Authors : Mritunjay Mukherjee

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/vb4z43cc

Scribd : https://tinyurl.com/vb6sbsah

DOI : https://doi.org/10.38124/ijisrt/25sep996

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Understanding how solvent composition influences peptide conformations is crucial for insights into protein folding, stability, and solvation effects. In this study, we investigate the conformational preferences of alanine dipeptide in mixed acetonitrile (ACN) and water solutions using molecular dynamics simulations. Solvent boxes with 10% and 40% ACN were constructed using Packmol, and 1 ns simulation were performed with OpenMM. The resulting trajectories were analyzed using MDTraj to extract backbone dihedral angles (φ and ψ) and identify stable conformational basins. Results demonstrate that solvent composition significantly affects the population of conformers, with higher ACN content favoring extended or gauche structures. These findings provide a quantitative understanding of solvation effects on peptide flexibility, offering insights relevant for protein chemistry and solvent-dependent folding studies.

Keywords : Alanine Dipeptide, Molecular Dynamics, Solvent Composition, Acetonitrile-Water Mixtures, Conformational Analysis, Ramachandran Angles, MD Simulation.

References :

1. Coutsias, E. A., Seok, C., Jacobson, M. P., & Dill, K. A. (2004). A kinematic view of loop closure: Backbone flexibility in proteins. Journal of Computational Chemistry, 25(4), 510–528. https://doi.org/10.1002/jcc.10416
2. McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J. M., Hernández, C. X., … Pande, V. S. (2015). MDTraj: A modern open library for the analysis of molecular dynamics trajectories. Biophysical Journal, 109(8), 1528–1532. https://doi.org/10.1016/j.bpj.2015.08.015
3. Mu, Y., Kosov, D. S., & Stock, G. (2005). Conformational dynamics of alanine dipeptide in solution: A comparison of molecular dynamics force fields and quantum calculations. Journal of Physical Chemistry B, 109(14), 6443–6454. https://doi.org/10.1021/jp0462627
4. Okamoto, Y., Morishita, T., & Mikami, M. (2003). Conformational sampling of alanine dipeptide in aqueous and nonaqueous solutions. Chemical Physics Letters, 374(5–6), 437–442. https://doi.org/10.1016/S0009-2614(03)00712-2
5. Piana, S., & Laio, A. (2007). A bias-exchange approach to free-energy calculations: Methanol–water solution and the alanine dipeptide in water. Journal of Chemical Physics, 127(4), 045104. https://doi.org/10.1063/1.2746035
6. Felitsky, D. J., Record, M. T. Jr., & Markley, J. L. (2004). Effects of solvent composition on peptide structure: A thermodynamic and spectroscopic analysis. Biochemistry, 43(13), 442–450. https://doi.org/10.1021/bi0356090
7. Ghosh, T., Kalra, A., & Garde, S. (2001). On the thermodynamics of hydrophobic interactions in mixed solvents. The Journal of Physical Chemistry B, 105(22), 4742–4750. https://doi.org/10.1021/jp010160x
8. Rizzo, R. C., & Jorgensen, W. L. (1999). Solvent effects on conformational preferences of small peptides: A computational study of alanine dipeptide in solution. Journal of the American Chemical Society, 121(21), 4827–4836. https://doi.org/10.1021/ja9842519
9. Roccatano, D., Barthel, A., & Zacharias, M. (1999). Mechanism of conformational changes of alanine dipeptide in solution: Molecular dynamics simulations. Journal of Molecular Structure: THEOCHEM, 492(1–3), 147–160. https://doi.org/10.1016/S0166-1280(99)00156-5
10. Van Gunsteren, W. F., & Berendsen, H. J. C. (1988). A leap-frog algorithm for stochastic dynamics. Molecular Simulation, 1(3), 173–185. https://doi.org/10.1080/08927028808080941
11. Roccatano, D., Barthel, A., & Zacharias, M. (1999). Mechanism of conformational changes of alanine dipeptide in solution: Molecular dynamics simulations. Journal of Molecular Structure: THEOCHEM, 492(1–3), 147–160. https://doi.org/10.1016/S0166-1280(99)00156-5
12. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869
13. Nikitin, A. M., & Lyubartsev, A. P. (2007). New six-site acetonitrile model for simulations of liquid acetonitrile and its aqueous mixtures. Journal of Computational Chemistry, 28(12), 2020–2026. https://doi.org/10.1002/jcc.20637