Molecular design of hydroxamic acid-based derivatives as urease inhibitors of Helicobacter pylori.

Helicobacter pylori Chemical space Machine learning Molecular dynamics Urease inhibitor

Journal

Molecular diversity
ISSN: 1573-501X
Titre abrégé: Mol Divers
Pays: Netherlands
ID NLM: 9516534

Informations de publication

Date de publication:
17 Jul 2024
Historique:
received: 27 04 2024
accepted: 08 06 2024
medline: 18 7 2024
pubmed: 18 7 2024
entrez: 17 7 2024
Statut: aheadofprint

Résumé

Helicobacter pylori is the main causative agent of gastric cancer, especially non-cardiac gastric cancers. This bacterium relies on urease producing much ammonia to colonize the host. Herein, the study provides valuable insights into structural patterns driving urease inhibition for high-activity molecules designed via exploring known inhibitors. Firstly, an ensemble model was devised to predict the inhibitory activity of novel compounds in an automated workflow (R

Identifiants

pubmed: 39020133
doi: 10.1007/s11030-024-10914-9
pii: 10.1007/s11030-024-10914-9
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Références

Thrift AP, El-Serag HB (2020) Burden of gastric cancer. Clin Gastroenterol Hepatol 18:534–542. https://doi.org/10.1016/j.cgh.2019.07.045
doi: 10.1016/j.cgh.2019.07.045 pubmed: 31362118
Yu Y, Zhu S, Li P et al (2018) Helicobacter pylori infection and inflammatory bowel disease: a crosstalk between upper and lower digestive tract. Cell Death Dis 9:961. https://doi.org/10.1038/s41419-018-0982-2
doi: 10.1038/s41419-018-0982-2 pubmed: 30237392
Mobley HLT, Island MD, Hausinger RP (1995) Molecular biology of microbial ureases. Microbiol Rev. https://doi.org/10.1128/mr.59.3.451-480.1995
doi: 10.1128/mr.59.3.451-480.1995 pubmed: 7565414
Debraekeleer A, Remaut H (2018) Future perspective for potential Helicobacter pylori eradication therapies. Future Microbiol 13:671–687. https://doi.org/10.2217/fmb-2017-0115
doi: 10.2217/fmb-2017-0115 pubmed: 29798689
Mugita Y, Nakagami G, Minematsu T et al (2020) Combination of urease inhibitor and antiseptic inhibits urea decomposition-induced ammonia production by Proteus mirabilis. Int Wound J 17:1558–1565. https://doi.org/10.1111/iwj.13422
doi: 10.1111/iwj.13422 pubmed: 32851777
Rekowski A, Wimmer MA, Hitzmann B et al (2020) Application of urease inhibitor improves protein composition and bread-baking quality of urea fertilized winter wheat. J Plant Nutr Soil Sci 183:260–270. https://doi.org/10.1002/jpln.201900529
doi: 10.1002/jpln.201900529
Goos RJ (2018) Evaluation of nutrisphere-N as an inhibitor of urease in soils with pH values near five. Soil Science Soc of Amer J 82:1568–1571. https://doi.org/10.2136/sssaj2018.07.0258
doi: 10.2136/sssaj2018.07.0258
Covacci A, Telford JL, Giudice GD et al (1999) Helicobacter pylori virulence and genetic geography. Science 284:1328–1333. https://doi.org/10.1126/science.284.5418.1328
doi: 10.1126/science.284.5418.1328 pubmed: 10334982
Montecucco C, Rappuoli R (2001) Living dangerously: how Helicobacter pylori survives in the human stomach. Nat Rev Mol Cell Biol 2:457–466. https://doi.org/10.1038/35073084
doi: 10.1038/35073084 pubmed: 11389469
Kim SE, Park MI, Park SJ et al (2014) The trend in Helicobacter pylori eradication rates by first-line triple therapy and related factors in eradication therapy. Gastroenterology 146:391. https://doi.org/10.1016/S0016-5085(14)61404-9
doi: 10.1016/S0016-5085(14)61404-9
Suzuki S, Esaki M, Kusano C et al (2019) Development of Helicobacter pylori treatment: how do we manage antimicrobial resistance? WJG 25:1907–1912. https://doi.org/10.3748/wjg.v25.i16.1907
