pH-Dependent Conformational Analysis of Threonine Using Different Molecular Modeling Methods

Mikhail E. Kuznetsov; Maria G. Khrenova; Anna M. Kulakova

doi:10.14529/jsfi260106

Authors

Mikhail E. Kuznetsov Lomonosov Moscow State University, Moscow, Russia https://orcid.org/0000-0002-4365-9337
Maria G. Khrenova Lomonosov Moscow State University, Moscow, Russia https://orcid.org/0000-0001-7117-3089
Anna M. Kulakova Lomonosov Moscow State University, Moscow, Russia https://orcid.org/0000-0002-9732-0996

DOI:

https://doi.org/10.14529/jsfi260106

Keywords:

molecular dynamics, threonine, conformers, NAMD3

Abstract

Conformational landscape of flexible molecules plays an important role in their reactivity, physicochemical properties and biological functions. The article presents a comparative study of the conformational stability of three protonated forms of threonine (Thr⁽⁺⁾, Thr⁽⁰⁾, Thr⁽⁻⁾) in aqueous solution using classical molecular dynamics (MD), umbrella sampling (US) and metadynamics (MTD) methods. It is shown that classical molecular dynamics fails to achieve ergodic sampling for Thr⁽⁰⁾and Thr⁽⁻⁾due to high rotational energy barriers around the C_α–C_β bond. The US method, despite being slightly more computationally expensive than classical MD, provides the most accurate Gibbs free energy profiles with minimal statistical error. Conventional MTD exhibits an unacceptably high confidence interval (up to 6 kcal/mol), while well-tempered MTD (WT-MTD) yields results that are quantitatively consistent with US (difference less then 0.2 kcal/mol) and an acceptable error margin (∼1 kcal/mol). It was established that at pH < 9.62 (Thr⁽⁰⁾and Thr⁽⁺⁾forms), the trans conformation is the most stable, whereas for the deprotonated Thr⁽⁻⁾form, the gauche⁽⁻⁾conformation is preferred. At the same time, the energy differences between the conformers are small (1–2 kcal/mol), and the transition barriers vary within the range of 3–12 kcal/mol.

References

Alonso, J.L., Pérez, C., Sanz, M.E., et al.: Seven conformers of L-threonine in the gas phase: a LA-MB-FTMW study. Physical Chemistry Chemical Physics 11(4), 617–627 (2009). https://doi.org/10.1039/b810940k

Barone, V., Cossi, M.: Quantum calculation of molecular energies and energy gradients in solution by a conductor solvent model. The Journal of Physical Chemistry A 102(11), 1995–2001 (1998). https://doi.org/10.1021/jp9716997

Best, R.B., Zhu, X., Shim, J., et al.: Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. Journal of Chemical Theory and Computation 8(9), 3257–3273 (2012). https://doi.org/10.1021/ct300400x

Dubey, P., Mukhopadhyay, A., Viswanathan, K.S.: Do amino acids prefer only certain backbone structures? Steering through the conformational maze of l-threonine using matrix isolation infrared spectroscopy and ab initio studies. Journal of Molecular Structure 1175, 117–129 (2019). https://doi.org/10.1016/j.molstruc.2018.07.066

Hamprecht, F.A., Cohen, A.J., Tozer, D.J., Handy, N.C.: Development and assessment of new exchange-correlation functionals. The Journal of Chemical Physics 109(15), 6264–6271 (1998). https://doi.org/10.1063/1.477267

Hariharan, P.C., Pople, J.A.: The influence of polarization functions on molecular orbital hydrogenation energies. Theoretica Chimica Acta 28(3), 213–222 (1973). https://doi.org/10.1007/BF00533485

Hernández, B., Pflüger, F., Adenier, A., et al.: Energy maps, side chain conformational flexibility, and vibrational features of polar amino acids L-serine and L-threonine in aqueous environment. The Journal of Chemical Physics 135(5), 055101 (2011). https://doi.org/10.1063/1.3617415

Humphrey, W., Dalke, A., Schulten, K.: VMD: Visual Molecular Dynamics. Journal of Molecular Graphics 14(1), 33–38 (1996). https://doi.org/10.1016/0263-7855(96)00018-5

Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., et al.: Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79(2), 926–935 (1983). https://doi.org/10.1063/1.445869

Kundrat, M.D., Autschbach, J.: Computational Modeling of the Optical Rotation of Amino Acids: A New Look at an Old Rule for pH Dependence of Optical Rotation. Journal of the American Chemical Society 130(13), 4404–4414 (2008). https://doi.org/10.1021/ja078257l

Lu, H., Chen, X., Zhan, C.G.: First-Principles Calculation of p Ka for Cocaine, Nicotine, Neurotransmitters, and Anilines in Aqueous Solution. The Journal of Physical Chemistry B 111(35), 10599–10605 (2007). https://doi.org/10.1021/jp072917r

Neese, F.: Software update: The ORCA program system–version 5.0. Wiley Interdisciplinary Reviews: Computational Molecular Science 12(5), e1606 (2022). https://doi.org/10.1002/wcms.1606

Phillips, J.C., Hardy, D.J., Maia, J.D.C., et al.: Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics 153(4), 044130 (2020). https://doi.org/10.1063/5.0014475

Quesada-Moreno, M.M., Marquez-Garcia, A.A., Aviles-Moreno, J.R., et al.: Conformational landscape of l-threonine in neutral, acid and basic solutions from vibrational circular dichroism spectroscopy and quantum chemical calculations. Tetrahedron: Asymmetry 24(24), 1537–1547 (2013). https://doi.org/10.1016/j.tetasy.2013.09.025

Souaille, M., Roux, B.: Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Computer Physics Communications 135(1), 40–57 (2001). https://doi.org/10.1016/S0010-4655(00)00215-0

Szidarovszky, T., Czakó, G., Császár, A.G.: Conformers of gaseous threonine. Molecular Physics 107(7), 761–775 (2009). https://doi.org/10.1080/00268970802616350

Voevodin, V.V., Antonov, A.S., Nikitenko, D.A., et al.: Supercomputer Lomonosov-2: Large Scale, Deep Monitoring and Fine Analytics for the User Community. Supercomputing Frontiers and Innovations 6(2), 4–11 (2019). https://doi.org/10.14529/jsfi190201