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John D. Chodera

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John D. Chodera url http://www.choderalab.org email [email protected] github https://github.com/choderalab CONTACT ORCID iD 0000-0003-0542-119X twitter jchodera mobile 646.737.3319 post 1275 York Ave, Box 357 New York, NY 10065 Education and positions 2012– Assistant Member, Computational and Systems Biology Program, Sloan Kettering Institute 2012– Assistant Member, Memorial Sloan Kettering Cancer Center 2013– Assistant Professor, Physiology, Biophysics, and Systems Biology Program, Weill Cornell Graduate School of Medical Sciences 2008–2012 Independent Distinguished Postdoctoral Fellow, California Institute for Quantitative Biosciences (QB3), University of California, Berkeley 2006–2008 Postdoctoral researcher, Department of Chemistry, Stanford University With Vijay S. Pande (founder, Folding@Home distributed computing project) 1999–2006 Ph.D. in Biophysics, University of California, San Francisco Dissertation: Master equation models of macromolecular dynamics from atomistic simulation. Committee: Ken A. Dill (thesis advisor), Matthew P. Jacobson, Vijay S. Pande 1995–1999 B.S. in Biology, California Institute of Technology Undergraduate research with Paul H. Patterson (experimental molecular neurobiology) and Jerry E. Solomon (computational chemistry) Fellowships and awards 2013–2016 Louis V. Gerstner Young Investigator Award 2013–2014 Google Exacycle for External Faculty 2008–2012 QB3-Berkeley Distinguished Postdoctoral Fellowship, University of California, Berkeley 2005–2006 IBM Predoctoral Fellowship 2005 Frank M. Goyan Award for outstanding work in physical chemistry, University of California, San Francisco 2000–2005 Howard Hughes Medical Institute Predoctoral Fellowship Research overview My research focuses on redesigning the way we develop small molecules for chemical biology and drug discovery and bringing rigorous atomistic modeling into the high-throughput biology and genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware and distributed computing technologies, I am developing a new generation of tools for predicting small molecule binding affinities, designing small molecules with desired properties, quantifying drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. As a core member of the Folding@home Consortium, my lab harnesses the computing power of hundreds of thousands of volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets. Using a unique automated biophysical laboratory, we collect new experimental data targeted to advance the quantitative accuracy of our methodologies, and gather new insight into drug susceptibility and resistance in kinases and other cancer targets. My work makes extensive use of scalable Bayesian statistical inference methods and information theoretic principles for designing experiments and quantifying error. John D. Chodera - Sloan Kettering Institute - 1 Publications Google Scholar statistics: http://goo.gl/qO0JW MyNCBI Bibliography: http://goo.gl/e3kjgK h-index: 34 / i10-index: 51 / citations: 6483 (31 Oct 2017) ∗ asterisk denotes equal contributions Submitted and Under Review Wojnarowicz P, Desai B, Chin Y, Lima e Silva R, Ohnaka M, Lee SB, Cao MG, Ouerfelli O, Xu S, Goldgur Y, Miller M, Chaudhary J, Garland W, Stoller G, Albanese SA, Soni R, Philip J, Healey J, Vinagolu R, Norton L, Rosen N, Hendrickson R, Iavarone A, Dannenberg A, Chodera JD, Pavletich N, Lasorella A, Campochiaro P, Benezra R A small-molecule pan Id antagonist, AGX51, shows strong anti-tumor and anti-neovascular activ- ity In revision for Nature We identify the binding mode· of a new small-molecule pan-Id antagonist prior to its confirmation by mass spectrometry crosslink- ing data. Andrew M. Intlekofer, Alan H. Shih, Bo Wang, Abbas Nazir, Ariën S. Rustenburg, Steven K. Albanese, Minal Patel, Christopher Famulare, Fabian M. Correa, Maria E. Arcila, Justin Taylor, Martin S. Tallman, Mikhail Roshal, Gregory A. Petsko, Chodera JD, Craig B. Thompson, Ross L. Levine, Eytan M. Stein Acquired clinical resistance to IDH inhibition through in trans IDH2 mutations Nature In revision for · Clinical double mutations acting in trans in cancer patients receiving IDH2 inhibitors act through a novel biophysical mechanism. Emily F. Ruff, Joseph M. Muretta, Andrew Thompson, Eric Lake, Soreen Cyphers, Stephen K. Albanese, Sonya Hanson, Julie M. Behr, David D. Thomas, Chodera JD, Nicholas M. Levinson A dynamic mechanism for allosteric activation of Aurora kinase A by activation loop phosphoryla- tion Submitted to Nature Communications Through a combination of FRET, IR, and EPR· labeling and large-scale molecular dynamics