doi: 10.3748/wjg.v25.i16.1907 pubmed: 31086459
Mustafa YF (2023) Modern developments in the application and function of metal/metal oxide nanocomposite-based antibacterial agents. BioNanoScience 13:840–852. https://doi.org/10.1007/s12668-023-01100-6
doi: 10.1007/s12668-023-01100-6
Rego YF, Queiroz MP, Brito TO et al (2018) A review on the development of urease inhibitors as antimicrobial agents against pathogenic bacteria. J Adv Res 13:69–100. https://doi.org/10.1016/j.jare.2018.05.003
doi: 10.1016/j.jare.2018.05.003 pubmed: 30094084
Kafarski P, Talma M (2018) Recent advances in design of new urease inhibitors: a review. J Adv Res 13:101–112. https://doi.org/10.1016/j.jare.2018.01.007
doi: 10.1016/j.jare.2018.01.007 pubmed: 30094085
Kosikowska P, Berlicki Ł (2011) Urease inhibitors as potential drugs for gastric and urinary tract infections: a patent review. Expert Opin Ther Pat 21:945–957. https://doi.org/10.1517/13543776.2011.574615
doi: 10.1517/13543776.2011.574615 pubmed: 21457123
Habala L, Devínsky F, Egger AE (2018) Review: metal complexes as urease inhibitors. J Coord Chem 71:907–940. https://doi.org/10.1080/00958972.2018.1458228
doi: 10.1080/00958972.2018.1458228
Krajewska B (2009) Ureases I. Functional, catalytic and kinetic properties: a review. J Mol Catal B Enzym 59:9–21. https://doi.org/10.1016/j.molcatb.2009.01.003
doi: 10.1016/j.molcatb.2009.01.003
Sun W, Luo L, Luo J et al (2023) Phytoconstituents of Selaginella effusa alston and their α -glucosidase as well as urease inhibitory activities. Chem Biodiversity 20:e202300387. https://doi.org/10.1002/cbdv.202300387
doi: 10.1002/cbdv.202300387
Sepehri S, Khedmati M (2023) An overview of the privileged synthetic heterocycles as urease enzyme inhibitors: Structure–activity relationship. Arch Pharm. https://doi.org/10.1002/ardp.202300252
doi: 10.1002/ardp.202300252
Yang Y-S, Su M-M, Zhang X-P et al (2018) Developing potential Helicobacter pylori urease inhibitors from novel oxoindoline derivatives: synthesis, biological evaluation and in silico study. Bioorg Med Chem Lett 28:3182–3186. https://doi.org/10.1016/j.bmcl.2018.08.025
doi: 10.1016/j.bmcl.2018.08.025 pubmed: 30170940
Shahin AI, Zaib S, Zaraei S-O et al (2023) Design and synthesis of novel anti-urease imidazothiazole derivatives with promising antibacterial activity against Helicobacter pylori. PLoS ONE 18:e0286684. https://doi.org/10.1371/journal.pone.0286684
doi: 10.1371/journal.pone.0286684 pubmed: 37267378
Patel L, Shukla T, Huang X et al (2020) Machine learning methods in drug discovery. Molecules 25:5277. https://doi.org/10.3390/molecules25225277
doi: 10.3390/molecules25225277 pubmed: 33198233
Vamathevan J, Clark D, Czodrowski P et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477. https://doi.org/10.1038/s41573-019-0024-5
doi: 10.1038/s41573-019-0024-5 pubmed: 30976107
Stokes JM, Yang K, Swanson K et al (2020) A deep learning approach to antibiotic discovery. Cell 180:688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021
doi: 10.1016/j.cell.2020.01.021 pubmed: 32084340
Dong X, Yu Z, Cao W et al (2020) A survey on ensemble learning. Front Comput Sci 14:241–258. https://doi.org/10.1007/s11704-019-8208-z
doi: 10.1007/s11704-019-8208-z
Falcón-Cano G, Molina C, Cabrera-Pérez MÁ (2020) ADME prediction with KNIME: development and validation of a publicly available workflow for the prediction of human oral bioavailability. J Chem Inf Model 60:2660–2667. https://doi.org/10.1021/acs.jcim.0c00019
doi: 10.1021/acs.jcim.0c00019 pubmed: 32379452
