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Research Articles

In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations

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Pages 2211-2230 | Received 08 Nov 2022, Accepted 10 Apr 2023, Published online: 02 May 2023

References

  • Almutairi, T. M., Rezki, N., Aouad, M. R., Hagar, M., Bakr, B. A., Hamed, M. T., Hassen, M. K., Elwakil, B. H., & Moneer, E. A. (2022). Exploring the antiparasitic activity of tris-1,3,4-thiadiazoles against Toxoplasma gondii-infected mice. In. Molecules, 27(7), 2246. https://doi.org/10.3390/molecules27072246
  • Arora, S., Lohiya, G., Moharir, K., Shah, S., & Yende, S. (2020). Identification of potential flavonoid inhibitors of the SARS-CoV-2 main protease 6YNQ: A molecular docking study. Digital Chinese Medicine, 3(4), 239–248. https://doi.org/10.1016/j.dcmed.2020.12.003
  • Bhardwaj, V. K., Singh, R., Sharma, J., Rajendran, V., Purohit, R., & Kumar, S. (2021). Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors. Journal of Biomolecular Structure and Dynamics, 39(10), 3449–3458. https://doi.org/10.1080/07391102.2020.1766572
  • Biovia, D. S. (2016). Discovery studio modeling environment, release 2017, San Diego. Dassault Systèmes.
  • Blay, V., Dong, J., & Moya, A. (2021). Machine learning study of the molecular drivers of natural product prices. Biofuels, Bioproducts and Biorefining, 15(6), 1820–1834. https://doi.org/10.1002/bbb.2281
  • Caballero, J. (2020). Considerations for docking of selective angiotensin-converting enzyme inhibitors. Molecules, 25(2), 295. https://doi.org/10.3390/molecules25020295
  • Carli, N., Massarelli, I., & Bianucci, A. M. (2014). A new neural network (tiling-contextual neural network for structures, TC-NNfS) enabling the treatment of relatively small datasets of therapeutic interest: An application to a small dataset of ACE inhibitors. Chemometrics and Intelligent Laboratory Systems, 137, 1–9. https://doi.org/10.1016/j.chemolab.2014.06.001
  • Cozier, G. E., Schwager, S. L., Sharma, R. K., Chibale, K., Sturrock, E. D., & Acharya, K. R. (2018). Crystal structures of sampatrilat and sampatrilat-Asp in complex with human ACE – a molecular basis for domain selectivity. The FEBS Journal, 285(8), 1477–1490. https://doi.org/10.1111/febs.14421
  • Edraki, N., Das, U., Hemateenejad, B., Dimmock, J. R., & Miri, R. (2016). Comparative QSAR analysis of 3,5-bis (Arylidene)-4-piperidone derivatives: The development of predictive cytotoxicity models. Iranian Journal of Pharmaceutical Research : IJPR, 15(2), 425–437. https://pubmed.ncbi.nlm.nih.gov/27642313
  • Egieyeh, S. A., Syce, J., Malan, S. F., & Christoffels, A. (2016). Prioritization of anti-malarial hits from nature: Chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs. Malaria Journal, 15(1), 50. https://doi.org/10.1186/s12936-016-1087-y
  • El-Enany, W. A. M. A., Gomha, S. M., El-Ziaty, A. K., Hussein, W., Abdulla, M. M., Hassan, S. A., Sallam, H. A., & Ali, R. S. (2020). Synthesis and molecular docking of some new bis-thiadiazoles as anti-hypertensive α-blocking agents. Synthetic Communications, 50(1), 85–96. https://doi.org/10.1080/00397911.2019.1683207
  • Fienberg, S., Cozier, G. E., Acharya, K. R., Chibale, K., & Sturrock, E. D. (2018). The design and development of a potent and selective novel diprolyl derivative that binds to the N-domain of angiotensin-I converting enzyme. Journal of Medicinal Chemistry, 61(1), 344–359. https://doi.org/10.1021/acs.jmedchem.7b01478
  • Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1998). Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: An analysis of ALOGP and CLOGP methods. The Journal of Physical Chemistry A, 102(21), 3762–3772. https://doi.org/10.1021/jp980230o
  • Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17(2-4), 241–253. https://doi.org/10.1023/A:1025386326946
  • Gonzalez Amaya, J. A., Cabrera, D. Z., Matallana, A. M., Arevalo, K. G., & Guevara-Pulido, J. (2020). In-silico design of new enalapril analogs (ACE inhibitors) using QSAR and molecular docking models. Informatics in Medicine Unlocked, 19, 100336. https://doi.org/10.1016/j.imu.2020.100336
