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Data-Driven Insights Into Ligands, Proteins, and Genetic Mutations

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Data-Driven Insights Into Ligands, Proteins, and Genetic Mutations Data-Driven Insights into Ligands, Proteins, and Genetic Mutations by Jing Lu A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in the University of Michigan 2016 Doctoral Committee: Professor Heather A. Carlson, Chair Professor Charles L. Brooks III Assistant Professor Barry Grant Professor David S. Sept Professor Kerby A. Shedden © Jing Lu, 2016 Acknowledgements I would like to thank my advisor, Dr. Heather Carlson, for years of patient guidance, teaching, and support through the course of my PhD. I have learnt how to think critically and be rigorous in every step of research. I also want to express gratitude to my committee: Professor Charles L. Brooks III, Assistant Professor Barry Grant, Professor David S. Sept, Professor Kerby A. Shedden. Their advising is insightful and deepens my understanding of my research projects. I would like to thank Dr. Richard Smith for timely support for both my writing and research. For many Saturdays and Sundays, he promptly responds my requests for proofreading. Much of my work is built on his code in protein and ligand analysis. I would like to thank other members in Dr. Carlson’s lab for helping me with my work. Through the discussion with Dr. Jim Dunbar, I have learnt many critical ideas in Cheminformatics. Also, thank you to Sarah Graham and Jordan Clark for their tremendous friendship and willing to help with my writing. I would also thank previous members in Dr. Carlson’s lab. I would thank Dr. Phani Ghanakota for many late-night discussions and Dr. Aqeel Ahmed for collaborations on projects. ii I would also like to thank Department of Computational Medicine and Bioinformatics for supporting me to finish my work, especially Dr. Margit Burmeister. She has been a great friend and mentor for helping me survive my PhD. Last but not least, I would thank my dear life-long partner, Kuangli Xie. She makes me feel calm during hard time. Her encouragement gives me the strength through difficulties. She gave me the feeling of completeness and makes me a better person every single day. I feel so lucky to have her in my life to write our stories together. iii Table of Contents: Acknowledgements .................................................................................................... ii List of Tables ............................................................................................................ viii List of Figures ............................................................................................................ xi List of Appendices .................................................................................................. xviii Abstract ................................................................................................................... xix Chapter 1. Introduction ......................................................................................... 1 1.1 Overview: ................................................................................................................ 1 1.2 Visualization for aiding structure-based drug design ........................................... 11 1.2.1 Molecular similarity ...................................................................................... 12 1.2.2 Graph representations of molecular data sets ............................................. 18 1.3 Ligand chemical space for allosteric compounds ................................................. 22 1.3.1 Rules derived from physicochemical properties analysis .............................. 23 1.3.2 Distinguishing allosteric modulators by physical chemical properties ......... 24 1.4 The impact of mutations on protein-ligand binding sites .................................... 26 1.4.1 The impact of SNP and possible association with diseases .......................... 26 1.4.2 nsSNP and the possible impact on protein structures .................................. 27 1.4.3 Existing databases of protein-ligand interactions ........................................ 28 1.4.4 Determining residue locations on protein’s structure .................................. 30 iv Chapter 2. ChemTreeMap: An Interactive Map of Biochemical Similarity in Molecular Datasets .................................................................................................. 33 2.1 Abstract ................................................................................................................. 33 2.2 Introduction .......................................................................................................... 34 2.3 Methods ................................................................................................................ 37 2.3.1 Representation of the molecules .................................................................. 37 2.3.2 Construction of the chemical similarity tree ................................................. 38 2.3.3 Clustering of Molecules for Very Large Datasets .......................................... 39 2.4 Implementation .................................................................................................... 42 2.4.1 ChemTreeMap can be easily extended with more features ......................... 43 2.4.2 Comparing ChemTreeMap to other SAR methods ........................................ 43 2.5 Datasets ................................................................................................................ 45 2.6 Results and Discussion .......................................................................................... 47 2.6.1 Comparing chemical diversity of large datasets ........................................... 47 2.6.2 Options for displaying more information ...................................................... 51 2.6.3 ChemTreeMap can be used to extract SAR information ............................... 52 2.7 Conclusion ............................................................................................................. 67 Chapter 3. Physicochemical differences between allosteric and competitive ligands 68 3.1 Abstract ................................................................................................................. 68 3.2 Introduction .......................................................................................................... 69 3.3 Methods: ............................................................................................................... 72 v 3.3.1 Data Collection .............................................................................................. 72 3.3.2 Calculation of Compound Descriptors ........................................................... 76 3.3.3 Removing Redundancy .................................................................................. 78 3.3.4 The Weighting Procedure, a Better Method for Removing Redundancy while Maintaining Diversity ................................................................................................ 79 3.3.5 Overlap between allosteric and competitive dataset ................................... 82 3.3.6 Chemical composition of datasets ................................................................ 82 3.3.7 Data Analysis................................................................................................. 83 3.4 Result and Discussion: .......................................................................................... 86 3.4.1 Distributions of Protein targets ..................................................................... 87 3.4.2 Protein-Ligand Clusters ................................................................................. 90 3.4.3 Chemical Space of Allosteric and Competitive Ligands ................................. 92 3.4.4 Physicochemical Differences between Allosteric and Competitive Ligands . 94 3.5 Conclusions ......................................................................................................... 108 Chapter 4. Exploring the Effect of Mendelian Disease and Neutral: nsSNPs in Protein Structure - a Large Scale Analysis ................................................................ 109 4.1 Abstract ............................................................................................................... 109 4.2 Introduction ........................................................................................................ 110 4.3 Results & Discussion ........................................................................................... 114 4.3.1 Summary of the data set ............................................................................. 114 4.3.2 Disease-Associated Missense Mutations Have High Preference for Ligand Binding Sites and the Protein Core .......................................................................... 114 vi 4.3.3 Robustness check for location preference of disease mutation ................. 122 4.3.4 Amino acid preference ................................................................................ 129 4.4 Methods .............................................................................................................. 133 4.4.1 Preference calculation................................................................................. 133 4.4.2 Mixed effects model .................................................................................... 135 4.4.3 Datasets ...................................................................................................... 136 4.4.4 Definition
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