast_AR_Antagonists with 1654 and 1522 compounds, respectively. The compounds are tested to androgen receptor activity (AR) in 11 high throughput screening assays. The carcinogenic dataset was used as the reference set.
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Affiliation(s)
- M Vračko
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
| | - V Drgan
- a National Institute of Chemistry , Kemijski Inštitut , Ljubljana , Slovenia
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Plošnik A, Zupan J, Vračko M. Evaluation of toxic endpoints for a set of cosmetic ingredients with CAESAR models. CHEMOSPHERE 2015; 120:492-499. [PMID: 25278177 DOI: 10.1016/j.chemosphere.2014.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Revised: 08/29/2014] [Accepted: 09/02/2014] [Indexed: 06/03/2023]
Abstract
The randomly selected set of 558 chemicals from Cosmetic inventory was studied with internet accessible program package CAESAR. Four toxic endpoints were considered: mutagenicity, carcinogenicity, developmental toxicity and skin sensitization. The CAESAR program provides beside the predictions comprehensive information on applicability domain and the similarity between the considered compound and the compounds from model's training set. This information was used to implement for clustering and classification of chemicals. As the technique the Self Organizing Maps was applied. This technique also enables us to define to each cluster the cluster indicator, i.e., the characteristic compound, which is considered as a representative for a cluster.
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Affiliation(s)
- Alja Plošnik
- Kemijski institut/National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Jure Zupan
- Kemijski institut/National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Marjan Vračko
- Kemijski institut/National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.
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Bangyal WH, Ahmad J, Shafi I, Abbas Q. A forward only counter propagation network-based approach for contraceptive method choice classification task. J EXP THEOR ARTIF IN 2012. [DOI: 10.1080/0952813x.2011.639091] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Balabin RM, Lomakina EI. Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys Chem Chem Phys 2011; 13:11710-8. [DOI: 10.1039/c1cp00051a] [Citation(s) in RCA: 139] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Balabin RM, Lomakina EI. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. J Chem Phys 2009; 131:074104. [DOI: 10.1063/1.3206326] [Citation(s) in RCA: 115] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol Divers 2009; 14:581-94. [DOI: 10.1007/s11030-009-9190-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2009] [Accepted: 07/26/2009] [Indexed: 10/20/2022]
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Nandi S, Vracko M, Bagchi MC. Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks. Chem Biol Drug Des 2008; 70:424-36. [PMID: 17949360 DOI: 10.1111/j.1747-0285.2007.00575.x] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Phenol and its congeners are known to induce caspase-mediated apoptosis activity and cytotoxicity on various cancer cell lines. Apoptosis, scavenging of radicals, antioxidant, and pro-oxidant characteristics are primarily responsible for the antitumor activities of phenolic compounds. Quantitative structure-activity relationship studies on the cellular apoptosis and cytotoxicity of phenolic compounds have been investigated recently by Selassie and colleagues (J Med Chem; 48:7234, 2005) wherein models were developed for various carcinogenic cell lines. These quantitative structure-activity relationship models are based on few experimentally obtained physicochemical parameters such as Verloop's sterimol descriptor, hydrophobicity, Hammett electronic parameter, and octanol/water partition coefficient. The paper deals with structure-activity relationships of phenols and its derivatives for the development of predictive models from the standpoint of theoretical structural parameters and ridge regression methodology. The quantitative structure-activity relationship studies developed here for the caspase-mediated apoptosis activity and cytotoxicity on murine leukemia cell line (L1210), human promylolytic cell line (HL-60), human breast cancer cell line (MCF-7), parenteral human acute lymphoblastic cells (CCRF-CEM), and multidrug-resistant subline of CCRF-resistant to vinblastine (CEM/VLB) cells utilize physicochemical molecular descriptors calculated solely from the structure of phenolic compounds under investigation along with the descriptors used by Selassie and group. It is seen that such quantitative structure-activity relationships can provide a better quality predictive model for the phenolic compounds. The biological activities of the nine sets of phenolic compounds have been calculated based on ridge regression analysis that clearly gives a better significant correlation compared to the activities predicted by Selassie and co-workers. Counter-propagation artificial neural network studies have been introduced in the present investigation for a better understanding of multidimensional rational patterns in more complex data sets. The counter-propagation artificial neural network studies were performed on the same data set and with the same descriptors as have been carried out in developing ridge regression models and the result of counter-propagation neural network models produces very interesting findings in terms of leave-one-out test. Finally, an attempt has been made for a comparative study of the relative effectiveness of linear statistical methods versus nonlinear techniques, such as counter-propagation neural networks in modeling structure-activity studies of the phenolic compounds.
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Affiliation(s)
- Sisir Nandi
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, 4 Raja S.C. Mullick Road, Jadavpur, Calcutta, India
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Liu H, Papa E, Walker JD, Gramatica P. In silico screening of estrogen-like chemicals based on different nonlinear classification models. J Mol Graph Model 2007; 26:135-44. [PMID: 17293141 DOI: 10.1016/j.jmgm.2007.01.003] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2006] [Revised: 01/10/2007] [Accepted: 01/12/2007] [Indexed: 01/28/2023]
Abstract
Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens.
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Affiliation(s)
- Huanxiang Liu
- Department of Structural and Functional Biology, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, via Dunant 3, 21100 Varese, Italy
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Vracko M, Bandelj V, Barbieri P, Benfenati E, Chaudhry Q, Cronin M, Devillers J, Gallegos A, Gini G, Gramatica P, Helma C, Mazzatorta P, Neagu D, Netzeva T, Pavan M, Patlewicz G, Randić M, Tsakovska I, Worth A. Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:265-84. [PMID: 16815767 DOI: 10.1080/10659360600787650] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.
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Affiliation(s)
- M Vracko
- European Chemical Beaureau, Institute for Health and Consumer Protection, European Commission Joint Research Centre, 21020 Ispra, Italy.
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