The Chodera lab was awarded an NIH R01 research grant to study the role of conformational reorganization energy in selective kinase inhibition. Because even evolutionarily related kinase can have vastly different energetic costs to adopt inhibitor-bound conformations, these difference can be exploited to design new selective inhibitors, but only with computational approaches to elucidate hidden conformational states and their energetic penalties.
You can read more about our research on selective kinase inhibitor design, or download the entire NIH R01 proposal we submitted here.
The Chodera lab has been awarded an NSF grant funding Data-Driven Discovery Science in Chemistry (D3SC) for a collaborative project with the laboratory of Michael Shirts (University of Colorado) that explores the use of advanced Bayesian methodologies for parameterization in molecular mechanics forcefields of small molecular liquids.
Rafal Wiewiora, a graduate student in the Tri-Institutional Program in Chemical Biology, has been awarded a DoD Peer Reviewed Cancer Research Horizon Award. This two-year grant supports exceptional junior-level scientists pursuing research in cancer with the guidance of a faculty mentor. Rafal’s project investigates the conformational heterogeneity of histone methyltransferases using molecular dynamics simulations on the Folding@home platform, a worldwide distributed computing project where hundreds of thousands of people around the world contribute their computing power toward the understanding of cancer targets. These models of conformational dynamics will be vital in aiding the in silico design of selective chemical probes to understand the roles that these methyltransferases play in cancer, as well as to develop new strategies to inhibit them. A native of Poland, Rafal received his MChem degree from the University of Oxford.
PBSB graduate student Patrick Grinaway will be funded by the first Open Science Fellowship from Silicon Therapeutics to develop new open source methodologies and software for scalable relative free energy calculations, building the foundation for truly automated small molecule design. This code will be part of the growing OpenMM software ecosystem, which makes use of OpenMM's GPU acceleration to achieve high performance while still being easy to build complex molecular modeling applications in Python.
Read more about the science here.
Congratulations to TPCB graduate student Chaya Stern for receiving a Diversity Scholarship to attend SciPy 2017 in Austin, TX July 10-16. SciPy is a conference focusing on scientific computing with Python, and brings together a community of open source software developers and users from industry, academia, and government to show off their projects, learn from each other, and collaborate to develop better code.
We're grateful to JumpTrading and NumFocus for providing the funds for this fellowship.
Congratulations to postdoc Gregory Ross for being awarded an inaugural MolSSI Postdoctoral Software Science Fellowship! Greg's project focuses on building a toolbox of self-tuning Monte Carlo methods for use alongside molecular dynamics based Markov chain Monte Carlo (MCMC) sampling to greatly enhance the efficiency of molecular simulations and facilitate the construction of new sampling algorithms. Read on for a more detailed description of his project.
An automatic sampling toolbox for molecular simulation
Gregory Ross, DPhil
In the biomolecular simulation community, molecular dynamics (MD) is the most popular method for sampling molecular configurations. MD is well suited to capturing global, concerted movements, but it struggles to sample configurations that are separated by large energetic barriers. As a result, configurations taken from MD simulations tend to be from the same local region. An equivalent problem is encountered in Bayesian inference when drawing samples from a high-dimensional, multimodal posterior distribution. MD-related techniques are a small subset of a large class of Markov chain Monte Carlo (MCMC) techniques, and practitioners in both the biomolecular simulation and Bayesian inference communities have, in principle, a great deal of choice on which sampling method to use. However, it is difficult to know which MCMC method will the most efficient at generating uncorrelated samples for a particular system. In my project, I will develop a toolbox for MCMC that is agnostic with regard to what is being sampled, whether a protein conformation or posterior distribution. I will focus on using adaptive MCMC methods to help select the sampling technique that works best for a given problem. The general idea is to write a library of MCMC tools and a software interface that allows for the construction of MCMC “blocks” which can be mixed together and easily applied. The goal is to simplify sampling from difficult distributions and, ultimately, to widen the range of phenomena currently accessible by molecular simulation.
Congratulations to TPCB graduate student Mehtap Isik, who is the 2017-2018 recipient of the Doris J. Hutchison Fellowship from the Sloan Kettering Division of the Weill Cornell Graduate School of Medical Sciences. Her project focuses on the development of model protein:ligand systems for advancing the field of predictive quantitative computational modeling for drug discovery using robotic wetlab experiments and advanced GPU computing, and is described in this proposal she submitted to the fellowship competition.
At low pH, such as under anoxic conditions relevant to diseases like cancer, some metabolic enzymes like LDHA can shift their substrate preferences and cause the accumulation of metabolites that lock the cell into pathogenic states. The mystery of how and why these enzymes start to prefer alternative substrates has lingered.
