In addition to targeting kinases, our laboratory is exploring the use of Markov state models (MSMs) to enable the computational discovery of allosteric sites
DRUGGING THE UNDRUGGABLE: TARGETING KRAS
Ras family proteins, important in the control of cell growth via signaling, are commonly mutated in human cancer. Activating mutations in Ras are a leading cause of resistance to modern targeted therapy, and patients who harbor Ras mutations have considerably poorer prognoses than those with wild type Ras. Targeting Ras has proven difficult because oncogenic mutations activate Ras primarily by ablating enzymatic activity, leaving classical enzyme inhibition strategies unworkable. The high affinity of Ras for GTP---which locks Ras in an active conformation---combined with high intracellular GTP concentrations makes outcompeting the bound nucleotide extremely difficult. A new covalent inhibitor for K-Ras G12C from the Shokat lab shows the potential for inactivation by allosteric modulators, but the G12C mutation is present in only small fraction of cancers and these inhibitors, which covalently bind mutant Cys12, present a ready path to inhibitor resistance by mutating position 12 to another activating mutation.
Our laboratory is pursuing a combined computational-experimental strategy to develop noncovalent allosteric modulators of Ras family proteins that circumvents these limitations. Initially focusing on K-Ras, we have carried out massively parallel simulations on Folding@home to construct Markov state models of the conformational states accessible with a small energetic penalty (up to 10 kT). Using a site-mapping algorithm, we identify conformations that may be functionally inactive (such as those similar to the GDP-bound state) and use virtual screening of soluble small molecule fragment libraries to identify potential scaffolds that could be derivatized for enhanced potency. Using a panel of K-Ras mutants in which tryptophan residues have been introduced at various sites to function as fluorescence reporters of ligand-stabilized allosteric conformational change, we plan to experimentally screen these computationally-identified fragments for weak binding using our automated biophysical laboratory. We will then utilize our automated ligand design strategy to identify synthetically-feasible derivatives that will be synthesized to increase potency.
Patrick Grinaway (PBSB Graduate Student)
Markov state models of biomolecular conformational dynamics
John D. Chodera and Frank Noé.
Curr. Opin. Struct. Biol., 25:135-144, 2014. [DOI] [PDF]
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 Comput. Biol. 12:e1004728, 2016. [DOI] [PDF] [bioRxiv] / data: [Dryad] / code: [GitHub]
Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics
Kai Wang K, John D. Chodera, Yanzhi Yang, and Michael R. Shirts.
J. Comput. Aid. Mol. Des. 27:989, 2013. [DOI] [PDF]
Markov models of molecular kinetics: Generation and validation
Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina Keller, Martin Fischbach, Martin Held, John D. Chodera, Christof Schüttle, and Frank Noé.
J. Chem. Phys. 134:174105, 2011. [DOI] [PDF]
Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
John D. Chodera*, Nina Singhal*, William C. Swope, Jed W. Pitera, Vijay S. Pande, and Ken A. Dill.
J. Chem. Phys. 126:155101, 2007. [DOI] [PDF]