We report the design and characterization of UNC3866, a potent antagonist of the methyl-lysine (Kme) reading function of the Polycomb CBX and CDY families of chromodomains. Polycomb CBX proteins regulate gene expression by targeting Polycomb Repressive Complex 1 to sites of H3K27me3 via their chromodomains. UNC3866 binds the chromodomains of CBX4 and CBX7 most potently with a Kd of ∼100 nM for each, and is 6- to 18-fold selective versus seven other CBX and CDY chromodomains while being highly selective versus >250 other protein targets. X-ray crystallography revealed that UNC3866 closely mimics the interactions of the methylated H3 tail with these chromodomains. UNC4195, a biotinylated derivative of UNC3866, was used to demonstrate that UNC3866 engages intact PRC1 and that EED incorporation into PRC1 is isoform-dependent in PC3 prostate cancer cells. Finally, UNC3866 inhibits PC3 cell proliferation, a known CBX7 phenotype, while UNC4219, a methylated negative control compound, has negligible effects.

Histone post-translational modifications regulate chromatin structure and function largely through interactions with effector proteins that often contain multiple histone-binding domains. While significant progress has been made characterizing individual effector domains, the role of paired domains and how they function in a combinatorial fashion within chromatin are poorly defined. Here we show that the linked tandem Tudor and plant homeodomain (PHD) of UHRF1 (ubiquitin-like PHD and RING finger domain-containing protein 1) operates as a functional unit in cells, providing a defined combinatorial readout of a heterochromatin signature within a single histone H3 tail that is essential for UHRF1-directed epigenetic inheritance of DNA methylation. These findings provide critical support for the ''histone code'' hypothesis, demonstrating that multivalent histone engagement plays a key role in driving a fundamental downstream biological event in chromatin.

The epigenetic inheritance of DNA methylation requires UHRF1, a histone- and DNA-binding RING E3 ubiquitin ligase that recruits DNMT1 to sites of newly replicated DNA through ubiquitylation of histone H3. UHRF1 binds DNA with selectivity towards hemi-methylated CpGs (HeDNA); however, the contribution of HeDNA sensing to UHRF1 function remains elusive. Here, we reveal that the interaction of UHRF1 with HeDNA is required for DNA methylation but is dispensable for chromatin interaction, which is governed by reciprocal positive cooperativity between the UHRF1 histone- and DNA-binding domains. HeDNA recognition activates UHRF1 ubiquitylation towards multiple lysines on the H3 tail adjacent to the UHRF1 histone-binding site. Collectively, our studies are the first demonstrations of a DNA-protein interaction and an epigenetic modification directly regulating E3 ubiquitin ligase activity. They also define an orchestrated epigenetic control mechanism involving modifications both to histones and DNA that facilitate UHRF1 chromatin targeting, H3 ubiquitylation, and DNA methylation inheritance.DOI: http://dx.doi.org/10.7554/eLife.17101.001

SUMMARY Access to high quality antibodies is a necessity for the study of histones and their posttranslational modifications (PTMs). Here we debut The Histone Antibody Specificity Database (http://www.histoneantibodies.com), an online and expanding resource cataloguing the behavior of widely used commercially available histone antibodies by peptide microarray. This interactive web portal provides a critical resource to the biological research community who routinely use these antibodies as detection reagents for a wide range of applications.

SUMMARY Histone post-translational modifications (PTMs) are important genomic regulators often studied by chromatin immunoprecipitation (ChIP), whereby their locations and relative abundance are inferred by antibody capture of nucleosomes and associated DNA. However, the specificity of antibodies within these experiments have not been systematically studied. Here, we use histone peptide arrays and internally calibrated ChIP (ICeChIP) to characterize 52 commercial antibodies purported to distinguish the H3K4 methylforms (me1, me2, and me3, with each ascribed distinct biological functions). We find that many widely-used antibodies poorly distinguish the methylforms and that high- and low-specificity reagents can yield dramatically different biological interpretations, resulting in substantial divergence from the literature for numerous H3K4 methylform paradigms. Using ICeChIP, we also discern quantitative relationships between enhancer H3K4 methylation and promoter transcriptional output and can measure global PTM abundance changes. Our results illustrate how poor antibody specificity contributes to the “reproducibility crisis,” demonstrating the need for rigorous, platform-appropriate validation.

One mechanism of regulating the catalytic activity of protein kinases is through conformational transitions. Despite great diversity in the structural changes involved in the transitions, a certain set of changes within the kinase domain (KD) has been observed for many kinases including Src and CDK2. We investigated this conformational transition computationally to identify the topological features that are energetically critical to the transition. Results from both molecular dynamics sampling and transition path optimization highlight the displacement of the αC helix as the major energy barrier, mediating the switch of the KD between the active and down-regulated states. The critical role of the αC helix is noteworthy by providing a rationale for a number of activation and deactivation mechanisms known to occur in cells. We find that kinases with the αC helix displacement exist throughout the kinome, suggesting that this feature may have emerged early in evolution.

We apply the adaptive biasing potential (ABP) method to optimize the principal curve defining a conformational transition between two known end states and to subsequently compute the one-dimensional potential of mean force as a function of arc length along the principal curve. This approach allows the use of the ABP method in a collective variable space of arbitrary dimension and offers several advantages over line-search methods. First, configurations are neither generated along an initial path for the transition nor equilibrated during evolution of the path. Second, and most importantly, the powerful sampling provided by the ABP serves to accelerate the dynamics during the optimization and computation of the free energy. Finally, the free energy is formulated as a potential of mean force that captures changes in the reaction channel along the principal curve, in contrast to the free energy profile evaluated from the local free-energy gradient in restrained path optimization methods. We first demonstrate the ABP formulation of path optimization using a two-dimensional potential surface and then with a more complex system of Src protein tyrosine kinase. The method is shown to be efficient and robust in the case of rugged, free-energy landscapes.

We develop an efficient sampling and free energy calculation technique within the adaptive biasing potential (ABP) framework. By mollifying the density of states we obtain an approximate free energy and an adaptive bias potential that is computed directly from the population along the coordinates of the free energy. Because of the mollifier, the bias potential is "nonlocal", and its gradient admits a simple analytic expression. A single observation of the reaction coordinate can thus be used to update the approximate free energy at every point within a neighborhood of the observation. This greatly reduces the equilibration time of the adaptive bias potential. This approximation introduces two parameters: strength of mollification and the zero of energy of the bias potential. While we observe that the approximate free energy is a very good estimate of the actual free energy for a large range of mollification strength, we demonstrate that the errors associated with the mollification may be removed via deconvolution. The zero of energy of the bias potential, which is easy to choose, influences the speed of convergence but not the limiting accuracy. This method is simple to apply to free energy or mean force computation in multiple dimensions and does not involve second derivatives of the reaction coordinates, matrix manipulations nor on-the-fly adaptation of parameters. For the alanine dipeptide test case, the new method is found to gain as much as a factor of 10 in efficiency as compared to two basic implementations of the adaptive biasing force methods, and it is shown to be as efficient as well-tempered metadynamics with the postprocess deconvolution giving a clear advantage to the mollified density of states method.