Kumar A, Loharch S, Kumar S et al (2021) Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2. Comput Struct Biotechnol J 19:424–438. https://doi.org/10.1016/j.csbj.2020.12.028
doi: 10.1016/j.csbj.2020.12.028 pubmed: 33391634
Reymond J-L (2015) The chemical space project. Acc Chem Res 48:722–730. https://doi.org/10.1021/ar500432k
doi: 10.1021/ar500432k pubmed: 25687211
Gromski PS, Henson AB, Granda JM, Cronin L (2019) How to explore chemical space using algorithms and automation. Nat Rev Chem 3:119–128. https://doi.org/10.1038/s41570-018-0066-y
doi: 10.1038/s41570-018-0066-y
Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
doi: 10.1016/j.neuron.2018.08.011 pubmed: 30236283
Venable RM, Krämer A, Pastor RW (2019) Molecular dynamics simulations of membrane permeability. Chem Rev 119:5954–5997. https://doi.org/10.1021/acs.chemrev.8b00486
doi: 10.1021/acs.chemrev.8b00486 pubmed: 30747524
De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061. https://doi.org/10.1021/acs.jmedchem.5b01684
doi: 10.1021/acs.jmedchem.5b01684 pubmed: 26807648
Asgari MS, Azizian H, Nazari Montazer M et al (2020) New 1,2,3-triazole–(thio)barbituric acid hybrids as urease inhibitors: design, synthesis, in vitro urease inhibition, docking study, and molecular dynamic simulation. Arch Pharm 353:2000023. https://doi.org/10.1002/ardp.202000023
doi: 10.1002/ardp.202000023
Minkara MS, Ucisik MN, Weaver MN, Merz KM (2014) Molecular dynamics study of Helicobacter pylori urease. J Chem Theory Comput 10:1852–1862. https://doi.org/10.1021/ct5000023
doi: 10.1021/ct5000023 pubmed: 24839409
Mazanetz MP, Marmon RJ, Reisser CBT, Morao I (2012) Drug discovery applications for KNIME: an open source data mining platform. Curr Top Med Chem 12:1965–1979
doi: 10.2174/156802612804910331 pubmed: 23110532
Sydow D, Wichmann M, Rodríguez-Guerra J et al (2019) TeachOpenCADD-KNIME: a teaching platform for computer-aided drug design using KNIME workflows. J Chem Inf Model 59:4083–4086. https://doi.org/10.1021/acs.jcim.9b00662
doi: 10.1021/acs.jcim.9b00662 pubmed: 31612715
Gaulton A, Hersey A, Nowotka M et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45:D945–D954. https://doi.org/10.1093/nar/gkw1074
doi: 10.1093/nar/gkw1074 pubmed: 27899562
Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
doi: 10.1093/nar/gkv951 pubmed: 26400175
Bemis GW, Murcko MA (1996) The properties of known drugs. 1 Molecular Frameworks. J Med Chem 39:2887–2893. https://doi.org/10.1021/jm9602928
doi: 10.1021/jm9602928 pubmed: 8709122
López-López E, Naveja JJ, Medina-Franco JL (2019) DataWarrior: an evaluation of the open-source drug discovery tool. Expert Opin Drug Discovery 14:335–341. https://doi.org/10.1080/17460441.2019.1581170
doi: 10.1080/17460441.2019.1581170
Schäfer T, Kriege N, Humbeck L et al (2017) Scaffold Hunter: a comprehensive visual analytics framework for drug discovery. J Cheminf 9:28. https://doi.org/10.1186/s13321-017-0213-3
doi: 10.1186/s13321-017-0213-3
Oyewole GJ, Thopil GA (2023) Data clustering: application and trends. Artif Intell Rev 56:6439–6475. https://doi.org/10.1007/s10462-022-10325-y
doi: 10.1007/s10462-022-10325-y pubmed: 36466764
Ikotun AM, Ezugwu AE, Abualigah L et al (2023) K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf Sci 622:178–210. https://doi.org/10.1016/j.ins.2022.11.139
doi: 10.1016/j.ins.2022.11.139
Cao Y, Jiang T, Girke T (2008) A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics 24:i366–i374. https://doi.org/10.1093/bioinformatics/btn186
doi: 10.1093/bioinformatics/btn186 pubmed: 18586736
