Docking Simulations and Virtual Screening to find Novel Ligands for T3S in Yersinia pseudotuberculosis YPIII, A drug target for type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis

Document Type : Research Article

Authors

1 Department of Pure and Applied Chemistry, University of Maiduguri, Brono State, Nigeria

2 Chemistry, Physical, Ahamdu Bello University, Zaria, Nigeria

3 Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.

4 Chemistry, Physical sciences, ABU, Zaria, Nigeria.

Abstract

In the Gram-negative pathogen Yersinia pseudotuberculosis and Chlamydia, the aggregate use of molecular docking, molecular dynamic simulations, and ADMET was successfully used to develop salicylidene acyl hydrazides as type III secretion (T3S) inhibitors. The molecular docking analysis was carried out on CMP's simulated protein, which helped to correlate amino acid associations with the ligand. The review of molecular dynamics simulations showed that the CMP protein A-chain was stable at and above 100ps concerning temperature, total energy, and kinetic energy. Virtual screening was performed to distinguish the new inhibitors depending on pharmacophore modeling and molecular docking. Based on the Rerank score fitness feature, ten top-ranked compounds were discovered. In keeping with the reference ranges, ADME tests were carried out on compounds retrieved from simulated sampling. For our further drug design, all the findings will give us more useful evidence.

