Investigating the interaction between umami peptides and umami receptor T1R1/T1R3-VFT: a computational approach
), Yuan Liua()Abstract
The study of ligand-receptor interactions is of great significance in food flavor perception. In this study, a computer simulation method was used to investigate the mechanism of interaction between umami peptides and T1R1/T1R3-Venus-flytrap domain (VFT) receptor. The binding site, conformational changes, and binding free energy between umami peptides and T1R1/T1R3-VFT were analyzed through molecular modeling, molecular docking, and molecular dynamics simulations. The receptor model constructed using AlphaFold2 has the best rationality. The molecular docking results showed that umami peptides primarily bound to T1R1-VFT through hydrogen bonding, with key binding residues such as Thr149, Arg151, and Asp108. The binding of umami peptides led to a more stable complex system, and the positively charged amino acids contributed positively to the overall binding free energy. This study provides theoretical support for the development of a better understanding of the interaction between umami substances and the umami receptor.
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References
S. Zheng, M. Zou, Y. Shao, et al., Two-dimensional measurements of receptor-ligand interactions, Front. Mol. Biosci. 10 (2023) 1154074. https://doi.org/10.3389/fmolb.2023.1154074.
A. Gaubert, B. Amigues, S. Spinelli, et al., Structure of odorant binding proteins and chemosensory proteins determined by X-ray crystallography, Methods Enzymol. 642 (2020) 151-167. https://doi.org/10.1016/bs.mie.2020.04.070.
S.H. Park, J.H. Lee, Dynamic G protein-coupled receptor signaling probed by solution NMR spectroscopy, Biochemistry 59 (2020) 1065-1080. https://doi.org/10.1021/acs.biochem.0c00032.
S.G. Patching, Surface plasmon resonance spectroscopy for characterisation of membrane protein-ligand interactions and its potential for drug discovery, Biochim. Biophys. Acta 1838 (2014) 43-55. https://doi.org/10.1016/j.bbamem.2013.04.028.
A.A.T. Naqvi, T. Mohammad, G.M. Hasan, et al., Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships, Curr. Top Med. Chem. 18 (2018) 1755-1768. https://doi.org/10.2174/1568026618666181025114157.
L.G. Ferreira, R.N. dos Santos, G. Oliva, et al., Molecular docking and structure-based drug design strategies, Molecules 20 (2015) 13384-13421. https://doi.org/10.3390/molecules200713384.
P.C. Do, E.H. Lee, L. Le, Steered molecular dynamics simulation in rational drug design, J. Chem. Inf. Model. 58 (2018) 1473-1482. https://doi.org/10.1021/acs.jcim.8b00261.
Y. Li, S. Jiang, Y. Zhu, W. Shi, et al., Effect of different drying methods on the taste and volatile compounds, sensory characteristics of Takifugu obscurus, Food Sci. Hum. Wellness 12 (2023) 223-232. https://doi.org/10.1016/j.fshw.2022.07.012.
I.E. Hartley, D.G. Liem, R. Keast, Umami as an ‘alimentary’ taste. a new perspective on taste classification, Nutrients 11 (2019) 182. https://doi.org/10.3390/nu11010182.
Y. Fan, W. Chen, N. Zhang, et al., Umami taste evaluation based on a novel mouse taste receptor cell-based biosensor, Biosens. Bioelectron. 237 (2023) 115447. https://doi.org/10.1016/j.bios.2023.115447.
Y.X. Fan, Y.L. Huang, N.L. Zhang, et al., Study on the distribution of umami receptors on the tongue and its signal coding logic based on taste bud biosensor, Biosens. Bioelectron. 197 (2022) 113780. https://doi.org/10.1016/j.bios.2021.113780.
B. Wu, I. Blank, Y. Zhang, et al., Investigating the influence of different umami tastants on brain perception via scalp electroencephalogram, J. Agri. Food Chem. 70 (2022) 11344-11352. https://doi.org/10.1021/acs.jafc.2c01938.
Y. Dang, L. Hao, J. Cao, et al., Molecular docking and simulation of the synergistic effect between umami peptides, monosodium glutamate and taste receptor T1R1/T1R3, Food Chem. 271 (2019) 697-706. https://doi.org/10.1016/j.foodchem.2018.08.001.
Y. Fu, J. Liu, E.T. Hansen, et al., Structural characteristics of low bitter and high umami protein hydrolysates prepared from bovine muscle and porcine plasma, Food Chem. 257 (2018) 163-171. https://doi.org/10.1016/j.foodchem.2018.02.159.
Z.Y. Liu, Y.W. Zhu, W.L. Wang, et al., Seven novel umami peptides from Takifugu rubripes and their taste characteristics, Food Chem. 330 (2020) 127204. https://doi.org/10.1016/j.foodchem.2020.127204.
S.Q. Song, J.D. Zhuang, C.Z. Ma, et al., Identification of novel umami peptides from Boletus edulis and its mechanism via sensory analysis and molecular simulation approaches, Food Chem. 398 (2023) 133835. https://doi.org/10.1016/j.foodchem.2022.133835.
X. Yu, L. Zhang, X. Miao, et al., The structure features of umami hexapeptides for the T1R1/T1R3 receptor, Food Chem. 221 (2017) 599-605. https://doi.org/10.1016/j.foodchem.2016.11.133.