  • Gramatica, P. (2014). External evaluation of QSAR models, in addition to cross-validation: Verification of predictive capability on totally new chemicals. Molecular Informatics, 33(4), 311–314. https://doi.org/10.1002/minf.201400030
  • Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. Journal of Computational Chemistry, 34(24), 2121–2132. https://doi.org/10.1002/jcc.23361
  • Hadaji, E., Bouachrine, M., El Hamdani, H., & Ouammou, A. (2022). QSAR and molecular docking study of quinolin derivatives with topoisomerase I inhibitory properties as potential anticancer agents using statistical methods. Materials Today: Proceedings, 51, 1838–1850. https://doi.org/10.1016/j.matpr.2020.08.032
  • Harder, E., Damm, W., Maple, J., Wu, C., Reboul, M., Xiang, J. Y., Wang, L., Lupyan, D., Dahlgren, M. K., Knight, J. L., Kaus, J. W., Cerutti, D. S., Krilov, G., Jorgensen, W. L., Abel, R., & Friesner, R. A. (2016). OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. Journal of Chemical Theory and Computation, 12(1), 281–296. https://doi.org/10.1021/acs.jctc.5b00864
  • Hollas, B. (2003). An analysis of the autocorrelation descriptor for molecules. Journal of Mathematical Chemistry, 33(2), 91–101. https://doi.org/10.1023/A:1023247831238
  • Hu, Y., Li, C.-Y., Wang, X.-M., Yang, Y.-H., & Zhu, H.-L. (2014). 1,3,4-thiadiazole: Synthesis, reactions, and applications in medicinal, agricultural, and materials chemistry. Chemical Reviews, 114(10), 5572–5610. https://doi.org/10.1021/cr400131u
  • Jain, A. K., Sharma, S., Vaidya, A., Ravichandran, V., & Agrawal, R. K. (2013). 1,3,4-Thiadiazole and its derivatives: A review on recent progress in biological activities. Chemical Biology & Drug Design, 81(5), 557–576. https://doi.org/10.1111/cbdd.12125
  • Jawarkar, R. D., Bakal, R. L., Zaki, M. E. A., Al-Hussain, S., Ghosh, A., Gandhi, A., Mukerjee, N., Samad, A., Masand, V. H., & Lewaa, I. (2022). QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches. Arabian Journal of Chemistry, 15(1), 103499. https://doi.org/10.1016/j.arabjc.2021.103499
  • Jawarkar, R. D., Sharma, P., Jain, N., Gandhi, A., Mukerjee, N., Al-Mutairi, A. A., Zaki, M. E. A., Al-Hussain, S. A., Samad, A., Masand, V. H., Ghosh, A., & Bakal, R. L. (2022). QSAR, molecular docking, MD simulation and MMGBSA calculations approaches to recognize concealed pharmacophoric features requisite for the optimization of ALK tyrosine kinase inhibitors as anticancer leads. Molecules, 27(15), 4951. https://doi.org/10.3390/molecules27154951
  • Karadžić, M. Ž., Jevrić, L. R., Mandić, A. I., Markov, S. L., Podunavac-Kuzmanović, S. O., Kovačević, S. Z., Nikolić, A. R., Oklješa, A. M., Sakač, M. N., & Penov-Gaši, K. M. (2017). Chemometrics approach based on chromatographic behavior, in silico characterization and molecular docking study of steroid analogs with biomedical importance. European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences, 105, 71–81. https://doi.org/10.1016/j.ejps.2017.05.004
  • Khan, M. A. H., & Imig, J. D. B. T.-R. M. (2018). Antihypertensive drugs. Elsevier. https://doi.org/10.1016/B978-0-12-801238-3.96704-7
  • Khan, K., Roy, K., & Benfenati, E. (2019). Ecotoxicological QSAR modeling of endocrine disruptor chemicals. Journal of Hazardous Materials, 369, 707–718. https://doi.org/10.1016/j.jhazmat.2019.02.019
  • Kumar, V., De, P., Ojha, P. K., Saha, A., & Roy, K. (2020). A multi-layered variable selection strategy for QSAR modeling of butyrylcholinesterase inhibitors. Current Topics in Medicinal Chemistry, 20(18), 1601–1627. https://doi.org/10.2174/1568026620666200616142753
  • Kumar, S., Nair, A. S., Bhashkar, V., Sudevan, S. T., Koyiparambath, V. P., Khames, A., Abdelgawad, M. A., & Mathew, B. (2021). Navigating into the chemical space of monoamine oxidase inhibitors by artificial intelligence and cheminformatics approach. ACS Omega, 6(36), 23399–23411. https://doi.org/10.1021/acsomega.1c03250
  • L DeLano, W. (2002). Pymol: An open-source molecular graphics tool. {CCP4} Newsletter on Protein Crystallography.
