Histone variants fine-tune transcription, replication, DNA damage repair, and faithful chromosome segregation. Whether and how nucleosome variants encode unique mechanical properties to their cognate chromatin structures remains elusive. Here, using in silico and in vitro nanoindentation methods, extending to in vivo dissections, we report that histone variant nucleosomes are intrinsically more elastic than their canonical counterparts. Furthermore, binding proteins, which discriminate between histone variant nucleosomes, suppress this innate elasticity and also compact chromatin. Interestingly, when we overexpress the binding proteins in vivo, we also observe increased compaction of chromatin enriched for histone variant nucleosomes, correlating with diminished access. Taken together, these data suggest a plausible link between innate mechanical properties possessed by histone variant nucleosomes, the adaptability of chromatin states in vivo, and the epigenetic plasticity of the underlying locus.

BackgroundPosttranslational modifications of core histones are correlated with changes in transcriptional status, chromatin fiber folding, and nucleosome dynamics. However, within the centromere-specific histone H3 variant CENP-A, few modifications have been reported, and their functions remain largely unexplored. In this multidisciplinary report, we utilize in silico computational and in vivo approaches to dissect lysine 124 of human CENP-A, which was previously reported to be acetylated in advance of replication.ResultsComputational modeling demonstrates that acetylation of K124 causes tightening of the histone core and hinders accessibility to its C-terminus, which in turn diminishes CENP-C binding. Additionally, CENP-A K124ac/H4 K79ac containing nucleosomes are prone to DNA sliding. In vivo experiments using a CENP-A acetyl or unacetylatable mimic (K124Q and K124A, respectively) reveal alterations in CENP-C levels and a modest increase in mitotic errors. Furthermore, mutation of K124 results in alterations in centromeric replication timing. Purification of native CENP-A proteins followed by mass spectrometry analysis reveals that while CENP-A K124 is acetylated at G1/S, it switches to monomethylation during early S and mid-S phases. Finally, we provide evidence implicating the histone acetyltransferase (HAT) p300 in this cycle.ConclusionsTaken together, our data suggest that cyclical modifications within the CENP-A nucleosome contribute to the binding of key kinetochore proteins, the integrity of mitosis, and centromeric replication. These data support the paradigm that modifications in histone variants can influence key biological processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-017-0124-6) contains supplementary material, which is available to authorized users.

Post-translational modifications (PTMs) of core histones have studied for over 2 decades, and are correlated with changes in transcriptional status, chromatin fiber folding, and nucleosome dynamics. However, within the centromere-specific histone H3 variant CENP-A, few modifications have been reported, and their functions remain largely unexplored. In this multidisciplinary report, we utilize in silico computational and in vivo approaches to dissect lysine 124 of human CENP-A, which was previously reported to be acetylated in advance of replication. Computational modeling demonstrates that acetylation of K124 causes tightening of the histone core, and hinders accessibility to its C-terminus, which in turn diminishes CENP-C binding. Additionally, CENP-A K124ac/H4 K79ac containing nucleosomes are prone to DNA sliding. In vivo experiments using an acetyl or unacetylatable mimic (CENP-A K124Q and K124A respectively) reveal alterations in CENP-C levels, and a modest increase in mitotic errors.Furthermore, mutation of K124 results in alterations in centromeric replication timing,

Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)�a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n•ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A-or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.

Histone variants regulate replication, transcription, DNA damage repair, and chromosome segregation. Though widely accepted as a paradigm, it has not been rigorously demonstrated that histone variants encode unique mechanical properties. Here, we present a new theoretical approach called Minimal Cylinder Analysis (MCA) to determine the Young's modulus of nucleosomes from all-atom Molecular Dynamics (MD) simulations. Recently, we validated this computational analysis against in vitro single-molecule nanoindentation of histone variant nucleosomes. In this report, we further extend MCA to study the biophysical properties of hybrid nucleosomes that are known to exist in human cancer cells and contain H3 histone variants CENP-A and H3.3. We investigate the mechanism by which the elasticity of these heterotypic 2 nucleosomes augments cryptic binding surfaces. Further, we derive biological predictions that might arise when such heterotypic nucleosomes take over large parts of the genome. Statement of SignificanceNucleosomes are the base unit of eukaryotic genome organization. Histone variants create unique local chromatin domains that fine-tune transcription, replication, DNA damage repair, and faithful chromosome segregation. It is becoming increasingly clear that the mechanical response of chromatin, through material properties such as elasticity, regulates genetic function.We developed a theoretical method, validated by in vitro nanoindentation studies, called Minimal Cylinder Analysis (MCA), to determine the Young's modulus of nucleosomes from Molecular Dynamics simulations. We then postulate specific biological predictions about oncogenic hybrid nucleosomes using MCA. In the future, this computational method can serve as a fast and high-throughput tool to discern how macromolecular systems respond to mechanical forces.

The nucleus has been studied for well over 100 years, and chromatin has been the intense focus of experiments for decades. In this review, we focus on an understudied aspect of chromatin biology, namely the chromatin fiber polymer’s mechanical properties. In recent years, innovative work deploying interdisciplinary approaches including computational modeling, in vitro manipulations of purified and native chromatin have resulted in deep mechanistic insights into how the mechanics of chromatin might contribute to its function. The picture that emerges is one of a nucleus that is shaped as much by external forces pressing down upon it, as internal forces pushing outwards from the chromatin. These properties may have evolved to afford the cell a dynamic and reversible force-induced communication highway which allows rapid coordination between external cues and internal genomic function.

The nucleus has been studied for well over 100 years, and chromatin has been the intense focus of experiments for decades. In this review, we focus on an understudied aspect of chromatin biology, namely the chromatin fiber polymer's mechanical properties. In recent years, innovative work deploying interdisciplinary approaches including computational modeling, in vitro manipulations of purified and native chromatin have resulted in deep mechanistic insights into how the mechanics of chromatin might contribute to its function. The picture that emerges is one of a nucleus that is shaped as much by external forces pressing down upon it, as internal forces pushing outwards from the chromatin. These properties may have evolved to afford the cell a dynamic and reversible force-induced communication highway which allows rapid coordination between external cues and internal genomic function.

DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to employ data from prior DEL screen(s) to gain information about which building blocks are most likely to be productive when designing new DELs for the same target. We demonstrate that similar building blocks have similar probabilities of forming compounds that bind. We then build a model from the inference that the combined behavior of individual building blocks is predictive of whether an overall compound binds. We illustrate our approach on a set of three-cycle OpenDEL libraries screened against soluble epoxide hydrolase (sEH) and report performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data. Lastly, we provide a discussion on how we believe this informatics workflow could be applied to benefit researchers in their specific DEL campaigns.