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Methyllysine sites in proteins are recognized by an array of reader domains that mediate protein–protein interactions for controlling cellular processes. Herein, we engineer a chromodomain, an essential methyllysine reader, to carry 4-azido- l -phenylalanine (AzF) via amber suppressor mutagenesis and demonstrate its potential to bind and crosslink methylated proteins in human cells. We further develop a first-of-its kind chromodomain variant bearing two AzF units with enhanced crosslinking potential suitable for profiling the transient methyllysine interactome. 

Ten-eleven translocation (TET) enzymes oxidize C–H bonds in 5-methylcytosine (5mC) to hydroxyl (5hmC), formyl (5fC) and carboxyl (5caC) intermediates en route to DNA demethylation. It has remained a challenge to study the function of a single oxidized product. We investigate whether alkyl groups other than methyl could be oxidized by TET proteins to generate a specific intermediate. We report here that TET2 oxidizes 5-ethylcytosine (5eC) only to 5-hydroxyethylcytosine (5heC). In biochemical assays, 5heC acts as a docking site for proteins implicated in transcription, imbuing this modification with potential gene regulatory activity. We observe that 5heC is resistant to downstream wild type hydrolases, but not to the engineered enzymes, thus establishing a unique tool to conditionally alter the stability of 5heC on DNA. Furthermore, we devised a chemical approach for orthogonal labeling of 5heC. Our work offers a platform for synthesis of novel 5-alkylcytosines, provides an approach to ‘tame’ TET activity, and identifies 5heC as an unnatural modification with a potential to control chromatin-dependent processes. 

This paper presents GraphRex, an efficient, robust, scalable, and easy-to-program framework for graph processing on datacenter infrastructure. To users, GraphRex presents a declarative, Datalog-like interface that is natural and expressive. Underneath, it compiles those queries into efficient implementations. A key technical contribution of GraphRex is the identification and optimization of a set of global operators whose efficiency is crucial to the good performance of datacenter-based, large graph analysis. Our experimental results show that GraphRex significantly outperforms existing frameworks---both high- and low-level---in scenarios ranging across a wide variety of graph workloads and network conditions, sometimes by two orders of magnitude.