J. Zhang, M. Zhao, G. Su, et al., Identification and taste characteristics of novel umami and umami-enhancing peptides separated from peanut protein isolate hydrolysate by consecutive chromatography and UPLC-ESI-QTOF-MS/MS, Food Chem. 278 (2019) 674-682. https://doi.org/10.1016/j.foodchem.2018.11.114.
S. Jiang, Y. Zhu, J. Peng, et al., Characterization of stewed beef by sensory evaluation and multiple intelligent sensory technologies combined with chemometrics methods, Food Chem. 408 (2023) 135193. https://doi.org/10.1016/j.foodchem.2022.135193.
Z. Cui, N. Zhang, T. Zhou, et al., Conserved sites and recognition mechanisms of T1R1 and T2R14 receptors revealed by ensemble docking and molecular descriptors and fingerprints combined with machine learning, J. Agric. Food Chem. 71 (2023) 5630-5645. https://doi.org/10.1021/acs.jafc.3c00591.
W. Wang, Z. Cui, M. Ning, et al., In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: a combined modeling approach, Biomaterials 281 (2022) 121338. https://doi.org/10.1016/j.biomaterials.2021.121338.
G. Spaggiari, A. Di Pizio, P. Cozzini, Sweet, umami and bitter taste receptors: state of the art of in silico molecular modeling approaches, Trends Food Sci. Technol. 96 (2020) 21-29. https://doi.org/10.1016/j.tifs.2019.12.002.
N. Nuemket, N. Yasui, Y. Kusakabe, et al., Structural basis for perception of diverse chemical substances by T1R taste receptors, Nat. Commun. 8 (2017) 15530. https://doi.org/10.1038/ncomms15530.
H. Liu, L.T. Da, Y. Liu, Understanding the molecular mechanism of umami recognition by T1R1-T1R3 using molecular dynamics simulations, Biochem. Biophys. Res. Commun. 514 (2019) 967-973. https://doi.org/10.1016/j.bbrc.2019.05.066.
N. Zhang, Z. Cui, M. Li, et al., Typical umami ligand-induced binding interaction and conformational change of T1R1-VFT, J. Agri. Food Chem. 70 (2022) 11652-11666. https://doi.org/10.1021/acs.jafc.2c05559.
J. Liu, N. Zhang, J. Li, et al., A novel umami electrochemical biosensor based on AuNPs@ZIF-8/Ti3C2 MXene immobilized T1R1-VFT, Food Chem. 397 (2022) 133838. https://doi.org/10.1016/j.foodchem.2022.133838.
F. An, K. Cao, S. Ji, et al., Identification, taste characterization, and molecular docking study of a novel microbiota-derived umami peptide, Food Chem. (2022) 134583. https://doi.org/10.1016/j.foodchem.2022.134583.
C. Li, Y. Hua, D. Pan, et al., A rapid selection strategy for umami peptide screening based on machine learning and molecular docking, Food Chem. 404 (2023) 134562. https://doi.org/10.1016/j.foodchem.2022.134562.
J.C. Zhang, J.C. Zhang, L. Liang, et al., Identification and virtual screening of novel umami peptides from chicken soup by molecular docking, Food Chem. 404 (2023) 134414. https://doi.org/10.1016/j.foodchem.2022.134414.
L. Qi, X. Gao, D. Pan, et al., Research progress in the screening and evaluation of umami peptides, Compr. Rev. Food Sci. Food Saf. 21 (2022) 1462-1490. https://doi.org/10.1111/1541-4337.12916.
M. Baek, F. DiMaio, I. Anishchenko, et al., Accurate prediction of protein structures and interactions using a three-track neural network, Science 373 (2021) 871-876. https://doi.org/10.1126/science.abj8754.
J. Jumper, R. Evans, A. Pritzel, et al., Highly accurate protein structure prediction with AlphaFold, Nature 596 (2021) 583-589. https://doi.org/10.1038/s41586-021-03819-2.
M. Mirdita, K. Schutze, Y. Moriwaki, L. et al., ColabFold: making protein folding accessible to all, Nat. Methods 19 (2022) 679-682. https://doi.org/10.1038/s41592-022-01488-1.
Z. Cui, Z. Zhang, T. Zhou, et al., A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML, Food Chem. 405 (2023) 134812. https://doi.org/10.1016/j.foodchem.2022.134812.
Y. Zhang, M.F. Sanner, AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes, Bioinformatics 35 (2019) 5121-5127. 10.1093/bioinformatics/btz459.
G. Weng, J. Gao, Z. Wang, et al., Comprehensive evaluation of fourteen docking programs on protein-peptide complexes, J. Chem. Theory Comput. 16 (2020) 3959-3969. https://doi.org/10.1021/acs.jctc.9b01208.
S. Salentin, S. Schreiber, V.J. Haupt, et al., PLIP: fully automated protein-ligand interaction profiler, Nucleic Acids Res. 43 (2015) W443-W447. https://doi.org/10.1093/nar/gkv315.
M.S. Valdés-Tresanco, M.E. Valdés-Tresanco, P.A. Valiente, et al., gmx_MMPBSA: a new tool to perform end-state free energy calculations with GROMACS, J. Chem. Theory Comput. 17 (2021) 6281-6291. https://doi.org/10.1021/acs.jctc.1c00645.
P. Benkert, M. Biasini, T. Schwede, Toward the estimation of the absolute quality of individual protein structure models, Bioinformatics 27 (2011) 343-350. https://doi.org/10.1093/bioinformatics/btq662.
O. Trott, A.J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem. 31 (2010) 455-461. https://doi.org/10.1002/jcc.21334.
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