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CONSIDERATION of GLYCOSIDIC TORSION ANGLE PREFERENCES and CH/Π INTERACTIONS in PROTEIN-CARBOHYDRATE DOCKING by Anita Karen Nive

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CONSIDERATION of GLYCOSIDIC TORSION ANGLE PREFERENCES and CH/Π INTERACTIONS in PROTEIN-CARBOHYDRATE DOCKING by Anita Karen Nive CONSIDERATION OF GLYCOSIDIC TORSION ANGLE PREFERENCES AND CH/π INTERACTIONS IN PROTEIN-CARBOHYDRATE DOCKING by Anita Karen Nivedha (Under the direction of Robert J. Woods) ABSTRACT Carbohydrates play a pivotal role in various life processes including energy metabolism, storage, immune recognition, transportation, signaling and biosynthesis. In these roles, they often interact with other integral components of the living system such as proteins and lipids. An understanding of how these molecules interact can further our knowledge of crucial biological processes, and begins with the knowledge of the three-dimensional structures of these complexes. However, owing to challenges involved in crystallizing oligosaccharide structures, theoretical modeling methods such as molecular docking are often used to predict how oligosaccharides interact with protein receptors. But, docking programs have generalized scoring functions which often produce unnatural oligosaccharide conformations during docking. In this thesis, we present two approaches to improve protein-carbohydrate docking by accounting for specific intra- and intermolecular interaction energies relating to carbohydrates, which are not currently dealt with by existing docking methodologies. In the first approach, we developed a set of Carbohydrate Intrinsic (CHI) energy functions in order to account for intramolecular energies of carbohydrate ligands primarily determined by the conformations of glycosidic torsion angles connecting individual saccharides. This work resulted in the development of Vina-Carb (incorporation of the CHI energy functions within the scoring function of AutoDock Vina), which significantly improved the conformations of oligosaccharide binding mode predictions. In the second approach, we developed a scoring function by fitting a mathematical model to data from literature describing the energy contributed by CH/π interactions. This energy function was used to score the crucial interactions between CH groups lining the carbohydrate ring and the π electron densities in aromatic amino acids of interacting proteins. Employing the CH/π interaction energy function to rescore docked protein-carbohydrate complexes improved the rankings of accurate pose predictions made by both AutoDock Vina and Vina-Carb. The scoring functions developed and used in this work are transferable and can therefore be used with other docking programs and also in the refinement of experimental carbohydrate structures. INDEX WORDS: Autodock, AutoDock Vina, Molecular Docking, Protein-Carbohydrate Docking, Docking Scoring Functions, Internal Energies, Carbohydrate, Carbohydrate Intrinsic Energy Functions, CHI Energy Functions, Vina-Carb, Antibody, Antigen, Lectin, Enzyme, Carbohydrate Binding Module, CH/π Interactions CONSIDERATION OF GLYCOSIDIC TORSION ANGLE PREFERENCES AND CH/π INTERACTIONS IN PROTEIN-CARBOHYDRATE DOCKING by Anita Karen Nivedha B. Tech., Vellore Institute of Technology University, India, 2008 A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY ATHENS, GEORGIA 2015 © 2015 Anita Karen Nivedha All Rights Reserved CONSIDERATION OF GLYCOSIDIC TORSION ANGLE PREFERENCES AND CH/π INTERACTIONS IN PROTEIN-CARBOHYDRATE DOCKING by Anita Karen Nivedha Major Professor: Robert J. Woods Committee: James H. Prestegard Liming Cai Donald Evans Electronic Version Approved: Suzanne Barbour Dean of the Graduate School The University of Georgia December 2015 DEDICATION I would like to dedicate this work to my beloved parents, Jenetta and Joshwa. iv ACKNOWLEDGEMENTS Firstly, I would like to acknowledge and extend my gratitude to my major Professor, Dr. Robert J. Woods for his support, encouragement, guidance and for giving me the wonderful opportunity to be a part of the Woods’ Group Family. I would like to thank my PhD Advisory Committee, Dr. James H. Prestegard, Dr. Liming Cai and Dr. Donald L. Evans for their valuable advice, insight and suggestions over the years as my dissertation took shape. I would like to thank colleagues who were directly involved in my research, Dr. B. Lachele Foley, Dr. Matthew B. Tessier, Dr. Spandana Makeneni and David F. Thieker. It has been a great learning experience and a pleasure collaborating and working with each one of you. I would like to acknowledge the support of my peers in the Woods’ group: Dr. Arunima Singh, Amika Sood, Dr. Jodi Hadden, Mark Baine, Dr. Xiaocong Wang, Dr. Keigo Ito, Dr. Oliver Grant, Huimin Hu, Dr. Valerie Murphy, Dr. Mari DeMarco, Mia Ji, Dr. Elisa Fadda, Dr. Joanne Martin and Dr. Hannah Smith. Matt, thank you for helping me