A class of kinases are particularly promiscuous binders of small molecule inhibitors. Using combined biomolecular simulations and biochemical studies, we show that the promiscuity of DDR1, one of the major members of this class, is likely due to an unusually stable DFG-out conformation.
Andrew M. Intlekofer, Alan H. Shih, Bo Wang, Abbas Nazir, Ariën S. Rustenburg, Steven K. Albanese, Minal Patel, Christopher Famulare, Fabian M. Correa, Naofumi Takemoto, Vidushi Durani, Hui Liu, Justin Taylor, Noushin Farnoud, Elli Papaemmanuil, Justin R. Cross, Martin S. Tallman, Maria E. Arcila, Mikhail Roshal, Gregory A. Petsko, Bin Wu, Sung Choe, Zenon D. Konteatis, Scott A. Biller, John D. Chodera, Craig B. Thompson, Ross L. Levine, and Eytan M. Stein
Nature 559:125, 2018. [DOI] [PDF]
We probe the biochemical and biophysical mechanisms underlying an unusual set of clinical resistance mutations that appeared in trans in the IDH2 dimer interface in cancer patients treated with IDH2 inhibitors.
Yosi Shamay, Janki Shah, Mehtap Işık, Aviram Mizrachi, Josef Leibold, Darjus F. Tschaharganeh, Daniel Roxbury, Januka Budhathoki-Uprety, Karla Nawaly, James L. Sugarman, Emily Baut, Michelle R. Neiman, Megan Dacek, Kripa S. Ganesh, Darren C. Johnson, Ramya Sridharan, Karen L. Chu, Vinagolu K. Rajasekhar, Scott W. Lowe, John D. Chodera, and Daniel A. Heller.
Nature Materials 17:361, 2018. [DOI] [PDF] [Supporting Info] [nano-drugbank]
In a collaboration with the Heller Lab at MSKCC, we show how indocyanine nanoparticles can package insoluble selective kinase inhibitors with high mass loadings and efficiently deliver them to tumors.
At low pH, metabolic enzymes lactate dehydrogenase and malate dehydrogenase undergo shifts in substrate utilization that have high relevance to cancer metabolism due to surprisingly simple protonation state effects.
Xu Jianing, Pham CG, Albanese SK, Dong Yiyu, Oyama T, Lee CH, Rodrik-Outmezguine V, Yao Z, Han S, Chen D, Parton DL, Chodera JD, Rosen N, Cheng EH, and Hsieh J. Journal of Clinical Investigation 126:3526, 2016. [DOI] [PDF]
In work with the James Hsieh lab at MSKCC, we examine the surprising origin of how different clinically-identified cancer-associated mutations in MTOR activate the kinase through distinct mechanisms.
Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianweng A. Feng, Daniel F. Ortwine, David L. Mobley, and John D. Chodera.
Journal of Computer-Aided Molecular Design 30:945, 2016. [DOI] [bioRxiv] [PDF] // data: [GitHub]
Solicited manuscript for special issue of the Journal of Computer Aided Molecular Design on the SAMPL5 Challenge.
The SAMPL Challenges have driven predictive physical modeling for ligand:protein binding forward by focusing the community on a series of blind challenges that evaluate performance on blind datasets, focus attention on current challenges for physical modeling techniques, and provide high-quality experimental datasets to the community after the challenge is over. For many years, challenges focused around hydration free energies have proven to be extremely useful, with theory now able to determine when experiment is wrong. To replace these challenges, since no more hydration free energy data is being measured, we proposed to use the partition or distribution coefficients of small druglike molecules between aqueous and apolar phases. We report the collection of cyclohexane-water partition data for a set of compounds used to drive the SAMPL5 distribution coefficient challenge, providing the experimental data, methodology, and insight for future iterations of this challenge.
We demonstrate a new tool that enables---for the first time---massively parallel molecular simulation studies of biomolecular dynamics at the superfamily scale, illustrating its application to protein tyrosine kinases, an important class of drug targets in cancer.
We present a simple scheme for automatically selecting how much initial simulation data to discard to equilibration or burn-in based on maximizing the number of statistically uncorrelated samples in the dataset.
Keywords: molecular simulation; molecular dynamics; burn-in; equilibration; production; analysis
Sonya M. Hanson, Sean Ekins, and John D. Chodera.
Journal of Computer Aided Molecular Design 29:1073, 2015. [DOI] [PDF] // IPython notebook [GitHub] // preprint: [bioRxiv]
Inspired by this In the Pipeline blog post
The drug development community faced a puzzling challenge when a disturbing paper published in PLoS One demonstrated results from the same assay performed with different dispensing technologies both varied wildly and significantly different in magnitude of reported potencies. Inspired by a talk given at the 2014 CADD GRC by Cosma Shalizi on bootstrapping to model error, we show how this simple idea can help explain a large amount of the discrepancy in this assay, and provide simple mathematical tools and an IPython notebook illustrating how easy it is to model the error and bias in experimental assays even when other information about assay reliability is unavailable.
We present a new mathematical framework for unifying various two-state rate theories presented in the physical chemistry literature over many decades, and provide a quantitative way to measure reaction coordinate quality.
We show how bound ligand poses can be identified even when the location of the binding sites are unknown using the machinery of alchemical modern free energy calculations on graphics processors.
A new inexpensive polarizable model of liquid water for next-generation forcefields is derived using an automated parameterization engine.
The finite-timestep errors in molecular dynamics simulations can be interpreted as a form of nonequilibrium work. We show how this leads to straightforward schemes for correcting for these errors or assessing their impact.
Keywords: velocity verlet with Velocity randomization; VVVR; nonequilibrium free energy; integrator error; nonequilibrium integration
Peter Eastman, Mark S. Friedrichs, John D. Chodera, Randy J. Radmer, Chris M. Bruns, Joy P. Ku, Kyle A. Beauchamp, T. J. Lane, Lee-Ping Wang, Diwakar Shukla, Tony Tye, Mike Houston, Timo Stich, Christoph Klein, Michael R. Shirts, and Vijay S. Pande.
J. Chem. Theor. Comput. 9:461, 2013. [DOI] [PDF]
We describe the latest version of an open-source, GPU-accelerated library and toolkit for molecular simulation.
Popular constant-force-feedback single-molecule experiments can cause severe artifacts in single-molecule force spectroscopy data. We demonstrate a simple alternative that eliminates these artifacts.