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Seo M, Choi J, Park J, Yu WJ, Kim S. Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity. CHEMOSPHERE 2024; 349:140926. [PMID: 38092168 DOI: 10.1016/j.chemosphere.2023.140926] [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: 09/27/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.
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Affiliation(s)
- Myungwon Seo
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Jiwon Choi
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Jongseo Park
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Wook-Joon Yu
- Developmental and Reproductive Toxicology Research Group, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea.
| | - Sunmi Kim
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
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Guan R, Liu W, Li N, Cui Z, Cai R, Wang Y, Zhao C. Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 337:122620. [PMID: 37769706 DOI: 10.1016/j.envpol.2023.122620] [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: 05/24/2023] [Revised: 09/03/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.
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Affiliation(s)
- Ruining Guan
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Wencheng Liu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Ningqi Li
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Zeyang Cui
- School of Information Science & Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Ruitong Cai
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Yawei Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China
| | - Chunyan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China.
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Wang F, Hu S, Ma DQ, Li Q, Li HC, Liang JY, Chang S, Kong R. ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators. Front Pharmacol 2022; 13:800885. [PMID: 35140614 PMCID: PMC8819068 DOI: 10.3389/fphar.2022.800885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.
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Affiliation(s)
- Feng Wang
- Changzhou University Huaide College, Taizhou, China
| | - Shuai Hu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
| | - De-Qing Ma
- Changzhou University Huaide College, Taizhou, China
| | - Qiuye Li
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hong-Cheng Li
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
| | - Jia-Yi Liang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, School of Chemical and Environmental Engineering, Jiangsu University of Technology, Changzhou, China
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Sellami A, Réau M, Montes M, Lagarde N. Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns. Front Endocrinol (Lausanne) 2022; 13:986016. [PMID: 36176461 PMCID: PMC9513233 DOI: 10.3389/fendo.2022.986016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.
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Zhou Q, Chen J, Zhang J, Zhou F, Zhao J, Wei X, Zheng K, Wu J, Li B, Pan B. Toxicity and endocrine-disrupting potential of PM 2.5: Association with particulate polycyclic aromatic hydrocarbons, phthalate esters, and heavy metals. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 292:118349. [PMID: 34653588 DOI: 10.1016/j.envpol.2021.118349] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
The adverse effects of fine atmospheric particulate matter with aerodynamic diameters of ≤2.5 μm (PM2.5) are closely associated with particulate chemicals. In this study, PM2.5 samples were collected from highway and industry sites in Hangzhou, China, during the autumn and winter, and their cytotoxicity and pulmonary toxicity and endocrine-disrupting potential (EDP) were evaluated in vitro and in vivo; the particulate polycyclic aromatic hydrocarbons (PAHs), phthalate esters (PAEs), and heavy metals were then characterized. The toxicological results suggested that the PM2.5 from highway site induced higher cytotoxicity (cell viability inhibition, intracellular oxidative stress, and cell membrane injury) and pulmonary toxicity (inflammatory response (IR) and oxidative stress (OS)) than the samples from industry site, while the PM2.5 from industry site exhibited higher EDP (estrogenic and anti-androgenic activity). The cytotoxicity and pulmonary toxicity of PM2.5 in the winter were higher than those in the autumn, while no seasonal difference in the endocrine-disrupting potential was observed (p > 0.05). The Pearson correlation analysis between the biological effects and particulate chemicals revealed that the PM2.5-induced inflammatory response and oxidative stress were closely associated with the particulate PAHs and heavy metals (Pearson correlation coefficients: rIR, PAHs = 0.822-0.988, rIR, heavy metals = 0.895-0.971, rOS, PAHs = 0.843-0.986, and rOS, heavy metals = 0.887-0.933), while particulate di (2-ethylhexyl)phthalate (DEHP) substantially contributed to the EDP of PM2.5 (rEDP, DEHP = 0.981). This study indicated that the toxicity and EDP of PM2.5 could vary with the surrounding environment and season, which was closely associated with the variations of particulate chemicals. Further studies are needed to clarify the associations between the harmful effects of PM2.5 and other contributing factors.
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Affiliation(s)
- Qinghua Zhou
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Jinyuan Chen
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Junfan Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Feifei Zhou
- Departments of TCM Gynecology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jingjing Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Xiuzhen Wei
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Kaiyun Zheng
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Jian Wu
- Ecology and Environmental Science Research & Design Institute of Zhejiang Province, Hangzhou, China
| | - Bingjie Li
- College of Environment, Zhejiang University of Technology, Hangzhou, China
| | - Bingjun Pan
- College of Environment, Zhejiang University of Technology, Hangzhou, China.