when I was a newbie in the group, and amongst other things, for teaching me to do docking, which constitutes a major portion of my dissertation today. Arunima, Amika, Spandana and Jodi, thank you for being with me through the ups and downs in Graduate School. Keigo, thank you for helping me with all my QM questions and for your tips on scientific writing. Mark, thank you for being a huge support during my time in the group and for all of your efforts in keeping everything around the lab in order. I am thankful to God for being my Provider and for all of His blessings at every stage of my life as a graduate student. I would like to acknowledge the unconditional love, v support and encouragement given by Mama and Papa. Thank you for being my greatest cheerleaders. I would like to extend my heartfelt thanks to Amy, Ashley, Niranjana, Madison, Jagadish, Cookieday, Adwoa, Ken, Femi, Anna, Ebenezer, Adeline, Savior Karnik and Manikins, for being there for me, for believing in me, cheering me on and supporting me throughout Graduate School. I could not have done it without your solid support. I would certainly not be where I am if not for all of the wonderful people who have sown into my life and my career. For them, I am forever grateful. vi LIST OF TABLES Table 4.1 PDB IDs and ligand sequences employed in the study, including the shape RMSD (SRMSD) values for the ligands generated by GLYCAM, relative to the crystallographic ligands. ....................................................................................... 25 Table 5.1 Comparison between ADV and VC at the four settings of CHI-coefficient and CHI-cutoff. ............................................................................................................ 68 Table 5.2 PRMSDmin(5) produced by ADV and VC1|2 for the 12 test systems with ligands containing 1,6-linkages. ........................................................................................ 69 Table 5.3 Comparison between ADV and VC1|2 for the apo proteins Test Set. ............... 76 Table 6.1 Average rank of accurate PRMSDmin pose predictions by ADV and VC1|2 before and after rescoring as a function of the CH/π interaction energy coefficients. The systems are divided into different groups based on the number of detected CH/π interactions. ................................................................................... 95 vii LIST OF FIGURES Figure 2.1. An illustration of the conversion from the chain and ring form of glucose. .... 6 4 1 Figure 2.2 A representation of two chair conformations of Glucose, namely, C1 and C4. ................................................................................................................................. 7 Figure 2.3 A 1-3 glycosidic linkage formation between a glucopyranose (Glcp) unit and a galactopyranose (Galp) unit. The D in the name refers to the molecule being dextrorotatory, which refers to it rotating plane polarized light to the right. .......... 8 Figure 2.4 Carbohydrate epimers: galactose and glucose are C4 epimers, while glucose and mannose are C2 epimers. .................................................................................. 8 Figure 3.1 a.) Rigid Docking b.) Flexible Ligand Docking .............................................. 14 Figure 3.2 The workflow within the AutoDock Vina algorithm. ..................................... 18 Figure 4.1 (a) Illustration of an antibody with its variable fragment (Fv) aligned to the grid box. The yellow dot represents the CoM of the CDRs (0,0,0), and the green dot represents the center of the grid box (0,0,11). (b) Aligned orientation of an antibody antigen-binding fragment (Fab), with respect to the internal reference axes. The region in red + pink represents the VH domain (CDRs (red) and framework regions (pink) of the heavy chain) of the antibody, while the region in blue represents the VL domain (CDRs (dark blue) and framework regions (cyan) of the light chain). The X-axis for the alignment was defined by a vector passing through the CoM of the variable light chain (VL domain, which contains the light chain CDRs and framework sequences), and the CoM of the variable heavy chain (VH domain). The Z-axis was defined as a vector normal to the X-axis, and passing through the CoM of the entire variable region, or variable fragment (Fv). viii The antibody was then translated so that the CoM of the CDRs was placed at the origin. The Y-axis was defined as a vector perpendicular to the XZ-plane, and passing through the origin. The docking grid box was aligned to the internal co- ordinate axes with its center offset from the origin by 11Å along the Z-axis, so as to optimally encompass the CDR loops, while also permitting adequate volume for the movement of the ligand during docking. Such a definition enabled the docking grid box to be consistently aligned with respect to the CDRs. ..............
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