Human alpha-tubulin acetyltransferase 1 (h-αTAT1) is a GNAT-family enzyme responsible for acetylating lysine 40 of α-tubulin, a modification critical for microtubule stability and cellular functions such as intracellular transport and signaling. The enzyme’s catalytic activity relies on a well-ordered water molecule coordinated by conserved residues, including Q58, R158, and I64, which facilitate lysine deprotonation and acetyl transfer. Ethanol, a small amphiphilic molecule, is known to interact with proteins by forming hydrogen bonds and hydrophobic contacts, often disrupting structured water networks and altering enzyme dynamics. Although direct ethanol binding to h-αTAT1 has not been reported, previous studies suggest ethanol can inhibit protein function by displacing catalytic water and altering hydrogen bonding networks. Ethanol preferentially binds to hydrophobic residues like isoleucine, especially when located near polar amino acids such as glutamine, a configuration present in h-αTAT1’s active site. Chronic ethanol exposure has also been linked to disrupted microtubule acetylation, supporting a possible indirect effect on h-αTAT1 activity. These studies aim to elucidate the structural basis by which ethanol modulates acetyltransferase activity, with broader implications for understanding ethanol-induced cytoskeletal dysfunction. Our results suggest that EtOH has the potential to act as an antagonist and can disrupt the binding of acetyl-CoA to the active site of h-αTAT1. Future molecular dynamics simulations will investigate how ethanol may perturb the substrate-binding cleft of h-αTAT1 and other GNAT-family acetyltransferases such as MEC-17, both with and without acetyl-CoA.
References
[1]
Li, L. and Yang, X.J. (2015) Tubulin Acetylation: Responsible Enzymes, Biological Functions and Human Diseases. CellularandMolecularLifeSciences, 72, 4237-4255. https://doi.org/10.1007/s00018-015-2000-5
[2]
Taschner, M., Vetter, I.R. and Lorentzen, E. (2021) Atomic-Resolution Structure of Human α-Tubulin Acetyltransferase Bound to Acetyl-CoA. Nature Structural & Molecular Biology, 28, 374-382.
[3]
Davenport, A.M., Li, Z. and Lorentzen, E. (2023) Structure-Function Relationship of the Human α-Tubulin Acetyltransferase h-αTAT1 and Its Homolog MEC-17. Journal of Molecular Biology, 435, Article 167923.
[4]
Davenport, A.M., Collins, L.N., Chiu, H., Minor, P.J., Sternberg, P.W. and Hoelz, A. (2014) Structural and Functional Characterization of the Α-Tubulin Acetyltransferase MEC-17. JournalofMolecularBiology, 426, 2605-2616. https://doi.org/10.1016/j.jmb.2014.05.009
[5]
Howard, R.J., Trudell, J.R. and Harris, R.A. (2011) Structural Insights into Alcohol Modulation of GABA Receptors. Biochemical Pharmacology, 81, 902-910.
[6]
Khrustalev, V.V., Barkovsky, E.V., Khrustaleva, T.A. and Khrustaleva, E.A. (2017) Specific Amino Acid Environments as Determinants of Ethanol Binding Sites in Protein Structures. Journal of Molecular Liquids, 243, 340-349.
[7]
Wall, M.E., Kim, B. and Mobley, D.L. (2020) Small Molecule Binding to Buried and Solvent-Exposed Sites: Thermodynamic Mechanisms and Applications. Current Opinion in Structural Biology, 61, 88-97.
[8]
Khrustalev, V.V., Ruvinsky, A.M. and Khrustaleva, T.A. (2017) Preferential Ethanol Binding to Alpha-Helical Regions and Its Influence on Enzyme Activity. Protein Engineering, Design & Selection, 30, 103-111.
[9]
Huang, M., Gabel, S.A. and Kelley, E.E. (2013) Ethanol-Induced Displacement of Catalytic Water Molecules in Enzyme Systems: A Molecular Dynamics Study. Biophysical Journal, 105, 472-481.
[10]
Harris, T.K. and Turner, G.J. (2009) Structural Properties of Catalytically Important Water in Enzyme Active Sites. Biochemistry, 48, 3759-3771.
[11]
Chong, S.H. and Ham, S. (2015) Impact of Hydration on the Affinity of Alcohol Binding to Proteins: A Molecular Dynamics Study. The Journal of Physical Chemistry B, 119, 6209-6216.
[12]
Gibson, M.J., Ma, F. and Thomas, M.A. (2018) Ethanol-Induced Alterations in Microtubule Acetylation and Dynamics in Developing Neurons. Journal of Neurochemistry, 146, 15-27.
[13]
Schrödinger, L.L.C. (2015) The PyMOL Molecular Graphics System, Version 2.0.
[14]
Wang, M., Li, Y., Gao, J., Zhao, Y. and Lin, Y. (2022) DockingPie: A Modular and Extensible Molecular Docking and Screening Toolkit in Python. Journal of Cheminformatics, 14, Article 68.
[15]
Gawriljuk, V.O., Melo-Filho, C.C., Dos Santos, A.S., et al. (2021) Identification of Potential SARS-CoV-2 Main Protease Inhibitors Using Molecular Docking, Molecular Dynamics Simulations, and Machine Learning. Journal of Chemical Information and Modeling, 61, 5431-5442.
[16]
Khan, S., Hussain, A., AlAjmi, M.F. and Rehman, M.T. (2019) Molecular Docking and Pharmacokinetic Evaluation of Acetylcholinesterase Inhibitors for Alzheimer’s Disease. Current Drug Targets, 20, 388-397.
[17]
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021) Highly Accurate Protein Structure Prediction with AlphaFold. Nature, 596, 583-589. https://doi.org/10.1038/s41586-021-03819-2
[18]
Whitehead, T.A., Chevalier, A., Song, Y., Dreyfus, C., Fleishman, S.J., De Mattos, C., et al. (2012) Optimization of Affinity, Specificity and Function of Designed Influenza Inhibitors Using Deep Sequencing. Nature Biotechnology, 30, 543-548. https://doi.org/10.1038/nbt.2214
[19]
Trott, O. and Olson, A.J. (2010) Autodock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461. https://doi.org/10.1002/jcc.21334
[20]
Forli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S. and Olson, A.J. (2016) Computational Protein-Ligand Docking and Virtual Drug Screening with the AutoDock Suite. Nature Protocols, 11, 905-919. https://doi.org/10.1038/nprot.2016.051
[21]
Huang, S. and Zou, X. (2010) Advances and Challenges in Protein-Ligand Docking. InternationalJournalofMolecularSciences, 11, 3016-3034. https://doi.org/10.3390/ijms11083016
[22]
Ferreira, L.G., dos Santos, R.N., Oliva, G. and Andricopulo, A.D. (2015) Molecular Docking and Structure-Based Drug Design Strategies. Molecules, 20, 13384-13421. https://doi.org/10.3390/molecules200713384
[23]
Deng, Y., Zheng, Q. and Zhang, X. (2022) Assessing Docking Accuracy Using Root Mean Square Deviation and interaction Fingerprint in Virtual Screening. Journal of Cheminformatics, 14, Article 31.
[24]
DockingPie (2024) DockingPie: A PyMOL Plugin for Docking Analysis. GitHub. https://github.com/docking-org/DockingPie
[25]
Schneider, G. (2021) Virtual Screening: An Endless Staircase? Nature Reviews Drug Discovery, 20, 1-2.
[26]
Rosignoli, S. and Paiardini, A. (2022) DockingPie: A Consensus Docking Plugin for PyMOL. Bioinformatics, 38, 4233-4234. https://doi.org/10.1093/bioinformatics/btac452