Histone tails harbor a plethora of post-translational modifications that direct the function of chromatin regulators, which recognize them through effector domains. Effector domain/histone interactions have been broadly studied, but largely using peptide fragments of histone tails. Here, we extend these studies into the nucleosome context and find that the conformation adopted by the histone H3 tails is inhibitory to BPTF PHD finger binding. Using NMR spectroscopy and MD simulations, we show that the H3 tails interact robustly but dynamically with nucleosomal DNA, substantially reducing PHD finger association. Altering the electrostatics of the H3 tail via modification or mutation increases accessibility to the PHD finger, indicating that PTM crosstalk can regulate effector domain binding by altering nucleosome conformation. Together, our results demonstrate that the nucleosome context has a dramatic impact on signaling events at the histone tails, and highlights the importance of studying histone binding in the context of the nucleosome.

Molecular recognition plays a central role in biochemical processes. Although well studied, understanding the mechanisms of recognition is inherently difficult due to the range of potential interactions, the molecular rearrangement associated with binding, and the time and length scales involved. Computational methods have the potential for not only complementing experiments that have been performed, but also in guiding future ones through their predictive abilities. In this review, we discuss how molecular dynamics (MD) simulations may be used in advancing our understanding of the thermodynamics that drive biomolecular recognition. We begin with a brief review of the statistical mechanics that form a basis for these methods. This is followed by a description of some of the most commonly used methods: thermodynamic pathways employing alchemical transformations and potential of mean force calculations, along with end-point calculations for free energy differences, and harmonic and quasi-harmonic analysis for entropic calculations. Finally, a few of the fundamental findings that have resulted from these methods are discussed, such as the role of configurational entropy and solvent in intermolecular interactions, along with selected results of the model system T4 lysozyme to illustrate potential and current limitations of these methods.

Gram-positive bacteria use sortase cysteine transpeptidase enzymes to covalently attach proteins to their cell wall and to assemble pili. In pathogenic bacteria sortases are potential drug targets, as many of the proteins that they display on the microbial surface play key roles in the infection process. Moreover, the Staphylococcus aureus Sortase A (SaSrtA) enzyme has been developed into a valuable biochemical reagent because of its ability to ligate biomolecules together in vitro via a covalent peptide bond. Here we review what is known about the structures and catalytic mechanism of sortase enzymes. Based on their primary sequences, most sortase homologs can be classified into six distinct subfamilies, called class A–F enzymes. Atomic structures reveal unique, class-specific variations that support alternate substrate specificities, while structures of sortase enzymes bound to sorting signal mimics shed light onto the molecular basis of substrate recognition. The results of computational studies are reviewed that provide insight into how key reaction intermediates are stabilized during catalysis, as well as the mechanism and dynamics of substrate recognition. Lastly, the reported in vitro activities of sortases are compared, revealing that the transpeptidation activity of SaSrtA is at least 20-fold faster than other sortases that have thus far been characterized. Together, the results of the structural, computational, and biochemical studies discussed in this review begin to reveal how sortases decorate the microbial surface with proteins and pili, and may facilitate ongoing efforts to discover therapeutically useful small molecule inhibitors.

Allosteric networks allow enzymes to transmit information and regulate their catalytic activities over vast distances. In principle, molecular dynamics (MD) simulations can be used to reveal the mechanisms that underlie this phenomenon; in practice, it can be difficult to discern allosteric signals from MD trajectories. Here, we describe how MD simulations can be analyzed to reveal correlated motions and allosteric networks, and provide an example of their use on the coagulation enzyme thrombin. Methods are discussed for calculating residue-pair correlations from atomic fluctuations and mutual information, which can be combined with contact information to identify allosteric networks and to dynamically cluster a system into highly correlated communities. In the case of thrombin, these methods show that binding of the antagonist hirugen significantly alters the enzyme’s correlation landscape through a series of pathways between Exosite I and the catalytic core. Results suggest that hirugen binding curtails dynamic diversity and enforces stricter venues of influence, thus reducing the accessibility of thrombin to other molecules.

Background: Sortase enzymes catalyze a transpeptidation reaction that displays bacterial surface proteins. Results: Structural and computational studies reveal how the sortase B enzyme recognizes its sorting signal substrate. Conclusion: Sortase enzymes catalyze transpeptidation using a substrate-stabilized oxyanion hole. Significance: The results of this work could facilitate the rational design of sortase inhibitors.