Keywords

Main Subjects

References

  • H.O. Multidrug and extensively drug-resistant tuberculosis: Global report on surveillance and response. World Health Organization; Geneva, Switzerland (2010).
  • J. Prado-Prado, E. Uriarte, F. Borges, H. Gonza´ lez-Dı´az, Multi-target spectral moments for QSAR and Complex Networks study of antibacterial drugs. European Journal of Medicinal Chemistry, 44 (2009) 4516–4521.
  • J. Walker, E. A. Clark, D. C. Ford, H. L. Bullifent, E. V. McAlister, M. L. Duffield, K. R. Acharya, P. C. Oyston, Structure and function of cytidine monophosphate kinase from Yersinia pseudotuberculosis, essential for virulence but not for survival. 2(12) (2012) 120142. doi: 10.1098/rsob.120142 DOI: 10.1098/rsob.120142.
  • Achtman, K. Zurth, C. Morelli, G. Torrea, A. Guiyoule, E. Carniel, Yersinia pestis, the cause of plague, is a recently emerged clone of Yersinia pseudotuberculosis. Proc. Natl Acad. Sci., 96 (1999) 14043–14048. doi:10.1073/pnas.96.24.14043.
  • Kumar, A. K. Abbas, N. Fausto, R. N. Mitchell, Robbins Basic Pathology, 8th ed.; Saunders Elsevier: Philadelphia, PA (2007).
  • medicalnewstoday.com
  • Centers for Disease Control and Prevention MMWR Morbidity Mortality Weekly Report, CDC, 2015. Sexually Transmitted Diseases Treatment Guidelines, 2015 Recommendations and Reports Vol. 64 (3), June 5.
  • B. Rice, Unmet medical needs in antibacterial therapy. Biochem Pharmacol., 71(7) (2006) 991–995.
  • A. Siadati, N. Nami, & M. R. Zardoost, A DFT study of solvent effects on the cycloaddition of norbornadiene and 3,4-dihydroisoquinoline-N-oxide. Progress in Reaction Kinetics and Mechanism, 36(3) (2011) 252-258.‏
  • R. Zardoost, & S. A. Siadati, A DFT study on the effect of functional groups on the formation kinetics of 1,2,3-triazolo-1,4-benzoxazine via intramolecular 1,3-dipolar cycloaddition. Progress in Reaction Kinetics and Mechanism, 38(2) (2013) 191-196.‏
  • R. Zardoost, S. A. Siadati, & B. G. Oghani, A DFT study on the 1,3‑dipolar cycloaddition of benzonitrile oxide and N‑ethylmaleimide. Progress in Reaction Kinetics and Mechanism, 38(3) (2013) 316-322.‏
  • I. Edache, A. Uzairu, P. A. P. Mamza and G. A. Shallengwa, Prediction of HemO Inhibitors Based on Iminoguanidine using QSAR, 3DQSAR Study, Molecular Docking, Molecular Dynamic Simulation and ADMET. J Drug Design Discov Res, 1(2) (2020) 36-52.
  • I. Edache, A. Uzairu, P. A. Mamza and G. A. Shallengwa, A comparative QSAR analysis, 3D-QSAR, molecular docking and molecular design of iminoguanidine-based inhibitors of HemO: A rational approach to antibacterial drug design. J. Drugs Pharm. Sci., 4(3) (2020) 21-36.
  • A. Siadati, M. Afzali, & M. Sayyadi, Could silver nano-particles control the 2019-nCoV virus? An urgent glance to the past. Chem. Rev. Lett., 3(1) (2020) 9-11.
  • A. Siadati, M. A. Rezvanfar, E. Babanezhad, A. Beheshti, & M. Payab, Harmony of operations of some vitamins in controlling the 2019-nCoV virus based on scientific reports, Chem. Rev. Lett., 3 (2020) 202-206.
  • Turski, in Virtual ADMET Assessment in Target Selection and Maturation (Eds.: B. Testa, L. Turski) IOS Press, (2006) Washington, DC.
  • Abbasi, F. Ramezani, M. Elyasi, H. Sadeghi-Aliabadi, M. Amanlou, A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold. DARU Journal of Pharmaceutical Sciences. 23(1): (2015) 1–10.
  • Hansch, A. Kurup, R. Garg, H. Gao, Chem-bioinformatics and QSAR: a review of QSAR lacking positive hydrophobic terms. Chem. Rev. 101(3): (2001) 619–72.
  • T. Kroemer, Structure-based drug design: docking and scoring. Current Protein and Peptide Science, 8(4): (2007) 312–28.
  • G. Ferreira, R. N. dos Santos, G. Oliva, A. D. Andricopulo, Molecular Docking and Structure-Based Drug Design Strategies. Molecules, 20 (2015) 13384-13421; https://doi: 10.3390/molecules200713384.
  • Kalyaanamoorthy, Y. P. Chen, Structure-based drug design to augment hit discovery. Drug Discov. Today, 16 (2011) 831–839.
  • Segall, Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr. Pharm. Des., 18 (2012) 1292.
  • A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 23: (1997) 3–25.
  • F. Veber, S. R. Johnson, H. Y. Cheng, B. R. Smith, K. W. Ward, K. D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 45 (2002) 2615–2623.
  • T. Wager, X. Hou, P. R. Verhoest, A. Villalobos, Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem. Neurosci., 1 (2010) 435–449.
  • W. Johnson, K. R. Dress, M. Edwards, Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett., 19 (2009) 5560–5564.
  • Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E. E. Bolton, PubChem update: improved access to chemical data. Nucleic Acids Res., 47(D1) (2019) D1102-1109. doi:10.1093/nar/gky1033. [PubMed PMID: 30371825].