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Goya-Jorge E, Amber M, Gozalbes R, Connolly L, Barigye SJ. Assessing the chemical-induced estrogenicity using in silico and in vitro methods. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2021; 87:103688. [PMID: 34119701 DOI: 10.1016/j.etap.2021.103688] [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: 03/04/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 06/12/2023]
Abstract
Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC's effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds.
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Affiliation(s)
- Elizabeth Goya-Jorge
- ProtoQSAR SL., CEEI (Centro Europeo de Empresas Innovadoras), Parque Tecnológico de Valencia, 12 Av. Benjamin Franklin, 46980, Paterna, Valencia, Spain; Department of Food Science, Faculty of Veterinary Medicine-FARAH, University of Liège, 10 Av. Cureghem, 4000, Sart-Tilman, Liège, Belgium.
| | - Mazia Amber
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, BT9 5DL, Belfast, Northern Ireland, United Kingdom.
| | - Rafael Gozalbes
- ProtoQSAR SL., CEEI (Centro Europeo de Empresas Innovadoras), Parque Tecnológico de Valencia, 12 Av. Benjamin Franklin, 46980, Paterna, Valencia, Spain; MolDrug AI Systems SL, 45 Olimpia Arozena Torres, 46018, Valencia, Spain.
| | - Lisa Connolly
- The Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, BT9 5DL, Belfast, Northern Ireland, United Kingdom.
| | - Stephen J Barigye
- ProtoQSAR SL., CEEI (Centro Europeo de Empresas Innovadoras), Parque Tecnológico de Valencia, 12 Av. Benjamin Franklin, 46980, Paterna, Valencia, Spain; MolDrug AI Systems SL, 45 Olimpia Arozena Torres, 46018, Valencia, Spain.
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Wang J, Huang Y, Wang S, Yang Y, He J, Li C, Zhao YH, Martyniuk CJ. Identification of active and inactive agonists/antagonists of estrogen receptor based on Tox21 10K compound library: Binomial analysis and structure alert. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 214:112114. [PMID: 33711575 DOI: 10.1016/j.ecoenv.2021.112114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 06/12/2023]
Abstract
Endocrine disrupting chemicals can mimic, block, or interfere with hormones in organisms and subsequently affect their development and reproduction, which has raised significant public concern over the past several decades. To investigate (quantitative) structure-activity relationship, 8280 compounds were compiled from the Tox21 10K compound library. The results show that 50% activity concentrations of agonists are poorly related to that of antagonists because many compounds have considerably different activity concentrations between the agonists and antagonists. Analysis on the chemical classes based on mode of action (MOA) reveals that estrogen receptor (ER) is not the main target site in the acute toxicity to aquatic organisms. Binomial analysis of active and inactive ER agonists/antagonists reveals that ER activity of compounds is dominated by octanol/water partition coefficient and excess molar refraction. The binomial equation developed from the two descriptors can classify well active and inactive ER chemicals with an overall prediction accuracy of 73%. The classification equation developed from the molecular descriptors indicates that estrogens react with the receptor through hydrophobic and π-n electron interactions. At the same time, molecular ionization, polarity, and hydrogen bonding ability can also affect the chemical ER activity. A decision tree developed from chemical structures and their applications reveals that many hormones, proton pump inhibitors, PAHs, progestin, insecticides, fungicides, steroid and chemotherapy medications are active ER agonists/antagonists. On the other hand, many monocyclic/nonaromatic chain compounds and herbicides are inactive ER compounds. The decision tree and binomial equation developed here are valuable tools to predict active and inactive ER compounds.
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Affiliation(s)
- Jia Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Ying Huang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Shuo Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Yi Yang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Jia He
- Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Chao Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China.
| | - Yuan H Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China.
