We describe the discovery of an agonist of the nuclear receptor NR2F1 that specifically activates dormancy programs in malignant cells. The agonist led to a self-regulated increase in NR2F1 mRNA and protein and downstream transcription of a novel dormancy program. This program led to growth arrest of an HNSCC PDX line, human cell lines, and patient-derived organoids in 3D cultures and in vivo. This effect was lost when NR2F1 was knocked out by CRISPR-Cas9. RNA sequencing revealed that agonist treatment induces transcriptional changes associated with inhibition of cell cycle progression and mTOR signaling, metastasis suppression, and induction of a neural crest lineage program. In mice, agonist treatment resulted in inhibition of lung HNSCC metastasis, even after cessation of the treatment, where disseminated tumor cells displayed an NR2F1hi/p27hi/Ki-67lo/p-S6lo phenotype and remained in a dormant single-cell state. Our work provides proof of principle supporting the use of NR2F1 agonists to induce dormancy as a therapeutic strategy to prevent metastasis.

Simulaid performs a large number of simulation‐related tasks: interconversion and modification of structure and trajectory files, optimization of orientation, and a large variety of analysis functions. The program can handle structures in PDB (Berman et al., Nucleic Acids Res 2000, 28, 235), Charmm (Brooks et al., J Comput Chem 4, 187) CRD, Amber (Case et al.), Macromodel (Mohamadi et al., J Comput Chem 1990, 11, 440), Gromos/Gromacs (Hess et al.), InsightII (InsightII. Accelrys Inc.: San Diego, 2005), Grasp (Nicholls et al., Proteins: Struct Funct Genet 1991, 11, 281) .crg, Tripos (Tripos International, S. H. R., St. Louis, MO) .mol2 (input only), and in the MMC (Mezei, M.; MMC: Monte Carlo program for molecular assemblies. Available at: http://inka.mssm.edu/∼mezei/mmc) formats; and trajectories in the formats of Charmm, Amber, Macromodel, and MMC. Analysis features include (but are not limited to): (1) simple distance calculations and hydrogen‐bond analysis, (2) calculation of 2‐D RMSD maps (produced both as text file with the data and as a color‐coded matrix) and cross RMSD maps between trajectories, (3) clustering based on RMSD maps, (4) analysis of torsion angles, Ramachandran (Ramachandran and Sasiskharan, Adv Protein Chem 1968, 23, 283) angles, proline kink (Visiers et al., Protein Eng 2000, 13, 603) angles, pseudorotational (Altona and Sundaralingam, J Am Chem Soc 1972, 94, 8205; Cremer and Pople, J Am Chem Soc 1975, 97, 1354) angles, and (5) analysis based on circular variance (Mezei, J Mol Graphics Model 2003, 21, 463). Torsion angle evolutions are presented in dial plots (Ravishanker et al., J Biomol Struct Dyn 1989, 6, 669). Several of these features are unique to Simulaid. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010

We have developed an improved local move Monte Carlo (LMMC) loop sampling approach for loop predictions. The method generates loop conformations based on simple moves of the torsion angles of side chains and local moves of backbone of loops. To reduce the computational costs for energy evaluations, we developed a grid-based force field to represent the protein environment and solvation effect. Simulated annealing has been used to enhance the efficiency of the LMMC loop sampling and identify low-energy loop conformations. The prediction quality is evaluated on a set of protein loops with known crystal structure that has been previously used by others to test different loop prediction methods. The results show that this approach can reproduce the experimental results with the root mean square deviation within 1.8 Å for all the test cases. The LMMC loop prediction approach developed here could be useful for improvement in the quality the loop regions in homology models, flexible protein–ligand and protein–protein docking studies.