Gadaleta D (2020) Automated integration of structural, biological and metabolic similarities to sustain read-across. Altex. https://doi.org/10.14573/altex.2002281
doi: 10.14573/altex.2002281 pubmed: 32388568
Chung NC, Miasojedow B, Startek M, Gambin A (2019) Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinf 20:644. https://doi.org/10.1186/s12859-019-3118-5
doi: 10.1186/s12859-019-3118-5
Stumpfe D, Hu H, Bajorath J (2019) Evolving concept of activity cliffs. ACS Omega 4:14360–14368. https://doi.org/10.1021/acsomega.9b02221
doi: 10.1021/acsomega.9b02221 pubmed: 31528788
Medina-Franco JL (2012) Scanning structure-activity relationships with structure-activity similarity and related maps: from Consensus Activity Cliffs to Selectivity Switches. J Chem Inf Model 52:2485–2493. https://doi.org/10.1021/ci300362x
doi: 10.1021/ci300362x pubmed: 22989212
Naveja JJ, Norinder U, Mucs D et al (2018) Chemical space, diversity and activity landscape analysis of estrogen receptor binders. RSC Adv 8:38229–38237. https://doi.org/10.1039/C8RA07604A
doi: 10.1039/C8RA07604A pubmed: 35559115
Guha R, Van Drie JH (2008) Structure−activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48:646–658. https://doi.org/10.1021/ci7004093
doi: 10.1021/ci7004093 pubmed: 18303878
Loharch S, Karmahapatra V, Gupta P et al (2019) Integrated Chemoinformatics Approaches Toward Epigenetic Drug Discovery. In: Mohan CG (ed) Structural Bioinformatics: Applications in Preclinical Drug Discovery Process. Springer International Publishing, Cham, pp 247–269
doi: 10.1007/978-3-030-05282-9_8
Jasim SF, Mustafa YF (2022) Synthesis, ADME study, and antimicrobial evaluation of novel naphthalene-based derivatives. J Med Chem Sci 5:793–807. https://doi.org/10.26655/JMCHEMSCI.2022.5.14
doi: 10.26655/JMCHEMSCI.2022.5.14
Mustafa YF (2024) Coumarins derived from natural methoxystilbene as oxidative stress-related disease alleviators: synthesis and in vitro-in silico study. J Mol Struct 1302:137471. https://doi.org/10.1016/j.molstruc.2023.137471
doi: 10.1016/j.molstruc.2023.137471
Yu X-D, Zheng R-B, Xie J-H et al (2015) Biological evaluation and molecular docking of baicalin and scutellarin as Helicobacter pylori urease inhibitors. J Ethnopharmacol 162:69–78. https://doi.org/10.1016/j.jep.2014.12.041
doi: 10.1016/j.jep.2014.12.041 pubmed: 25557028
Ha N-C, Oh S-T, Sung JY et al (2001) Supramolecular assembly and acid resistance of Helicobacter pylori urease. Nat Struct Biol 8:505–509
doi: 10.1038/88563 pubmed: 11373617
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334
doi: 10.1002/jcc.21334 pubmed: 19499576
Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. Softwarex 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
doi: 10.1016/j.softx.2015.06.001
Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174. https://doi.org/10.1002/jcc.20035
doi: 10.1002/jcc.20035 pubmed: 15116359
Rappe AK, Casewit CJ, Colwell KS et al (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114:10024–10035. https://doi.org/10.1021/ja00051a040
doi: 10.1021/ja00051a040
Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713. https://doi.org/10.1021/acs.jctc.5b00255
doi: 10.1021/acs.jctc.5b00255 pubmed: 26574453
Lu T Sobtop, Version [1.0(Dev)]. http://Sobereva.Com/Soft/Sobtop (accessed on 27 October 2022).
Lu T, Chen F (2012) Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 33:580–592. https://doi.org/10.1002/jcc.22885
doi: 10.1002/jcc.22885 pubmed: 22162017
Li P, Roberts BP, Chakravorty DK, Merz KM (2013) Rational design of particle mesh ewald compatible lennard-jones parameters for +2 metal cations in explicit solvent. J Chem Theory Comput 9:2733–2748. https://doi.org/10.1021/ct400146w