simulations, we show that phorphory- lation activates Aurora kinase by a novel mechanism that does not simply correspond to a DFG-out to DFG-in shift. Abl:TKI Co-Crystal Structures Kevin Hauser, Christopher Negron, Steven K. Albanese, Soumya Ray, Lingle Wang, Dmitry Lupyan, Thomas Steinbrecher, Robert Abel, and Chodera JD Accurately predicting targeted kinase inhibitor resistance to clinical Abl mutations using alchem- ical free-energy calculations Submitted to Nature Communications We show how alchemical free energy calculations· can be used to predict whether clinical point mutations in human kinase do- mains confer resistance or susceptibility to targeted kinase inhibitors. Abl:axitinib Abl:bosutinib Abl:Dasatinib Abl:imatinib Abl:nilotinib Abl:ponatinib PDB: 4WA9 PDB: 3UE4 PDB: 4XEY PDB: 1OPJ PDB: 3CS9 PDB: 3OXZ Prime Preprints ahead of submission FEP+ Parton DL, Hanson SM, Rodríguez-Laureano L, Albanese SK, Gradia S, Jeans C, Seeliger M, Chodera JD MUE An open library of human kinase1.24 domain1.25 constructs0.790.80 for automated bacterial expression Preprint ahead of submission(kcal/mol) χ 1.21 0.79 · bioR iv We engineer a library of humanRMSE kinase domains with useful bacterial expression using a novel phosphatase coexpression tech- 1.871.91 1.111.12 nique. (kcal/mol) 1.87 1.11 Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg, Josh Fass, Gregory Ross, Chodera JD, and David L. Mobley Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Bind- ing Modes Using Nonequilibrium Candidate Monte Carlo Prime Preprint ahead of submission chemRxiv FEP+ Nonequilibrium candidate Monte Carlo· can be used to accelerate the sampling of ligand binding modes by orders of magnitude. Experiment Experiment Resistant or not classiFication 131 S N R 131 S N R John D. Chodera - Sloan Kettering Institute - 2 Accuracy 0.6950.698 0.8990.902 s 0 5 1 0.690 0.901 s 1 9 2 Specificity 0.7260.727 0.9380.940 n 1 76 8 0.719 0.938 n 0 96 4 Sensitivity 0.500.51 0.670.68 Prediction r 0 31 9 0.50 0.66 Prediction r 0 7 12 0.65 0.84 Accuracy: 0.65 0.64 Susceptible or Neutral or Resistant Accuracy: 0.83 0.83 Boyce SE, Tellinghuisen JT, and Chodera JD Avoiding accuracy-limiting pitfalls in the study of protein-ligand interactions with isothermal titra- tion calorimetry Preprint ahead of submission bioRχiv We demonstrate how to avoid accuracy-limiting· problems in standard isothermal calorimetry experiments as well as capture the primary sources of uncertainty in thermodynamic parameters. Chodera JD, Noé F, Hinrichs NS, Keller B, Elms PJ, Kaiser CM, Ewall-Wice A, Marqusee S, and Bustamante C Bayesian hidden Markov model analysis of single-molecule biophysical experiments Preprint ahead of submission arχiv We present a Bayesian hidden Markov· model analysis scheme that allows biomolecular conformational dynamics—and the cor- responding uncertainty due to limited data—to be inferred from single-molecule trajectories. This approach was developed for a single-molecule study examining folding dynamics of nascent proteins exiting the ribosome [Science 334:1723, 2011 · DOI]. Chodera JD, Elms PJ, Swope WC, Prinz J-H, Marqusee S, Bustamante C, Noé F, and Pande VS A robust approach to estimating rates from time-correlation functions Preprint ahead of submission arχiv We present a simple, robust approach· to estimating two-state rate constants from experimental or simulation data. Reviews and Commentaries Chodera JD and Noé F Markov state models of biomolecular conformational dynamics Current Opinion in Structural Biology 25:135, 2014 DOI A review of exciting recent developments in the stochastic· modeling of biomolecular dynamics using techniques I originally co-developed to study protein folding. Chodera JD and Mobley DL Entropy-enthalpy compensation: Role and ramifications for rational ligand design Annual Reviews in Biophysics 42:121, 2013 DOI Entropy-enthalpy compensation is likely a universal· phenomena, but not as severe as widely thought, and irrelevant for drug discovery and ligand design. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson KM, and Pande VS Free energy methods in drug discovery and design: Progress and challenges Current Opinion in Structural Biology 21:150, 2011 DOI A review of current opportunities and challenges for alchemical· free energy calculations in drug discovery and design. Prinz JH, Wu H, Sarich M, Keller B, Fischbach M, Held M, Chodera JD, Schütte, and Noé F Markov models of molecular kinetics: Generation and validation Journal of Chemical Physics 134:174105, 2011 DOI Current best practices for the generation and validation· of Markov state models for describing the stochastic dynamics of biomolecular
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