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Du Y. Binding Curve Viewer: Visualizing the Equilibrium and Kinetics of Protein-Ligand Binding and Competitive Binding. J Chem Inf Model 2024; 64:4180-4192. [PMID: 38720179 PMCID: PMC11134506 DOI: 10.1021/acs.jcim.4c00130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/28/2024]
Abstract
Understanding the thermodynamics and kinetics of the protein-ligand interaction is essential for biologists and pharmacologists. To visualize the equilibrium and kinetics of the binding reaction with 1:1 stoichiometry and no cooperativity, we obtained the exact relationship of the concentration of the protein-ligand complex and the time in the second-order binding process and numerically simulated the process of competitive binding. First, two common concerns in measuring protein-ligand interactions were focused on how to avoid the titration regime and how to establish the appropriate incubation time. Then, we gave examples of how the commonly used experimental conditions of [L]0 ≫ [P]0 and [I]0 ≫ [P]0 affected the estimation of the kinetic and thermodynamic properties. Theoretical inhibition curves were calculated, and the apparent IC50 and IC50 were estimated accordingly under predefined conditions. Using the estimated apparent IC50, we compared the apparent Ki and Ki calculated by using the Cheng-Prusoff equation, Lin-Riggs equation, and Wang's group equation. We also applied our tools to simulate high-throughput screening and compare the results of real experiments. The visualization tool for simulating the saturation experiment, kinetic experiments of binding and competitive binding, and inhibition curve, "Binding Curve Viewer," is available at www.eplatton.net/binding-curve-viewer.
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Affiliation(s)
- Yu Du
- Department
of Clinical Laboratory, The Second Affiliated
Hospital of Jiaxing University, Huancheng North Road 1518, Jiaxing, Zhejiang 314000, China
- The
Key Laboratory, The Second Affiliated Hospital
of Jiaxing University, Huancheng North Road 1518, Jiaxing, Zhejiang 314000, China
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Golosovskaia E, Örn S, Ahrens L, Chelcea I, Andersson PL. Studying mixture effects on uptake and tissue distribution of PFAS in zebrafish (Danio rerio) using physiologically based kinetic (PBK) modelling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168738. [PMID: 38030006 DOI: 10.1016/j.scitotenv.2023.168738] [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: 09/18/2023] [Revised: 11/17/2023] [Accepted: 11/19/2023] [Indexed: 12/01/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are ubiquitously distributed in the aquatic environment. They include persistent, mobile, bioaccumulative, and toxic chemicals and it is therefore critical to increase our understanding on their adsorption, distribution, metabolism, excretion (ADME). The current study focused on uptake of seven emerging PFAS in zebrafish (Danio rerio) and their potential maternal transfer. In addition, we aimed at increasing our understanding on mixture effects on ADME by developing a physiologically based kinetic (PBK) model capable of handling co-exposure scenarios of any number of chemicals. All studied chemicals were taken up in the fish to varying degrees, whereas only perfluorononanoate (PFNA) and perfluorooctanoate (PFOA) were quantified in all analysed tissues. Perfluorooctane sulfonamide (FOSA) was measured at concerningly high concentrations in the brain (Cmax over 15 μg/g) but also in the liver and ovaries. All studied PFAS were maternally transferred to the eggs, with FOSA and 6:2 perfluorooctane sulfonate (6,2 FTSA) showing significant (p < 0.02) signs of elimination from the embryos during the first 6 days of development, while perfluorobutane sulfonate (PFBS), PFNA, and perfluorohexane sulfonate (PFHxS) were not eliminated in embryos during this time-frame. The mixture PBK model resulted in >85 % of predictions within a 10-fold error and 60 % of predictions within a 3-fold error. At studied levels of PFAS exposure, competitive binding was not a critical factor for PFAS kinetics. Gill surface pH influenced uptake for some carboxylates but not the sulfonates. The developed PBK model provides an important tool in understanding kinetics under complex mixture scenarios and this use of New Approach Methodologies (NAMs) is critical in future risk assessment of chemicals and early warning systems.
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Affiliation(s)
| | - Stefan Örn
- Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Ioana Chelcea
- Department of Chemistry, Umeå University, Umeå, Sweden
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Huang K, Zhou W, Fu J, Zhang Q, Teng Y, Gu L, Fu Y, Hu B, Mei Y, Zhang H, Zhang A, Fu J, Jiang G. Linking Transthyretin-Binding Chemicals and Free Thyroid Hormones: In Vitro to In Vivo Extrapolation Based on a Competitive Binding Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:9130-9139. [PMID: 37261382 DOI: 10.1021/acs.est.3c01094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88-32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.
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Affiliation(s)
- Kai Huang
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Wei Zhou
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Jie Fu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qun Zhang
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yunhe Teng
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Luyao Gu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yilin Fu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Boyuan Hu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yang Mei
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
| | - Haiyan Zhang
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
| | - Aiqian Zhang
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Jianjie Fu
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Guibin Jiang
- School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, China
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Blay V, Li X, Gerlach J, Urbina F, Ekins S. Combining DELs and machine learning for toxicology prediction. Drug Discov Today 2022; 27:103351. [PMID: 36096360 PMCID: PMC9995617 DOI: 10.1016/j.drudis.2022.103351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.
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Affiliation(s)
- Vincent Blay
- Department of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, CA 95064, USA.
| | - Xiaoyu Li
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jacob Gerlach
- Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc, 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA.
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