  • National Center for Biotechnology Information. PubChem Database. Source=ChEMBL, AID=473049, https://pubchem.ncbi.nlm.nih.gov/bioassay/473049 (accessed on July. 07, 2019).
  • A. Clark, K. R. Acharya, Structure of cytidine monophosphate kinase from Yersinia pseudotuberculosis (2012). DOI: 10.2210/pdb4e22/pdb Primary publication DOI: 10.1098/rsob.120142.
  • C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kale, K. Schulten, Scalable Molecular Dynamics with NAMD. J. Comput. Chem., 26(16) (2005) 1781-1802.
  • P. Allen, D. J. Tildesley, Computer Simulations of Liquids. Clarendon Press: (1987) Oxford.
  • Wavefunction, Inc. Spartan’14, (2014) version 1.1.4, Irvine, California, USA. wavefun.com.
  • W. Yap, PaDel –Descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32(7) (2011) 1466-1474.
  • Ray, P. B. Madrid, P. Catz, E. Susanna, L. Valley, M. J. Furniss, Development of a new generation of 4-aminoquinoline antimalarial compounds using predictive pharmacokinetic and toxicology models. Journal of Medicinal Chemistry, 53 (2010) 3685–3695.
  • Kusumaningrum, E. Budianto, S. Kosela, W. Sumaryono, F. Juniarti, The molecular docking of 1,4-naphthoquinone derivatives as inhibitors of Polo-like kinase 1 using Molegro Virtual Docker. J. App. Pharm. Sci., 4 (11) (2014) 047-053.
  • R. Puspaningtyas, Docking studies of Physalis peruviana ethanol extract using molegro virtual docker on insulin tyrosine kinase receptor as antidiabetic agent. International Current Pharmaceutical Journal, 3(5) (2014) 265-269.
  • Liu, J. Feng, S. S. Young, PowerMV: A Software Enviroment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation. J. Chem. Inf. Model., 45: (2005) 515-522.
  • MedChem Designer™ version 3.1.0130. simulations-plus.com
  • Artursson, J. Karlsson, Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells, Biochem Biophys Res Commun., 175 (1991) 880–885.
  • Rouane, N. Tchouar, A. Kerassa, S. Belaidi, M. Cinar, Structure-Based Virtual Screening and Drug-Like of Quercetin Derivatives with Anti-Malaria Activity. Reviews in Theoretical Science, 5 (2017) 1–11. doi:10.1166/rits.2017.1090.
  • Haddadi, H. Meghezzi, A. Amar, DFT and QSAR investigations of substituent effects in pyrazolooxazine derivatives: Activity prediction. Journal of Theoretical and Computational Chemistry, 18 (2019) 1950001. https://doi:10.1142/S0219633619500019.
  • Pajouhesh, G. R. Lenz, Medicinal chemical properties of successful central nervous system drugs. Neuro Rx. 2 (2005) 541–553.
  • Salah, S. Belaidi, N. Melkemi, N. Tchouar, Molecular Geometry, Electronic Properties, MPO Methods and Structure Activity/Property Relationship Studies of 1,3,4-Thiadiazole Derivatives by Theoretical Calculations. Reviews in Theoretical Science, 3 (2015) 1–10.
  • Atkinson, S. Cole, C. Green, H. Van de Waterbeemd, Lipophilicity and other parameters affecting brain penetration. Cent. Nerv. Syst. Agents. Med. Chem., 2 (2002) 229–240.
  • Van de Waterbeemd, G. Camenisch, G. Folkers, J. R. Chretien, O. A. Raevsky, Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J. Drug Target, 6 (1998) 151–165.
  • Bodor, C. K. Shim, A new method for the estimation of partition coefficient, J. Am. Chem. Soc., 111 (1989) 3783–3786.
  • Ertl, B. Rohde, P. Selzer, Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties, J. Med. Chem., 43 (2000) 3714–3717.
  • N. Viswanadhan, A. K. Ghose, G. R. Revankar, R. K. Robins, Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships.4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics, J. Chem. Inf. Comput., 29 (1989) 163–172.
  • H. Zhao, M. H. Abraham, J. Le, A. Hersey, C. N. Luscombe, G. Beck, I. Cooper, Rate-limited steps of human oral absorption and QSAR studies. Pharmacol. Res., 19 (2002) 1446–1457.
  • M. Keserü, G. M. Makara, The influence of lead discovery strategies on the properties of drug candidates. Nat. Rev. Drug Discov., 8 (2009) 203–212.
  • L. Hopkins, C. R. Groom, A. Alex, Ligand efficiency: a useful metric for lead selection. Drug Discov. Today, 9 (2004) 430–431.
  • A. Smith, B. C. Jones, D. K. Walker, Design of drugs involving the concepts and theories of drug metabolism and pharmacokinetics. Med. Res. Rev., 16 (1996) 243–266.
  • D. Leeson, B. Springthorpe, The influence of drug-like concepts on decision making in medicinal chemistry. Nat. Rev. Drug Discov., 6 (2007) 881–890.
  • Ryckmans, M. P. Edwards, V. A. Horne, A. M. Correia, D. R. Owen, L. R. Thompson, I. Tran, M. F. Tutt, T. Young, Rapid assessment of a novel series of selective CB (2) agonists using parallel synthesis protocols: A lipophilic efficiency (LipE) analysis, Bioorg. Med. Chem. Lett., 19 (2009) 4406–4409.

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History

  • Receive Date: 29 October 2020
  • Revise Date: 03 February 2021
  • Accept Date: 11 February 2021

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