| | - Christopher J Martyniuk
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, UF Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, University of Florida, Gainesville, FL 32611, USA
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Shen C, Zhu K, Ruan J, Li J, Wang Y, Zhao M, He C, Zuo Z. Screening of potential oestrogen receptor α agonists in pesticides via in silico, in vitro and in vivo methods. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116015. [PMID: 33352482 DOI: 10.1016/j.envpol.2020.116015] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/28/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
In modern agricultural management, the use of pesticides is indispensable. Due to their massive use worldwide, pesticides represent a latent risk to both humans and the environment. In the present study, 1056 frequently used pesticides were screened for oestrogen receptor (ER) agonistic activity by using in silico methods. We found that 72 and 47 pesticides potentially have ER agonistic activity by the machine learning methods random forest (RF) and deep neural network (DNN), respectively. Among endocrine-disrupting chemicals (EDCs), 14 have been reported as EDCs or ER agonists by previous studies. We selected 3 reported and 7 previously unreported pesticides from 76 potential ER agonists to further assess ERα agonistic activity. All 10 selected pesticides exhibited ERα agonistic activity in human cells or zebrafish. In the dual-luciferase reporter gene assays, six pesticides exhibited ERα agonistic activity. Additionally, nine pesticides could induce mRNA expression of the pS2 and NRF1 genes in MCF-7 cells, and seven pesticides could induce mRNA expression of the vtg1 and vtg2 genes in zebrafish. Importantly, the remaining 48 out of 76 potential ER agonists, none of which have previously been reported to have endocrine-disrupting effects or oestrogenic activity, should be of great concern. Our screening results can inform environmental protection goals and play an important role in environmental protection and early warnings to human health.
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Affiliation(s)
- Chao Shen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Jinpeng Ruan
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Jialing Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Yi Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Meirong Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, China
| | - Chengyong He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Zhenghong Zuo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China.
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Tang W, Chen J, Hong H. Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training. CHEMOSPHERE 2020; 253:126768. [PMID: 32464767 DOI: 10.1016/j.chemosphere.2020.126768] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/08/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
Humans and animals may be exposed to tens of thousands of natural and synthetic chemicals during their lifespan. It is difficult to assess risk for all the chemicals with experimental toxicity tests. An alternative approach is to use computational toxicology methods such as quantitative structure-activity relationship (QSAR) modeling. Mitochondrial toxicity is involved in many diseases such as cancer, neurodegeneration, type 2 diabetes, cardiovascular diseases and autoimmune diseases. Thus, it is important to rapidly and efficiently identify chemicals with mitochondrial toxicity. In this study, five machine learning algorithms and twelve types of molecular fingerprints were employed to generate QSAR discriminant models for mitochondrial toxicity. A threshold moving method was adopted to resolve the imbalance issue in the training data. Consensus of the models by an averaging probability strategy improved prediction performance. The best model has correct classification rates of 81.8% and 88.3% in ten-fold cross validation and external validation, respectively. Substructures such as phenol, carboxylic acid, nitro and arylchloride were found informative through analysis of information gain and frequency of substructures. The results demonstrate that resolving imbalance in training and building consensus models can improve classification rates for mitochondrial toxicity prediction.
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Affiliation(s)
- Weihao Tang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA
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Ji C, Song Q, Chen Y, Zhou Z, Wang P, Liu J, Sun Z, Zhao M. The potential endocrine disruption of pesticide transformation products (TPs): The blind spot of pesticide risk assessment. ENVIRONMENT INTERNATIONAL 2020; 137:105490. [PMID: 32007685 DOI: 10.1016/j.envint.2020.105490] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 06/10/2023]
Abstract
The ecological and health risk assessment of environmental pesticide residues have attracted ever-growing attention; however, their transformation products (TPs) have seldom been considered. Herein, we examined the endocrine-disrupting effects of 4 widely used pesticides as pyriproxyfen (Pyr), malathion (ML), benalaxyl (BX), and fenoxaprop-ethyl (FE), together with their 21 TPs through in vitro and in silico approaches, and found approximately 50% of the TPs exhibited stronger endocrine-disrupting effects than their corresponding parent compounds. Specifically, Pyr and 9 TPs (five TPs of Pyr, one of ML, one of BX, and two of FE) exhibited estrogen-disrupting effects, which were also confirmed by results of E-screen and pS2 expression assays, and molecular docking showed that certain hydroxylated TPs could well mimic the binding mode of estrogen with ERα. Meanwhile, two TPs of Pyr, ML and its TP demonstrated weak glucocorticoid antagonistic activities partially contributed by hydrogen bonds. We also discovered that in H295R cells, all the endocrine disruptors increased hormone secretion and the related gene expression levels. Conclusively, since an increasing number of pesticide TPs have been being detected in various environmental media, a more comprehensive understanding of the ecological risk of pesticide TPs is imperative for risk assessments more extensively and regulatory policy-making on pesticide restriction in the future.
