UCSF UC San Francisco Electronic Theses and Dissertations Title Relating protein pharmacology by ligand chemistry Permalink https://escholarship.org/uc/item/5vp5h8g4 Author Keiser, Michael James Publication Date 2009 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Copyright 2009 by Michael James Keiser i i To my family iii Acknowledgements I thank my advisor Brian Shoichet, for combining concrete foundations of support with the strong beams of frank advice that give it structure. I thank Brian for knowing when to actively guide and when to lead by example. From Brian I learned the power of falsifiable hypotheses defined such that either result, expected or not, will advance the field—and that the unexpected is often the most intriguing. I thank also John Irwin, who was my rotation advisor and fellow traveler down the many roads of this thesis, and who no matter how busy has always made time for me, even out of thin air. I was excited and apprehensive the morning of my first interviews at UCSF, but I ended that day exhilarated. The research atmosphere of Mission Bay is like no other. Professors Patricia Babbitt, Andrej Sali, and Jim Wells have provided years of essential advice, enthusiasm, and direction both in their roles on my committee and off of it. Patsy mapped the lay of the land, Andrej lit the way with lights statistical, and Jim knew where we were going. Critical to the stories that fill the following pages has been the ready support, energy, and expertise of our collaborators. I give special thanks to Bryan Roth, who appears in two of the first three chapters here. Bryan and his able team of many at the Psychoactive Drug Screening Center—in particular Vincent Setola—are the catalyst that helped transform our predictions into papers. Kelan Thomas and Douglas Edwards found their own paths to this project, and both have contributed greatly. I thank also Corey Adams, for wielding the instruments of drug similarity across the realm of core metabolism. Amanda DeGraw and Mark Distefano had the energy and dedication to build, from an idea, a discovery. iv Eswar Narayanan put BLAST papers into my hands and the Extreme Value Distribution into my head precisely when each was most necessary. Paul Valiant gave much mathematical guidance; he then informed me of all the ways in which my statistics were wrong, intimated at solutions, and left the remainder as an exercise to the reader. Michael Mysinger implemented libraries during his rotation used by SEA today, and has since suggested many improvements to SEA’s random-background routines and to its code. Likewise, Jérôme Hert weather-tested nearly every design decision I made in SEA by implementing its opposite and is, I suspect, the reason I now find myself an author on a paper that proclaims creationism a ‘laughable canard.’ Brian Feng has provided sage advice and ideas over many a dinner, and Yu Chen superb scientific conversation and tea. Veena Thomas made sure I went into Orals with a plan and came out with a pass. Sarah Boyce, Kerim Babaoglu, Kristin Coan, and Denise Teotico I thank for their friendship and perspective. Christian Laggner and Henry Lin have taken up the SEA mantle; may they carry it with aplomb. Matt Merski strove mightily to make a chemist out of a computer scientist, and I thank Peter Kolb and Christian for their contributions here too. Pascal Wassam proved to be the most curious and capable systems administrator I have had the pleasure to meet, while Julia Molla and Rebecca Brown have kept the world spinning and all administrative requirements met. I thank Johannes Hermann, Kaushik Raha, Alan Graves, Oliv Eidam, Rafaela Ferreira, Allison Doak, Gabe Rocklin, and Jens Carlsson for many ideas, and also the lab, which has been a most enjoyable place to spend these last five years. Finally, I thank my parents, Drs. Judy and Wayne, and my sisters, Elizabeth and Jenn, for more than could fit on these pages. This work owes much in its motivation to countless biomedical conversations with my dad over the years (as per bowling ball theory), and my mom’s Ph.D. in science education may also be rather relevant to the topic at hand. I dedicate this thesis to my family. v The text of Chapter 1 is a reprint of the material as it appears in: Keiser, M.J. et al. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25(2), 197-206 (2007). It appears here with permission from the authors. The supplementary material from this paper has been included as Appendix A.1. The text of Chapter 2 is a reprint of the material as will appear in: Keiser, M.J.*, Setola, V.*, et al. Predicting new molecular targets for known drugs. Nature (2009); accepted. *Co-first authors. It appears here with permission from the authors. The supplementary material from this paper has been included as Appendix A.2. The text of Chapter 3 is a reprint of the material as it appears in: Adams, J.C.*, Keiser, M.J.*, et al. A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Comput Biol 5(8), (2009). *Co-first authors. It appears here with permission from the authors. The supplementary material from this paper has been included as Appendix A.3. The text of Appendix B is a reprint of the material as it appears in: Keiser M.J., Hert J. Off-target networks derived from ligand set similarity. Methods Mol Biol 575, 195-205 (2009). It appears here with permission from the authors. v i Abstract Relating protein pharmacology by ligand chemistry Michael James Keiser The identification of protein function based on biological information is an area of intense research. Here we consider a complementary technique that quantitatively groups and relates proteins based on the chemical similarity of their ligands. We began with 65,000 ligands annotated into sets for hundreds of drug targets. The similarity score between each set was calculated using ligand topology. A statistical model was developed to rank the significance of the resulting similarity scores, which were expressed as networks to map the sets together. Although these networks were connected solely by chemical similarity, biologically sensible clusters nevertheless emerged. When we used this “Similarity Ensemble Approach” to compare drugs to target sets, unexpected links emerged. Methadone, Emetine, and Imodium were predicted and experimentally found to antagonize muscarinic M3, α2 adrenergic, and neurokinin NK2 receptors, respectively. Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining their side effects and efficacy. We thereby sought further unexpected links by comparing a collection of 3,665 FDA-approved and investigational drugs against hundreds of targets. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and the antagonism of the histamine H4 receptor by v ii the enzyme inhibitor Rescriptor. Overall, 23 additional novel drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. This Similarity Ensemble Approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. Small molecule drugs also target many core metabolic enzymes in humans and pathogens. We therefore grouped and compared drugs and metabolites by their associated targets and enzymes, mapping these associations onto existing metabolic networks. This revealed what novel territory remains for metabolic drug discovery. We calculated these networks for 385 model organisms and pathogens. Chemical similarity links between drugs and metabolites may suggest drug toxicity, routes of metabolism, and polypharmacology. viii Table of Contents ACKNOWLEDGEMENTS .....................................................................................................IV ABSTRACT........................................................................................................................VII TABLE OF CONTENTS......................................................................................................... IX LIST OF TABLES ...............................................................................................................XIV LIST OF FIGURES..............................................................................................................XVI INTRODUCTION ................................................................................................................. 1 I. Chemical backgrounds.......................................................................................................................3 II. taniBLAST and SEA.........................................................................................................................8 III. Guide to the chapters......................................................................................................................9 IV. References.......................................................................................................................................10 GLOSS TO CHAPTER 1....................................................................................................... 11 I. References..........................................................................................................................................13