Catalytic promiscuity of a bacterial α-N-methyltransferase

The posttranslational methylation of N-terminal α-amino groups (α-N-methylation) is a ubiquitous reaction found in all domains of life. Although this modification usually occurs on protein substrates, recent studies have shown that it also takes place on ribosomally synthesized natural products. Here we report an investigation of the bacterial α-N-methyltransferase CypM involved in the biosynthesis of the peptide antibiotic cypemycin. We demonstrate that CypM has low substrate selectivity and methylates a variety of oligopeptides, cyclic peptides such as nisin and haloduracin, and the ε-amino group of lysine. Hence it may have potential for enzyme engineering and combinatorial biosynthesis. Bayesian phylogenetic inference of bacterial α-N-methyltransferases suggests that they have not evolved as a specific group based on the chemical transformations they catalyze, but that they have been acquired from various other methyltransferase classes during evolution.     

Using a reduced dimensionality model to compute the thermodynamic properties of finite polypeptide aggregates

By implementing a simple reduced dimensionality model to describe the interactions in finite systems composed of two seven-amino-acid peptides, the thermodynamic properties of ordered and disordered aggregates were computed. Within this model, the hydrophobicity of each amino acid was varied, and the stability of the systems computed. Accurate averages in the canonical ensemble were obtained using various replica exchange Monte Carlo algorithms. Low and high temperature regions were encountered where the ordered and disordered aggregates were stabilized. It was observed that as the degree of hydrophobicity increased, the stability of the aggregates increased, with a significant energetic stabilization obtained for the ordered aggregates. Upon decreasing the concentration of the solution, the stability of the amorphous aggregates increased when compared to the ordered systems.     

Lysogens and free viruses in fresh, brackish, and marine waters: a Bayesian analysis

A yearlong study was conducted to determine factors that affect the abundance and distribution of lysogens and free viruses at fresh-, brackish-, and saltwater stations in Newport Bay, CA. The viral and bacterial abundance were highest in the freshwater (average 1.1 × 10⁸ and 1.1 × 10⁷ mL⁻¹, respectively) and lowest in the marine water (average 0.4 × 10⁸ and 0.5 × 10⁷ mL⁻¹, respectively). Bacterial and viral counts were also several times higher during the summer than in winter. Approximately, 35% of the 141 samples were inducible in the presence of mitomycin C. The highest percentage of inducible lysogens was observed in marine waters (42%), while the lowest percentage was observed in the warmer freshwater (23%). A statistical model for the joint occurrence of lysogens and free viruses was formulated and estimated using Bayesian techniques to understand the key environmental determinants of viruses and lysogens. Our results support the existence of significant heterogeneity between the saltwater and freshwater sites. A parsimonious model that combines the two saltwater sites performs best among the specifications that were considered. Bacteria and water temperature were significant determinants of virus counts, whereas lysogen relationships are unclear. Importantly, conditional on the covariates, viruses and lysogen fractions exhibit robust negative correlation.     

All-Atom Monte Carlo Approach to Protein-Peptide Binding

We develop a procedure for exploring the free energy landscape of protein-peptide binding at atomic detail and apply it to PDZ domain-peptide interactions. The procedure involves soft constraints on receptor proteins providing limited chain flexibility, including backbone motions. Peptide chains are left fully flexible and kept in spatial proximity of the protein through periodic boundary conditions. By extensive Monte Carlo simulations, full representative conformational ensembles at temperatures where bound and unbound states coexist are obtained. To make this approach computationally feasible, we develop an effective all-atom energy function centering on hydrophobicity, hydrogen bonding, and electrostatic interactions. Our initial focus is a set of 11 PDZ domain-peptide pairs with experimentally determined complex structures. Minimum-energy conformations are found to be highly similar to the respective native structures in eight of the cases (all-atom peptide RMSDs < 6 Å). Having achieved that, we turn to a more complete characterization of the bound peptide state through a clustering scheme applied on the full ensembles of peptide structures. We find a significant diversity among bound peptide conformations for several PDZ domains, in particular involving the N terminal side of the peptide chains. Our computational model is then tested further on a set of nine PDZ domain-peptide pairs where the peptides are not originally present in the experimentally determined structures. We find a similar success rate in terms of the nativeness of minimum-energy conformations. Finally, we investigate the ability of our approach to capture variations in binding affinities for different peptide sequences. This is done in particular for a set of related sequences binding to the third PDZ domain of PSD-95 with encouraging results.     

Crystal Structure of Human Poly(A) Polymerase Gamma Reveals a Conserved Catalytic Core for Canonical Poly(A) Polymerases

In eukaryotes, the poly(A) tail added at the 3′ end of an mRNA precursor is essential for the regulation of mRNA stability and the initiation of translation. Poly(A) polymerase (PAP) is the enzyme that catalyzes the poly(A) addition reaction. Multiple isoforms of PAP have been identified in vertebrates, which originate from gene duplication, alternative splicing or post-translational modifications. The complexity of PAP isoforms suggests that they might play different roles in the cell. Phylogenetic studies indicate that vertebrate PAPs are grouped into three clades termed α, β and γ, which originated from two gene duplication events. To date, all the available PAP structures are from the PAPα clade. Here, we present the crystal structure of the first representative of the PAPγ clade, human PAPγ bound to cordycepin triphosphate (3′dATP) and Ca^2 +. The structure revealed that PAPγ closely resembles its PAPα ortholog. An analysis of residue conservation reveals a conserved catalytic binding pocket, whereas residues at the surface of the polymerase are more divergent.     

Dynamic Monte Carlo simulation of non-equilibrium Brownian diffusion of single-chain macromolecules

We performed dynamic Monte Carlo simulations of biased diffusion of 3D phantom single lattice polymer. We observed spontaneous deformation of polymer coil when the external driving forces exceed a critical strength. In addition, longer chains require lower critical strengths, at which their activated velocities deviate from Newtonian-fluid behaviours and merge into a master curve exhibiting shear-thinning followed with shear thickening. We attributed the cause of deformation to the random updating of monomers. The latter represents the dynamic heterogeneity along the real polymer chain, and raises a nonlinear asymmetric accumulation of local acceleration and then an internal tension between chain middle and chain end, as evidenced by our previous Brownian Dynamics simulations. Our results unravel a single-molecular-level source of nonlinear dynamics, which has been overlooked in current theoretical considerations on the basis of Rouse ideal-chain model.     

Log‐gamma linear‐mixed effects models for multiple outcomes with application to a longitudinal glaucoma study

Glaucoma is a progressive disease due to damage in the optic nerve with associated functional losses. Although the relationship between structural and functional progression in glaucoma is well established, there is disagreement on how this association evolves over time. In addressing this issue, we propose a new class of non‐Gaussian linear‐mixed models to estimate the correlations among subject‐specific effects in multivariate longitudinal studies with a skewed distribution of random effects, to be used in a study of glaucoma. This class provides an efficient estimation of subject‐specific effects by modeling the skewed random effects through the log‐gamma distribution. It also provides more reliable estimates of the correlations between the random effects. To validate the log‐gamma assumption against the usual normality assumption of the random effects, we propose a lack‐of‐fit test using the profile likelihood function of the shape parameter. We apply this method to data from a prospective observation study, the Diagnostic Innovations in Glaucoma Study, to present a statistically significant association between structural and functional change rates that leads to a better understanding of the progression of glaucoma over time.