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Affiliation(s)
- Chenyang Ji
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Qin Song
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Yuanchen Chen
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhiqiang Zhou
- Department of Applied Chemistry, China Agricultural University, Beijing 100193, China
| | - Peng Wang
- Department of Applied Chemistry, China Agricultural University, Beijing 100193, China
| | - Jing Liu
- College of Environment & Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhe Sun
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Meirong Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
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Schneider M, Pons JL, Labesse G, Bourguet W. In Silico Predictions of Endocrine Disruptors Properties. Endocrinology 2019; 160:2709-2716. [PMID: 31265055 PMCID: PMC6804484 DOI: 10.1210/en.2019-00382] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 06/26/2019] [Indexed: 01/12/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.
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Affiliation(s)
- Melanie Schneider
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
| | - William Bourguet
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
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Verma N, Singh H, Khanna D, Rana PS, Bhadada SK. Classification of drug molecules for oxidative stress signalling pathway. IET Syst Biol 2019; 13:243-250. [PMID: 31538958 PMCID: PMC8687196 DOI: 10.1049/iet-syb.2018.5078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.
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Affiliation(s)
- Nikhil Verma
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India.
| | - Harpreet Singh
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Divya Khanna
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Prashant Singh Rana
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India
| | - Sanjay Kumar Bhadada
- Department of Endocrinology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
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13
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Bai X, Yan L, Ji C, Zhang Q, Dong X, Chen A, Zhao M. A combination of ternary classification models and reporter gene assays for the comprehensive thyroid hormone disruption profiles of 209 polychlorinated biphenyls. CHEMOSPHERE 2018; 210:312-319. [PMID: 30005353 DOI: 10.1016/j.chemosphere.2018.07.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 06/24/2018] [Accepted: 07/05/2018] [Indexed: 06/08/2023]
Abstract
Computational toxicology is widely applied to screen tens and thousands of potential environmental endocrine disruptors (EDCs) for its great efficiency and rapid evaluation in recent years. Polychlorinated biphenyls (PCBs) with 209 congeners have not been comprehensively tested for their ability to interact with the thyroid receptor (TR), which is one of the most extensively studied targets related to the effects of EDCs. In this study, we aimed to determine the thyroid-disrupting mechanisms of 209 PCBs through the combination of a novel computational ternary classification model and luciferase reporter gene assay. The reporter gene assay was performed to examine the hormone activities of 22 PCBs via TR to classify their profiles as agonistic, antagonistic or inactive. Thus, six agonists, eleven antagonists and seven inactive chemicals against TR were identified in in vitro assays. Then, six relevant variables, including molecular structural descriptors and molecular docking scores, were selected for describing compounds. Machine learning methods (i.e., linear discriminant analysis (LDA) and support vector machines (SVM)) were used to build classification models. The optimal model was obtained by using SVM, with an accuracy of 88.24% in the training set, 80.0% in the test set and 75.0% in 10-fold cross-validation. The remaining 187 PCB congeners' hormone activities were predicted using the obtained models. Out of these PCBs, the SVM model predicted 38 agonists and 81 antagonists. The findings revealed that higher chlorinated PCBs tended to have TR-antagonistic activities, whereas lower chlorinated PCBs were agonists. This study provided a reference for further exploring PCBs' thyroid effect.
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Affiliation(s)
- Xiaoxia Bai
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310006, China
| | - Lu Yan
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Chenyang Ji
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Quan Zhang
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - An Chen
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, 310018, China
| | - Meirong Zhao
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China.
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14
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Yan L, Zhang Q, Huang F, Nie WW, Hu CQ, Ying HZ, Dong XW, Zhao MR. Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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15
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Adeniyi AA, Soliman MES. Implementing QM in docking calculations: is it a waste of computational time? Drug Discov Today 2017; 22:1216-1223. [PMID: 28689054 DOI: 10.1016/j.drudis.2017.06.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 05/25/2017] [Accepted: 06/29/2017] [Indexed: 12/14/2022]
Abstract
The greatest challenge in molecular docking (MD) is the deficiency of scoring functions (SFs), which limits their reliability. SFs are too simplified to represent the true features of the complex free energy of protein-ligand interactions. Investigations of docking functions have traded accuracy for speed through the use of approximations and simplifications. Consequently, there has been an increase in the popularity of quantum-mechanical (QM)-based methods as reference points for the development of fast, reliable, valuable, yet inexpensive, tools. As we discuss here, one significant QM-based parameter used to predict docking is the accuracy of atomic partial charges, which is strongly related to the accuracy of the SF prediction.
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Affiliation(s)
- Adebayo A Adeniyi
- School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4001, South Africa.
| | - Mahmoud E S Soliman
- School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4001, South Africa.
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