Small-molecule targeting of MUSASHI RNA-binding activity in acute myeloid leukemia

Gerard Minuesa, Steven K Albanese, Arthur Chow, Alexandra Schurer, Sun-Mi Park, Christina Z. Rotsides, James Taggart, Andrea Rizzi, Levi N. Naden, Timothy Chou, Saroj Gourkanti, Daniel Cappel, Maria C Passarelli, Lauren Fairchild, Carolina Adura, Fraser J Glickman, Jessica Schulman, Christopher Famulare, Minal Patel, Joseph K Eibl, Gregory M Ross, Derek S Tan, Christina S Leslie, Thijs Beuming, Yehuda Goldgur, John D Chodera, Michael G Kharas
Preprint: [bioRxiv]

We use absolute alchemical free energy calculations to identify the likely interaction site for a small hydrophobic ligand that shows activity against MUSASHI in AML.

Toward learned chemical perception of force field typing rules

Camila Zanette Caitlin C. Bannan Christopher I. Bayly Josh Fass Michael K. Gilson Michael R. Shirts John Chodera David L. Mobley
Preprint ahead of submission. [ChemRxiv] [GitHub]

We show how machine learning can learn typing rules for molecular mechanics force fields within a Bayesian statistical framework.

An open library of human kinase domain constructs for automated bacterial expression


Steven K. Albanese*, Daniel L. Parton*, Mehtap Isik**, Lucelenie Rodríguez-Laureano**, Sonya M. Hanson,  Julie M. Behr, Scott Gradia, Chris Jeans, Nicholas M. Levinson, Markus A. Seeliger, and John D. Chodera.
* co-first author; ** co-second author
Biochemistry, in press. [bioRxiv]
Interactive data browser: []
Plasmids available via AddGene

Human kinase catalytic domains---the therapeutic target of selective kinase inhibitors used in the treatment of cancer and other diseases---are notoriously difficult and expensive to express in insect or human cells. Here, we utilize the phosphatase co-expression technology developed by Markus Seeliger (now at Stony Brook) to develop a library of human kinase catalytic domains for facile and inexpensive expression in bacteria.

Quantifying configuration-sampling error in Langevin simulations of complex molecular systems


Josh Fass, David Sivak , Gavin E. Crooks, Kyle A. Beauchamp, Benedict Leimkuhler, and John Chodera.
Entropy 20:318, 2018. [DOI] [GitHub] [bioRxiv preprint]

Molecular dynamics simulations necessarily use a finite timestep, which introduces error or bias in the sampled configuration space density that grows rapidly with increasing timestep. For the first time, we show how to compute a natural measure of this error---the KL divergence---in both phase and configuration space for a widely used family of Langevin integrators, and show that VRORV is generally superior for simulation of molecular systems.

Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

David Mobley, Caitlin C. Bannan, Andrea Rizzi, Christopher I. Bayly, John D. Chodera, Victoria T Lim, Nathan M. Lim, Kyle A. Beauchamp, Michael R. Shirts, Michael K. Gilson, Peter K. Eastman.
Preprint ahead of submission: [DOI]

We describe the philosophy behind a modern approach to molecular mechanics forcefield parameterization, and present initial results for the first SMIRNOFF-encoded forcefield: SMIRNOFF99Frosst.

A dynamic mechanism for allosteric activation of Aurora kinase A by activation loop phosphorylation

Emily F. Ruff, Joseph M. Muretta, Andrew Thompson, Eric W. Lake, Soreen Cyphers, Steven K. Albanese, Sonya M. Hanson, Julie M. Behr, David D. Thomas,  John D. Chodera, and Nicholas M. Levinson. 
eLife 7:e32766, 2018. [DOI] [bioRxiv]

We show that, contrary to the canonical belief that activation shifts DFG-out to DFG-in populations, phosphorylation of AurA does not shift DFG-in/out equilibrium but instead remodels the conformational distribution of the DFG-in state.

