A high quality nuclear magnetic resonance solution structure of peptide deformylase from Escherichia coli: Application of an automated assignment strategy using GARANT

The NMR structure of the peptide deformylase (PDF) (1–150) from Escherichia coli, which is an essential enzyme that removes the formyl group from nascent polypeptides and represents a potential target for drug discovery, was determined using 15N/13C doubly labeled protein. Nearly completely automated assignment routines were employed to assign three-dimensional triple resonance, 15N-resolved and 13C-resolved NOESY spectra using the program GARANT. This assignment strategy, demonstrated on a 17 kDa protein, is a significant advance in the automation of NMR data assignment and structure determination that will accelerate future work. A total of 2302 conformational constraints were collected as input for the distance geometry program DYANA. After restrained energy minimization with the program X-PLOR the 20 best conformers characterize a high quality structure with an average of 0.43 Å for the root-mean-square deviation calculated from the backbone atoms N, C and C, and 0.81 Å for all heavy atoms of the individual conformers relative to the mean coordinates for residues 1 to 150. The globular fold of PDF contains two -helices comprising residues 25–40, 125–138, six -strands 57–60, 70–77, 85–88, 98–101, 105–111, 117–123 and one 310 helix comprising residues 49–51. The C-terminal helix contains the HEXXH motif positioning a zinc ligand in a similar fashion to other metalloproteases, with the third ligand being cysteine and the fourth presumably a water. The three-dimensional structure of PDF affords insight into the substrate recognition and specificity for N-formylated over N-acetylated substrates and is compared to other PDF structures.     

The influence of geometry on hydrodynamic and mass transfer characteristics in an external airlift reactor for the cultivation of filamentous fungi

The influences of geometric configuration, mycelial broth rheology and superficial gas velocity (U_sg) were investigated with respect to the following hydrodynamic parameters: gas holdup (), oxygen transfer coefficient (K_La) and mixing time (t_m). Increases in U_sg and height of gas separator (H_t) caused an increase in and K_La, and a decrease in t_m. Consequently, a diameter ratio (D_d/D_r) of 0.71 and H_t 0.20 m were found to be the best geometry and operation parameters to achieve high aeration and mixing efficiency for the high viscous broth system in the cultivation of filamentous fungi. An external airlift reactor (EALR) was developed and designed for the cultivation of filamentous fungi. The EALR with two spargers excels in reliability and high aeration and mass transfer coefficiency, resulting in a fast mycelial growth and high biomass productivity in the cultivation of the fungus Rhizopus oryzae.     

Bayesian survival analysis using a MARS model

A Bayesian multivariate adaptive regression spline fitting approach is used to model univariate and multivariate survival data with censoring. The possible models contain the proportional hazards model as a subclass and automatically detect departures from this. A reversible jump Markov chain Monte Carlo algorithm is described to obtain the estimate of the hazard function as well as the survival curve.     

Stochastic models for cell motion and taxis

Certain biological experiments investigating cell motion result in time lapse video microscopy data which may be modeled using stochastic differential equations. These models suggest statistics for quantifying experimental results and testing relevant hypotheses, and carry implications for the qualitative behavior of cells and for underlying biophysical mechanisms. Directional cell motion in response to a stimulus, termed taxis, has previously been modeled at a phenomenological level using the Keller-Segel diffusion equation. The Keller-Segel model cannot distinguish certain modes of taxis, and this motivates the introduction of a richer class of models which is nevertheless still amenable to statistical analysis. A state space model formulation is used to link models proposed for cell velocity to observed data. Sequential Monte Carlo methods enable parameter estimation via maximum likelihood for a range of applicable models. One particular experimental situation, involving the effect of an electric field on cell behavior, is considered in detail. In this case, an Ornstein- Uhlenbeck model for cell velocity is found to compare favorably with a nonlinear diffusion model.     

Nitrosylation of rabbit ferrous heme-hemopexin

Hemopexin (HPX) serves as a trap for toxic plasma heme, ensuring its complete clearance by transportation to the liver. Moreover, HPX-heme has been postulated to play a key role in the homeostasis of nitric oxide (NO). Here, the thermodynamics for NO binding to rabbit ferrous HPX-heme as well as the EPR and optical absorption spectroscopic properties of rabbit ferrous nitrosylated HPX-heme (HPX-heme-NO) are reported. The value of the dissociation equilibrium constant for NO binding to rabbit ferrous HPX-heme (i.e., H) is (1.4±0.2)×10^–7 M, at pH 7.0 and 10.0 °C; the value of H is unaffected by sodium chloride. At pH 7.0, rabbit ferrous HPX-heme-NO is a six-coordinate heme-iron species, characterized by an X-band EPR spectrum with an axial geometry and by =146 mM^–1 cm^–1 at 419 nm. At pH 4.0, rabbit ferrous HPX-heme-NO is a five-coordinate heme-iron species, characterized by an X-band EPR spectrum with three-line splitting centered at 334 mT and by =74 mM^–1 cm^–1 at 387 nm. The pK_a value of the reversible pH-induced six- to five-coordinate spectroscopic transition is 4.8±0.1 in the absence of sodium chloride and 4.3±0.1 in the presence of 1.5×10^–1 M sodium chloride. This result is in agreement with the effect of sodium chloride on rabbit HPX-heme stability. The present data have been analyzed in parallel with those of a related heme model compound and heme-protein systems.     

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.     

Statistical inference and model selection for the 1861 Hagelloch measles epidemic

A stochastic epidemic model is proposed which incorporates heterogeneity in the spread of a disease through a population. In particular, three factors are considered: the spatial location of an individual's home and the household and school class to which the individual belongs. The model is applied to an extremely informative measles data set and the model is compared with nested models, which incorporate some, but not all, of the aforementioned factors. A reversible jump Markov chain Monte Carlo algorithm is then introduced which assists in selecting the most appropriate model to fit the data.     

Exploring the nonlinear geometry of protein homology

The explosion of biological data resulting from genomic and proteomic research has created a pressing need for data analysis techniques that work effectively on a large scale. An area of particular interest is the organization and visualization of large families of protein sequences. An increasingly popular approach is to embed the sequences into a low-dimensional Euclidean space in a way that preserves some predefined measure of sequence similarity. This method has been shown to produce maps that exhibit global order and continuity and reveal important evolutionary, structural, and functional relationships between the embedded proteins. However, protein sequences are related by evolutionary pathways that exhibit highly nonlinear geometry, which is invisible to classical embedding procedures such as multidimensional scaling (MDS) and nonlinear mapping (NLM). Here, we describe the use of stochastic proximity embedding (SPE) for producing Euclidean maps that preserve the intrinsic dimensionality and metric structure of the data. SPE extends previous approaches in two important ways: (1) It preserves only local relationships between closely related sequences, thus allowing the map to unfold and reveal its intrinsic dimension, and (2) it scales linearly with the number of sequences and therefore can be applied to very large protein families. The merits of the algorithm are illustrated using examples from the protein kinase and nuclear hormone receptor superfamilies.     

Power-law versus exponential distributions of animal group sizes

There has been some confusion concerning the animal group size: an exponential distribution was deduced by maximizing the entropy; lognormal distributions were practically used; as power-law decay with exponent 3/2 was proposed in physical analogy to aerosol condensation. Here I show that the animal group-size distribution follows a power-law decay with exponent 1, and is truncated at a cut-off size which is the expected size of the groups an arbitrary individual engages in. An elementary model of animal aggregation based on binary splitting and coalescing on contingent encounter is presented. The model predicted size distribution holds for various data from pelagic fishes and mammalian herbivores in the wild.