In a paper from the Thompson laboratory at MSKCC just published in Nature Chemical Biology, graduate student Ariën S. ("Bas") Rustenburg in the lab, together with collaborators from the Gunner lab at CCNY, used modeling to show how protonation state effects explain why substrates like alpha-ketoglutarate that position a carboxylate tail proximal to LDHA's Q100 greatly increase turnover rates at low pH.
L-2-Hydroxyglutarate production arises from noncanonical enzyme function at acidic pH.
Andrew M. Intlekofer, Bo Huang, Hui Liu, Hardik Shah, Carlos Carmona-Fontaine, Ariën S. Rustenburg, Salah Salah, M R Gunner, John D. Chodera, Justin R. Cross, and Craig B. Thompson.
Nature Chemical Biology, in press. [DOI] [GitHub]
I had the great pleasure of being the final speaker at OpenEye's ever-stimulating CUP meeting in Santa Fe, NM, now in its 17th year. David Mobley and I were able to demonstrate the ease by which hydration free energies could be computed on GPUs in the cloud using open source tools.
Many people requested a copy of my slides, so here they are! [PDF]
I had a wonderful visit to give the Harvard Widely Applied Mathematics Seminar yesterday! Many thanks to Alpha Lee, Michael Brenner, and all of the other scientists I spoke with about their exciting research.
Here are the slides for the talk I gave on nonequilibrium statistical mechanics approaches for drug design: [PDF]
In collaboration with the Nicholas Levinson lab at the University of Minnesota, we have just published a paper in Nature Chemical Biology using experiment and simulation to probe the mechanism of allosteric activation of Aurora A kinase (AurA). AurA is found to be hyperphosphorylated in approximately 10% of melanoma patients due to mutations that deactivate the protein phosphatase PP6, leading to defects in chromosome segregation and genomic stability.
AurA kinase plays two distinct roles in mitosis, with a centrosomal pool of kinase activated by phosphorylation similarly to other kinases, but a separate pool controlled by a more exotic mechanism of binding to the spindle-associated protein Tpx2. Using an aggregate of several microseconds of data generated on Folding@home to study wild-type AurA and some engineered mutants, we helped the Levinson lab puzzle out a key role of highly stable waters localized in the active site that mediate allosteric communication in the Tpx2-mediated activation of AurA.
Soreen Cyphers, Emily F Ruff, Julie M Behr, John D Chodera, and Nicholas M Levinson.
A water-mediated allosteric network governs activation of Aurora kinase A
Nature Chemical Biology, in press. [DOI] [GitHub]
We have made all the explicit-solvent Folding@home simulation data and analysis scripts used in this paper available for download:
The trajectory data itself is too large to share via GitHub, so we make it available via the Open Science Framework.
We've shared on Figshare a microsecond trajectory of the human tyrosine kinase ABL1 kinase domain generated by our lab on Folding@home for an openpathsampling-related project, but we figured this may be of general interest! Thanks to postdoc Sonya Hanson for putting this together.
Christmas comes early! We've released a beta of OpenMM 7.1, packed with speed improvements and new features, including:
- Optimized clang builds of both Anaconda packages and ZIP installers, offering anywhere from a 30% to 50x boost for some applications that use the CPU platform.
- Custom Forces can now compute energy derivatives with respect to global parameters! Lambda dynamics can now be implemented via a CustomIntegrator.
- Gay-Berne ellipsoid potential!
- Bonded forces can now use periodic boundary conditions.
To get the updated OpenMM conda package, use the beta channel:
conda install -c omnia/label/beta openmm==7.1.0
If you have already been using the dev channel 7.1.0 nightly builds, force a downgrade first:
# Force downgrade to 7.0.1 conda install --yes -c omnia openmm==7.0.1 # Clear local cache conda clean -plti --yes # Install the beta conda install --yes -c omnia/label/beta openmm==7.1.0
Nightly dev builds are now called 7.2.0. You can always get the latest version with:
conda install --yes -c omnia/label/dev openmm
The NIH recently issued a request for information (RFI) on the role of preprints in NIH applications. You can read my response here. Many others shared insightful responses publicly, and ASAPbio has indexed them here.
Our lab is a core member of the Folding@home Consortium, a research network of 11 laboratories around the world that use Folding@home to study the molecular mechanisms underlying cancer and other diseases and identify new routes toward therapies. Together, we are aiming to recruit one million volunteers donating compute cycles to help us!
Please join us, especially if you have a GPU: Folding@home can harness the power of your GPU.
It costs nothing (other than your electrical bill) and provides a way to donate your idle computer cycles to biomedical research.
Other useful links:
- Learn about the Folding@home "One in a Million" campaign
- Read about how we use Folding@home to study molecular mechanisms of cancer
- See how far we've come according to current Folding@home statistics
- Learn more about the project via the Folding@home main page
- Read the over 130 scientific papers that have come from the Folding@home project