To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This second publication describes advanced aspects of the theory and details of how to customize path ensembles.
To make powerful path sampling techniques broadly accessible and efficient, we have produced a new Python framework for easily implementing path sampling strategies (such as transition path and interface sampling) in Python. This first publication describes some of the theory and capabilities behind the approach.
Kevin Hauser, Christopher Negron, Steven K. Albanese, Soumya Ray, Thomas Steinbrecher, Robert Abel, John D. Chodera, and Lingle Wang.
Communications Biology 1:70, 2018 [DOI] [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).
Trung Hai Nguyen, Arien S. Rustenburg, Stefan G. Krimmer, Hexi Zhang, John D. Clark, Paul A. Novick, Kim Branson, Vijay S. Pande, John D Chodera, David D. L. Minh.
We show how Bayesian inference can produce greatly improved estimates of statistical uncertainty from isothermal titration calorimetry (ITC) experiments, allowing the joint distribution of thermodynamic parameter uncertainties to be inferred.
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
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.
We show how machine learning can learn typing rules for molecular mechanics force fields within a Bayesian statistical framework.
Hanson SM*, Georghiou G*, Miller WT, Rest JS, Chodera JD, Seeliger MA. Submitted.
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.
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: [github.io]
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.
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.
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.
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.
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.
Gregory A. Ross, Ariën S. Rustenburg, Patrick B. Grinaway, Josh Fass, and John D. Chodera
Journal of Physical Chemistry B 122:5466, 2018. [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.
Nonequilibrium candidate Monte Carlo can be used to accelerate the sampling of ligand binding modes by orders of magnitude over instantaneous Monte Carlo.
Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, 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.
Guilherme Duarte Ramos Matos, Daisy Y. Kyu, Hannes H. Loeffler, John D. Chodera, Michael R. Shirts, David 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.
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.