doi: 10.1021/ct400146w pubmed: 23914143
Frisch MJ, Trucks GW, Schlegel HB et al (2016) Gaussian 09, Revision E01; Gaussian, Inc.: Wallingford, CT, USA
Liang J, Wang B, Yang Y et al (2023) Approaching the dimerization mechanism of small molecule inhibitors targeting PD-L1 with molecular simulation. IJMS 24:1280. https://doi.org/10.3390/ijms24021280
doi: 10.3390/ijms24021280 pubmed: 36674800
Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461. https://doi.org/10.1517/17460441.2015.1032936
doi: 10.1517/17460441.2015.1032936 pubmed: 25835573
Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82. https://doi.org/10.1021/ci100275a
doi: 10.1021/ci100275a pubmed: 21117705
Colombo G, Toba S, Merz KM (1999) Rationalization of the enantioselectivity of subtilisin in DMF. J Am Chem Soc 121:3486–3493. https://doi.org/10.1021/ja9839062
doi: 10.1021/ja9839062
Shi W-K, Deng R-C, Wang P-F et al (2016) 3-Arylpropionylhydroxamic acid derivatives as Helicobacter pylori urease inhibitors: synthesis, molecular docking and biological evaluation. Bioorg Med Chem 24:4519–4527. https://doi.org/10.1016/j.bmc.2016.07.052
doi: 10.1016/j.bmc.2016.07.052 pubmed: 27492194
O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8:36. https://doi.org/10.1186/s13321-016-0148-0
doi: 10.1186/s13321-016-0148-0 pubmed: 27382417
Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44:235–249. https://doi.org/10.1016/S1056-8719(00)00107-6
doi: 10.1016/S1056-8719(00)00107-6 pubmed: 11274893
Mamidala R, Bhimathati SRS, Vema A (2021) Discovery of novel dihydropyrimidine and hydroxamic acid hybrids as potent Helicobacter pylori urease inhibitors. Bioorg Chem 114:105010. https://doi.org/10.1016/j.bioorg.2021.105010
doi: 10.1016/j.bioorg.2021.105010 pubmed: 34102519
Ni W-W, Liu Q, Ren S-Z et al (2018) The synthesis and evaluation of phenoxyacylhydroxamic acids as potential agents for Helicobacter pylori infections. Bioorg Med Chem 26:4145–4152. https://doi.org/10.1016/j.bmc.2018.07.003
doi: 10.1016/j.bmc.2018.07.003 pubmed: 29983280
Liu Q, Shi W-K, Ren S-Z et al (2018) Arylamino containing hydroxamic acids as potent urease inhibitors for the treatment of Helicobacter pylori infection. Eur J Med Chem 156:126–136. https://doi.org/10.1016/j.ejmech.2018.06.065
doi: 10.1016/j.ejmech.2018.06.065 pubmed: 30006158
Mazzei L, Musiani F, Ciurli S (2020) The structure-based reaction mechanism of urease, a nickel dependent enzyme: tale of a long debate. J Biol Inorg Chem 25:829–845. https://doi.org/10.1007/s00775-020-01808-w
doi: 10.1007/s00775-020-01808-w pubmed: 32809087
Wu X, Wang N, Liang J et al (2023) Is the triggering of PD-L1 dimerization a potential mechanism for food-derived small molecules in cancer immunotherapy? A Study by Molecular Dynamics. IJMS 24:1413. https://doi.org/10.3390/ijms24021413
doi: 10.3390/ijms24021413 pubmed: 36674929
Roberts BP, Miller BR, Roitberg AE, Merz KM (2012) Wide-open flaps are key to urease activity. J Am Chem Soc 134:9934–9937. https://doi.org/10.1021/ja3043239
doi: 10.1021/ja3043239 pubmed: 22670767

Auteurs

Na Wang (N)

College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.

Xiaoyan Wu (X)

College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.

Jianhuai Liang (J)

College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China.

Boping Liu (B)

College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China. boping@scau.edu.cn.

Bingfeng Wang (B)

College of Materials and Energy, South China Agricultural University, Guangzhou, 510630, China. wbfeng@scau.edu.cn.

Classifications MeSH