Determining Monomeric Polyglutamine Structure through FRET, Molecular Dynamics, and Q-learning

1. Determining Monomeric Polyglutamine Structure through FRET, Molecular Dynamics, and Q-learning Alfred Chung (UofM), Michael McPhail (MSU), Karis Stevenson (MIT) Dr. M.A. Zohdy (ECE Department OU), Dr. J. Finke (Chem Department OU)‏ Circular Dichroism Spectra (Ref.) Q-learning Discussion 32.8 ± 1.9 Å The end-to-end distances obtained through molecular simulations with both Parm99SB and the Finke models were found to correspond with the distance determined from FRET experiments. Although the Parm99SB force field is widely used as the gold standard for molecular simulations, we found that Parm99SB erroneously predicted a predominately alpha-helical peptide, against the CD experimental data. The Finke Model accurately determined the end-to-end distance as well as matching the results found through triplet state quenching experiments. The novel application of Q-Learning proved useful by reducing the time required for a simulation. Varying parameters within the model can be compared with distance distributions to quickly see effects. Finke Model Parm96 30.9 ± 1.5 Å 42.7 ± 10.5 Å Parm99SB 34.1 ± 1.5 Å CD Spectra Results Abstract FRET • The experimental distance was found to be 30.8 ± 2.7 Å. • Molecular dynamic simulations using Parm99SB and the Finke model were found to have distances ranging from 30 to 35 Å. Many neurodegenerative disorders are derived from a common class of misfolded proteins which contain an extended polyglutamine tract, implicated in the formation of toxic aggregates and oligomers in neurons. Although much research has been focused on the aggregates themselves, the tertiary structure and dynamics of the monomeric polyglutamine tract has not been well studied.The goal of this project was to investigate the structure of the monomeric polyglutamine peptide (K2Q16K2) by integrating in-vitro experiments, in-silico simulations, and a computer reinforcement learning algorithm. The three techniques were used to determine end-to-end distance of the polyglutamine peptide. Alpha Helix Beta Sheet Random Coil Amber Models Methods • FRET Experiments • Fluorescence-based Measurement Tool • Efficiency + Förster Distance = Distance • AMBER Molecular Dynamics • Computer-based simulation • Force fields used: Parm96, Parm99SB, Finke Model • Improved Q-learning • Reinforcement Learning Algorithm • Agents learns autonomously in environment and observes rewards Contact Rates Q-Learning Model Conclusion Equations Our findings create a framework for future researchers to use our polyglutamine model to investigate misfolding events and develop future therapeutics. Future studies will investigate the structural features of oligomeric and aggregated forms of the polyglutamine by building upon the three techniques used in this project. Q learning Distance Distribution Frequency References • Case, D.A et. al. “The Amber biomolecular simulation programs.” J. Computat. Chem. 26, 1668-1688 (2005). • Finke, John M., Margaret S. Cheung, and Jose N. Onuchic. "A Structural Model of Polyglutamine Determined from a Host-Guest Method Combining Experiments and Landscape Theory." Biophysical Journal 67 (2004): 1900-1918. • Singh, Vijay R., and Lisa J. Lapidus. “The Intrinsic Stiffness of Polyglutamine.” J. Phys. Chem. B 112 (2008): 13172-3176. • Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8 (3), 279-292. Distance (angstroms) "This work was performed during the SIBHI program at Oakland University funded by NSF and NIH under the BBSI program, grant number 0552707."