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Bahia MS, Kaspi O, Touitou M, Binayev I, Dhail S, Spiegel J, Khazanov N, Yosipof A, Senderowitz H. A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations. Mol Inform 2023; 42:e2200186. [PMID: 36617991 DOI: 10.1002/minf.202200186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/10/2023]
Abstract
QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.
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Affiliation(s)
| | - Omer Kaspi
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Meir Touitou
- School of Cancer and Pharmaceutical Sciences, King's College London, London, 150 Stamford Street, SE1 9NH, United Kingdom
| | - Idan Binayev
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Seema Dhail
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Jacob Spiegel
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Netaly Khazanov
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Abraham Yosipof
- Department of Information Systems, College of Law & Business, Ramat-Gan, P.O. Box 852, Bnei Brak, 5110801, Israel
| | - Hanoch Senderowitz
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
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2
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Banerjee A, De P, Kumar V, Kar S, Roy K. Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. CHEMOSPHERE 2022; 309:136579. [PMID: 36174732 DOI: 10.1016/j.chemosphere.2022.136579] [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: 03/07/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Zarei O, Raeppel SL, Hamzeh-Mivehroud M. An alignment-independent three-dimensional quantitative structure-activity relationship study on ron receptor tyrosine kinase inhibitors. J Bioinform Comput Biol 2022; 20:2250015. [PMID: 35880255 DOI: 10.1142/s0219720022500159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.
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Affiliation(s)
- Omid Zarei
- Cellular and Molecular Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Stéphane L Raeppel
- ChemRF Laboratories Inc., 3194, rue Claude-Jodoin, Montréal, QC, Canada H1Y 3M2, Canada
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Ferreira GM, Magalhães JGD, Maltarollo VG, Kronenberger T, Ganesan A, Emery FDS, Trossini GHG. QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives. J Biomol Struct Dyn 2019; 38:354-363. [PMID: 30789810 DOI: 10.1080/07391102.2019.1574603] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure-activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q2 and r2pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Glaucio Monteiro Ferreira
- Department of Pharmacy, Faculty of Pharmaceutical Sciences, University of Sao Paulo (USP), Sao Paulo, SP, Brazil
| | | | - Vinícius Gonçalves Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Internal Medicine VIII, University Hospital Tübingen, Tübingen, Germany
| | - Arasu Ganesan
- School of Pharmacy, University of East Anglia, Norwich, UK
| | - Flávio da Silva Emery
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
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Wang Y, Khan A, Chandra Kaushik A, Junaid M, Zhang X, Wei DQ. The systematic modeling studies and free energy calculations of the phenazine compounds as anti-tuberculosis agents. J Biomol Struct Dyn 2018; 37:4051-4069. [PMID: 30332914 DOI: 10.1080/07391102.2018.1537896] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Phenazine compounds have good activity against Mycobacterium tuberculosis (MTB). Based on the reported activities that were obtained in MTB H37Rv, a three-dimensional quantitative structure-activity relationship (3D-QSAR) model was built to design novel compounds against MTB. A fivefold cross-validation method and external validation were used to analyze the accuracy of forecasting. The model has a cross-validation coefficient q2=0.7 and a non-cross-validation coefficient r2 = 0.903, indicating that the model has good predictive possibility. The design of anti-pneumococcus MTB compounds was guided by the obtained 3D-QSAR model, and several compounds with better activity were obtained. To test the activity of these compounds, molecular docking, molecular dynamics simulation, and post-simulation analysis of the already reported drug targets in MTB were carried out. Among the total 15 drug targets, only three targets (Rv2361c, Rv2965c, and Rv3048c) were selected based on the docking results. Initial results reported that these compounds possessed good inhibition activity for Rv2361c. The top nine complexes of Rv2361 ligands were only subjected to MD simulation which resulted in a stable dynamics of the structures and showed a residual fluctuation in inhibitors binding pocket. Free energy reported that overall, the derivatives hold strong energy against the protein target. Energetic contribution results showed that residues, Asp76, Arg80, Asn124, Arg127, Arg244, and Arg250, play a major role in total energy. Systems biology approach validates shortlisted drug effect on the entire system which might be useful to predict potential drug in wet lab as well. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yueqi Wang
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
| | - Abbas Khan
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
| | - Aman Chandra Kaushik
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
| | - Muhammad Junaid
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
| | - Xuehong Zhang
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
| | - Dong-Qing Wei
- a State Key Laboratory of Microbial Metabolism & School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai , P. R. China
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Sharifi M, Buzatu D, Harris S, Wilkes J. Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks. BMC Bioinformatics 2017; 18:497. [PMID: 29297274 PMCID: PMC5751783 DOI: 10.1186/s12859-017-1895-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. Results An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. Conclusion A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs. Electronic supplementary material The online version of this article (10.1186/s12859-017-1895-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohsen Sharifi
- Division of Systems Biology, FDA's National Center for Toxicological Research, Jefferson, AR, 72079, USA
| | - Dan Buzatu
- Division of Systems Biology, FDA's National Center for Toxicological Research, Jefferson, AR, 72079, USA.
| | - Stephen Harris
- Division of Systems Biology, FDA's National Center for Toxicological Research, Jefferson, AR, 72079, USA
| | - Jon Wilkes
- Division of Systems Biology, FDA's National Center for Toxicological Research, Jefferson, AR, 72079, USA
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Jafari B, Hamzeh-Mivehroud M, Alizadeh AA, Sharifi M, Dastmalchi S. An Alignment-Independent 3D-QSAR Study of FGFR2 Tyrosine Kinase Inhibitors. Adv Pharm Bull 2017; 7:409-418. [PMID: 29071223 PMCID: PMC5651062 DOI: 10.15171/apb.2017.049] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 08/08/2017] [Accepted: 08/12/2017] [Indexed: 01/09/2023] Open
Abstract
Purpose: Receptor tyrosine kinase (RTK) inhibitors are widely used pharmaceuticals in cancer therapy. Fibroblast growth factor receptors (FGFRs) are members of RTK superfamily which are highly expressed on the surface of carcinoma associate fibroblasts (CAFs). The involvement of FGFRs in different types of cancer makes them promising target in cancer therapy and hence, the identification of novel FGFR inhibitors is of great interest. In the current study we aimed to develop an alignment independent three dimensional quantitative structure-activity relationship (3D-QSAR) model for a set of 26 FGFR2 kinase inhibitors allowing the prediction of activity and identification of important structural features for these inhibitors. Methods: Pentacle software was used to calculate grid independent descriptors (GRIND) for the active conformers generated by docking followed by the selection of significant variables using fractional factorial design (FFD). The partial least squares (PLS) model generated based on the remaining descriptors was assessed by internal and external validation methods. Results: Six variables were identified as the most important probes-interacting descriptors with high impact on the biological activity of the compounds. Internal and external validations were lead to good statistical parameters (r2 values of 0.93 and 0.665, respectively). Conclusion: The results showed that the model has good predictive power and may be used for designing novel FGFR2 inhibitors.
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Affiliation(s)
- Behzad Jafari
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Students Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Akbar Alizadeh
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehdi Sharifi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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