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Quantifying Tyrosine Rotamer Flexibility in Protein-Ligand Docking

Explore side-chain flexibility using 3D interaction homology for tyrosine rotamers in protein-ligand docking. Understand favorable and unfavorable interactions, HINT scoring, and Ramachandran plots. Conformational analysis results presented with references to insightful research.

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Quantifying Tyrosine Rotamer Flexibility in Protein-Ligand Docking

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  1. Accounting for side-chain flexibility in protein-ligand docking:3D Interaction Homology as an approach of quantifying side-chain flexibility of Tyrosine Rotamers Leila Alickovic Glen E. Kellogg, Ph.D.

  2. Homology modeling

  3. Protein-Ligand Docking

  4. Conformational selection model

  5. Conformational selection model

  6. Amino Acid Rotamers

  7. HINT • Hydropathyinetractions: • favorable polar (e.g., hydrogen bonding, Lewis acid-base, attractive Coulombic, etc.); • unfavorable polar (e.g., Lewis acid-acid and base-base, repulsive Coulombic, etc.); • favorable hydrophobic (hydrophobic-hydrophobic, p-stacking, etc.); • unfavorable hydrophobic (hydrophobic-polar, desolvation, etc.)

  8. Ramachandran plots

  9. HINT scoring and basis Map

  10. Average Maps 2D cartoon representation of clusters with data points. Each data point represents a tyrosine residue

  11. Average Maps

  12. Conformational Analysis

  13. Results

  14. Refrences • 1.  http://www.intechopen.com/books/protein-engineering-technology-and-application/protein-protein-and-protein-ligand-docking • 2. Du, X., Li, Y., Xia, Y. L., Ai, S. M., Liang, J., Sang, P., Ji, X. L., … Liu, S. Q. (2016). Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods. International journal of molecular sciences, 17(2), 144. doi:10.3390/ijms17020144 • 3. Kuhn, L. A. (no date) “Chapter 10. Strength in Flexibility: Modeling Side-Chain Conformational Change in Docking and Screening,” Computational and Structural Approaches to Drug Discovery RSC Biomolecular Sciences, pp. 181–191. doi: 10.1039/9781847557964-00181 • 4. Gaudreault, F., Chartier, M., & Najmanovich, R. (2012). Side-chain rotamer changes upon ligand binding: common, crucial, correlate with entropy and rearrange hydrogen bonding. Bioinformatics (Oxford, England), 28(18), i423-i430. • 5. Kolaskar, A.s., and V. Ramabrahmam. “Side Chain Characteristic Main Chain Conformations of Amino Acid Residues.” International Journal of Peptide and Protein Research, vol. 19, no. 1, Dec. 2009, pp. 1–9., doi:10.1111/j.1399-3011.1982.tb03016.x. • 6. Ahmed, M. H., Koparde, V. N., Safo, M. K., Scarsdale, J. N. and Kellogg, G. E. (2015) “3d interaction homology: The structurally known rotamers of tyrosine derive from a surprisingly limited set of information-rich hydropathic interaction environments described by maps,” Proteins: Structure, Function, and Bioinformatics, 83(6), pp. 1118–1136. doi: 10.1002/prot.24813 • 7. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein data bank. Nucl Acids 
Res 2000;28:235–242. 
 • 8.The 20 Amino Acids: hydrophobic, hydrophilic, polar and charged amino acids. Available at: https://proteinstructures.com/Structure/Structure/Ramachandran-plot.html • 9. Dudek MWA. clusterSim: searching for optimal clustering procedure for a data set. R package version 0.43-4, 2014. Available at: http://cran.rproject.org/web/packages/clusterSim/index.html 10. Raczkiewicz R, Braun W. Exact and efficient analytical calculation of the accessible surface areas and their gradients for macromolecules. J Comp Chem 1998;19:319–333.


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