Quantitative self-assembly prediction yields targeted nanomedicines

Yosi ShamayJanki Shah, Mehtap Işık, Aviram MizrachiJosef LeiboldDarjus F. TschaharganehDaniel RoxburyJanuka Budhathoki-UpretyKarla NawalyJames L. SugarmanEmily BautMichelle R. NeimanMegan DacekKripa S. GaneshDarren C. JohnsonRamya SridharanKaren L. ChuVinagolu K. RajasekharScott 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.

Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations

Kevin Hauser, Christopher Negron, Steven K. Albanese, Soumya Ray, Thomas Steinbrecher, Robert Abel, John D. Chodera, and Lingle Wang.
Manuscript prior to publication: [bioRxiv] [input files and analysis scripts]

In our first collaborative paper with Schrödinger, we present the first comprehensive benchmark assessing the ability for alchemical free energy calculations to predict clinical mutational resistance or susceptibility to targeted kinase inhibitors using the well-studied kinase Abl, the target of therapy for chronic myelogenous leukemia (CML).

Biomolecular simulations under realistic macroscopic salt conditions

Gregory A. Ross, Ariën S. Rustenburg, Patrick B. Grinaway, Josh Fass, and John D. Chodera
Journal of Physical Chemistry B, in press. [DOI] [bioRxiv] [simulation code] [results and analysis scripts]

We show how NCMC can be used to implement an efficient osmostat in molecular dynamics simulations to model realistic fluctuations in ion environments around biomolecules, and illustrate how the local salt environment around biological macromolecules can differ substantially from bulk.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg, Josh Fass, Gregory Ross, John D. Chodera, and David Mobley.
Journal of Physical Chemistry B, in press: [DOI] [ChemRxiv] [GitHub]

Nonequilibrium candidate Monte Carlo can be used to accelerate the sampling of ligand binding modes by orders of magnitude over instantaneous Monte Carlo.

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Bernard R. Brooks, Vijay S. Pande. PLoS Computational Biology 13:e1005659, 2017. [DOI] [bioRxiv] [website] [GitHub]

We describe the latest version of OpenMM, a GPU-accelerated framework for high performance molecular simulation applications.

Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database

Guilherme Duarte Ramos MatosDaisy Y. KyuHannes H. LoefflerJohn D. ChoderaMichael R. ShirtsDavid Mobley
Journal of Chemical Engineering Data, Journal of Chemical Engineering Data 62:1559, 2017. [DOI] [bioRxiv] [GitHub]

We review alchemical methods for computing solvation free energies and present an update (version 0.5) to the FreeSolv database of experimental and calculated hydration free energies of neutral compounds.

L-2-Hydroxyglutarate production arises from noncanonical enzyme function at acidic pH

Intlekofer A, Wang B, Liu H, Shah H, Carmona-Fontaine C, Rustenburg AS, Salah S, Gunner MR, Chodera JD, Cross JR, and Thompson CB.
Nature Chemical Biology 13:494, 2017. [DOI] [PDF] [GitHub]

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.

A water-mediated allosteric network governs activation of Aurora kinase A

Cyphers S, Ruff E, Behr JM, Chodera JD, and Levinson NM.
Nature Chemical Biology 13:402, 2017. [DOI] [PDF] [GitHub]

Over 50 microseconds of aggregate simulation data on Folding@home reveal a surprisingly stable hydrogen bond network underlies allosteric activation by Tpx2.

Mechanistically distinct cancer-associated mTOR activation clusters predict sensitivity to rapamycin

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.

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

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.

Ensembler: Enabling high-throughput molecular simulations at the superfamily scale

Daniel L. Parton, Patrick B. Grinaway, Sonya M. Hanson, Kyle A. Beauchamp, and John D. Chodera
PLoS Computational Biology 12:e1004728, 2016. [DOI] [PDF] [bioRxiv] / data: [Dryad] / code: [GitHub]

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.

A simple method for automated equilibration detection in molecular simulations

John D. Chodera.
J. Chem. Theor. Comput. 12:1799, 2016. [DOI[PDF] / code to reproduce manuscript: [GitHub] / preprint: [bioRxiv] / available in pymbar.timeseries

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

Modeling error in experimental assays using the bootstrap principle: Understanding discrepancies between assays using different dispensing technologies

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.