  • Masand, V. H., El-Sayed, N. N. E., Mahajan, D. T., Mercader, A. G., Alafeefy, A. M., & Shibi, I. G. (2017). QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines. Journal of Molecular Structure, 1130, 711–718. https://doi.org/10.1016/j.molstruc.2016.11.012
  • Masand, V. H., & Rastija, V. (2017). PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors. Chemometrics and Intelligent Laboratory Systems, 169, 12–18. https://doi.org/10.1016/j.chemolab.2017.08.003
  • McMurray, J. J. V. (2011). CONSENSUS to EMPHASIS: The overwhelming evidence which makes blockade of the renin–angiotensin–aldosterone system the cornerstone of therapy for systolic heart failure. European Journal of Heart Failure, 13(9), 929–936. https://doi.org/10.1093/eurjhf/hfr093
  • Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., De Ferranti, S., Després, J. P., Fullerton, H. J., Howard, V. J., Huffman, M. D., Isasi, C. R., Jiménez, M. C., Judd, S. E., Kissela, B. M., Lichtman, J. H., Lisabeth, L. D., Liu, S., … Turner, M. B. (2016). Heart Disease and Stroke Statistics—2016 Update. Circulation, 133(4), e38–e360. https://doi.org/10.1161/CIR.0000000000000350
  • Olasupo, S. B., Uzairu, A., Shallangwa, G., & Uba, S. (2019). QSAR analysis and molecular docking simulation of norepinephrine transporter (NET) inhibitors as anti-psychotic therapeutic agents. Heliyon, 5(10), e02640. https://doi.org/10.1016/j.heliyon.2019.e02640
  • Palakkeezhillam, V. N. V., Haribabu, J., Manakkadan, V., Rasin, P., Varughese, R. E., Gayathri, D., Bhuvanesh, N., Echeverria, C., & Sreekanth, A. (2023). Synthesis, spectroscopic characterizations, single crystal X-ray analysis, DFT calculations, in vitro biological evaluation and in silico evaluation studies of thiosemicarbazones based 1,3,4-thiadiazoles. Journal of Molecular Structure, 1273, 134309. https://doi.org/10.1016/j.molstruc.2022.134309
  • Politi, A., Durdagi, S., Moutevelis-Minakakis, P., Kokotos, G., Papadopoulos, M. G., & Mavromoustakos, T. (2009). Application of 3D QSAR CoMFA/CoMSIA and in silico docking studies on novel renin inhibitors against cardiovascular diseases. European Journal of Medicinal Chemistry, 44(9), 3703–3711. https://doi.org/10.1016/j.ejmech.2009.03.040
  • Przybyłek, M. (2020). Application 2D descriptors and artificial neural networks for beta-glucosidase inhibitors screening. Molecules, 25(24), 5942. https://doi.org/10.3390/molecules25245942
  • Regulska, K., Stanisz, B., Regulski, M., & Murias, M. (2014). How to design a potent, specific, and stable angiotensin-converting enzyme inhibitor. Drug Discovery Today, 19(11), 1731–1743. https://doi.org/10.1016/j.drudis.2014.06.026
  • Roy, K., Ambure, P., & Aher, R. B. (2017). How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemometrics and Intelligent Laboratory Systems, 162, 44–54. https://doi.org/10.1016/j.chemolab.2017.01.010
  • Roy, K., Kar, S., & Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems, 145, 22–29. https://doi.org/10.1016/j.chemolab.2015.04.013
  • Sagardia, I., Roa-Ureta, R. H., & Bald, C. (2013). A new QSAR model, for angiotensin I-converting enzyme inhibitory oligopeptides. Food Chemistry, 136(3-4), 1370–1376. https://doi.org/10.1016/j.foodchem.2012.09.092
  • Sander, T., Freyss, J., von Korff, M., & Rufener, C. (2015). DataWarrior: An open-source program for chemistry aware data visualization and analysis. Journal of Chemical Information and Modeling, 55(2), 460–473. https://doi.org/10.1021/ci500588j
  • Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020). Towards reproducible computational drug discovery. Journal of Cheminformatics, 12(1), 9. https://doi.org/10.1186/s13321-020-0408-x
  • Shah, S., Arora, S., Chaple, D., Badne, P., Yende, S., Khonde, S., & Deshmukh, S. (2023). 2D-QSAR modeling of chalcone analogues as angiotensin converting enzyme inhibitor. Biointerface Research in Applied Chemistry, 13(4), 1–23.
