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A Comprehensive Analysis of Gibbs Free Energy in Monte Carlo Simulations for Understanding 2D Protein Folding Dynamics
- 1 Queens University
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Abstract
This paper presents a detailed analysis of Gibbs free energy changes in 2D protein folding using Monte Carlo simulations. It starts by introducing fundamental concepts about proteins and the critical role of Gibbs free energy in the free energy landscape of protein folding. The study utilizes a Monte Carlo-based protein prediction server to simulate the folding process of protein sequences and investigates how temperature variations impact this process. Inspired by the work outlined in "Evolutionary Monte Carlo for protein folding simulations," the analysis extends to explore the specific properties of amino acids and their influence on protein folding dynamics in two dimensions. The simulation results offer insightful observations on how different parameters affect the protein folding process, providing a better understanding of the underlying mechanisms. This comprehensive review of the application of Monte Carlo methods to study 2D protein folding dynamics highlights the significance of temperature and Gibbs free energy in shaping the protein folding landscape, demonstrating the utility of these simulations in biochemical research.
Keywords
Protein folding, Gibbs free energy, Monte Carlo, Temperature changes
[1]. Vastag, B. (2002). Genome. gov launched. JAMA, 288(3), 307-307.
[2]. Wang R., Zhu J., Wang S., Wang T., Huang J., Zhu X. Multi-modal emotion recognition using tensor decomposition fusion and self-supervised multi-tasking. International Journal of Multimedia Information Retrieval, 2024, 13(4): 39.
[3]. Sanvictores, T., & Farci, F. (2022, October 31). Biochemistry, primary protein structure. StatPearls [Internet]. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK564343/#:~:text=To% 20reiterate%2C%20the%20primary%20structure, created%20during%20the%20protein%20biosynthesis
[4]. Sorokina, I., Mushegian, A. R., & Koonin, E. V. (2022b, January 4). Is protein folding a thermodynamically unfavorable, active, energy-dependent process? International Journal of Molecular Sciences.
[5]. Zhu, X., Guo, C., Feng, H., Huang, Y., Feng, Y., Wang, X., & Wang, R. (2024). A Review of Key Technologies for Emotion Analysis Using Multimodal Information. Cognitive Computation, 1-27.
[6]. Sambridge, M. (2014). A parallel tempering algorithm for probabilistic sampling and multimodal optimization. Geophysical Journal International, 196(1), 357-374.
[7]. Fang, Y. (2012). Gibbs free energy formula for protein folding. Thermodynamics—Fundamentals and Its Application in Science, Intech, Rijeka, 47-82.
[8]. Harish, B., Gillilan, R. E., Zou, J., Wang, J., Raleigh, D. P., & Royer, C. A. (2021). Protein unfolded states populated at high and ambient pressure are similarly compact. Biophysical Journal, 120(12), 2592-2598.
[9]. Balli, S., Shumway, K. R., & Sharan, S. (2022). Physiology, fever. In StatPearls [Internet]. StatPearls Publishing.
[10]. Buchan, D. W., Ward, S. M., Lobley, A. E., Nugent, T. C. O., Bryson, K., & Jones, D. T. (2010). Protein annotation and modelling servers at University College London. Nucleic acids research, 38(suppl_2), W563-W568.
[11]. Zhu, X., Huang, Y., Wang, X., & Wang, R. (2023). Emotion recognition based on brain-like multimodal hierarchical perception.Multimedia Tools and Applications, 1-19.
[12]. Sorokina, I., Mushegian, A. R., & Koonin, E. V. (2022a, January 4). Is protein folding a thermodynamically unfavorable, active, energy-dependent process? International Journal of Molecular Sciences.
[13]. Lotan, I., Schwarzer, F., & Latombe, J.-C. (n.d.). Efficient energy computation for Monte Carlo simulation of proteins.
Cite this article
Zuo,Y. (2024). A Comprehensive Analysis of Gibbs Free Energy in Monte Carlo Simulations for Understanding 2D Protein Folding Dynamics. Theoretical and Natural Science,74,93-103.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of ICBioMed 2024 Workshop: Computational Proteomics in Drug Discovery and Development from Medicinal Plants
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