Specific modifications to histones are essential epigenetic markers---heritable changes in gene expression that do not affect the DNA sequence. Methylation of lysine 9 in histone H3 is recognized by heterochromatin protein 1 (HP1), which directs the binding of other proteins to control chromatin structure and gene expression. Here we show that HP1 uses an induced-fit mechanism for recognition of this modification, as revealed by the structure of its chromodomain bound to a histone H3 peptide dimethylated at Nzeta of lysine 9. The binding pocket for the N-methyl groups is provided by three aromatic side chains, Tyr21, Trp42 and Phe45, which reside in two regions that become ordered on binding of the peptide. The side chain of Lys9 is almost fully extended and surrounded by residues that are conserved in many other chromodomains. The QTAR peptide sequence preceding Lys9 makes most of the additional interactions with the chromodomain, with HP1 residues Val23, Leu40, Trp42, Leu58 and Cys60 appearing to be a major determinant of specificity by binding the key buried Ala7. These findings predict which other chromodomains will bind methylated proteins and suggest a motif that they recognize.

Most cancer-related deaths are a result of metastasis, and thus the importance of this process as a target of therapy cannot be understated. By asking ‘how can we effectively treat cancer?’, we do not capture the complexity of a disease encompassing >200 different cancer types — many consisting of multiple subtypes — with considerable intratumoural heterogeneity, which can result in variable responses to a specific therapy. Moreover, we have much less information on the pathophysiological characteristics of metastases than is available for the primary tumour. Most disseminated tumour cells that arrive in distant tissues, surrounded by unfamiliar cells and a foreign microenvironment, are likely to die; however, those that survive can generate metastatic tumours with a markedly different biology from that of the primary tumour. To treat metastasis effectively, we must inhibit fundamental metastatic processes and develop specific preclinical and clinical strategies that do not rely on primary tumour responses. To address this crucial issue, Cancer Research UK and Cancer Therapeutics CRC Australia formed a Metastasis Working Group with representatives from not-for-profit, academic, government, industry and regulatory bodies in order to develop recommendations on how to tackle the challenges associated with treating (micro)metastatic disease. Herein, we describe the challenges identified as well as the proposed approaches for discovering and developing anticancer agents designed specifically to prevent or delay the metastatic outgrowth of cancer.

CHR-2797 is a novel metalloenzyme inhibitor that is converted into a pharmacologically active acid product (CHR-79888) inside cells. CHR-79888 is a potent inhibitor of a number of intracellular aminopeptidases, including leucine aminopeptidase. CHR-2797 exerts antiproliferative effects against a range of tumor cell lines in vitro and in vivo and shows selectivity for transformed over nontransformed cells.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

Background: The site of Jagged/Serrate ligand recognition by Notch is unknown.Results: Two critical residues involved in an intramolecular hydrophobic interaction across the central β-sheet of EGF12 form a ligand-binding platform.Conclusion: The ligand-binding region is adjacent to a Fringe-sensitive residue involved in modulating Notch activity.Significance: The results have implications for understanding receptor/ligand recognition, Notch regulation by O-glycosylation, and the development of paralogue-specific antibodies.