  • Shah, S. K., & Chaple, D. R. (2021). 2D-QSAR modeling of quinazolinone derivatives as angiotensin II Type 1a receptor blockers. International Journal of Quantitative Structure-Property Relationships, 7(2), 1–20. https://doi.org/10.4018/IJQSPR.290012
  • Shah, S., Chaple, D., Arora, S., Yende, S., Moharir, K., & Lohiya, G. (2021). Exploring the active constituents of Oroxylum indicum in intervention of novel coronavirus (COVID-19) based on molecular docking method. Network Modeling Analysis in Health Informatics and Bioinformatics, 10(1), 8. https://doi.org/10.1007/s13721-020-00279-y
  • Stoičkov, V., Šarić, S., Golubović, M., Zlatanović, D., Krtinić, D., Dinić, L., Mladenović, B., Sokolović, D., & Veselinović, A. M. (2018). Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach. SAR and QSAR in Environmental Research, 29(7), 503–515. https://doi.org/10.1080/1062936X.2018.1485737
  • Stumpfe, D., Hu, H., & Bajorath, J. (2019). Evolving concept of activity cliffs. ACS Omega, 4(11), 14360–14368. https://doi.org/10.1021/acsomega.9b02221
  • Tetko, I. V., Tanchuk, V. Y., & Villa, A. E. P. (2001). Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and e-state indices. Journal of Chemical Information and Computer Sciences, 41(5), 1407–1421. https://doi.org/10.1021/ci010368v
  • Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 29(6–7), 476–488. https://doi.org/10.1002/minf.201000061
  • Turner, S., Myers, M., Gadie, B., Nelson, A. J., Pape, R., Saville, J. F., Doxey, J. C., & Berridge, T. L. (1988). Antihypertensive thiadiazoles. 1. Synthesis of some 2-aryl-5-hydrazino-1,3,4-thiadiazoles with vasodilator activity. Journal of Medicinal Chemistry, 31(5), 902–906. https://doi.org/10.1021/jm00400a003
  • Victoria-Muñoz, F., Sánchez-Cruz, N., Medina-Franco, J. L., & Lopez-Vallejo, F. (2022). Cheminformatics analysis of molecular datasets of transcription factors associated with quorum sensing in Pseudomonas aeruginosa. RSC Advances, 12(11), 6783–6790. https://doi.org/10.1039/D1RA08352J
  • Wallace, A. C., Laskowski, R. A., & Thornton, J. M. (1995). LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions. Protein Engineering, 8(2), 127–134. https://doi.org/10.1093/protein/8.2.127
  • Yan, W., Lin, G., Zhang, R., Liang, Z., Wu, L., & Wu, W. (2020). Studies on molecular mechanism between ACE and inhibitory peptides in different bioactivities by 3D-QSAR and MD simulations. Journal of Molecular Liquids, 304, 112702. https://doi.org/10.1016/j.molliq.2020.112702
  • Yap, C. W. (2011). PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466–1474. https://doi.org/10.1002/jcc.21707
  • Yunta, M. (2017). It is important to compute intramolecular hydrogen bonding in drug design? American Journal of Modelling and Optimization, 5, 24–57. https://doi.org/10.12691/ajmo-5-1-3
  • Zheng, W., Tian, E., Liu, Z., Zhou, C., Yang, P., Tian, K., Liao, W., Li, J., & Ren, C. (2022). Small molecule angiotensin converting enzyme inhibitors: A medicinal chemistry perspective. Frontiers in Pharmacology, 13, 968104. https://www.frontiersin.org/articles/10 .3389/fphar.2022.968104 https://doi.org/10.3389/fphar.2022.968104

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