Fishing new proteins in the twilight zone of genomes: the test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram-negative bacteria

We address the problem of clustering the whole protein content of genomes into three different categories-globular, all-alpha, and all-beta membrane proteins-with the aim of fishing new membrane proteins in the pool of nonannotated proteins (twilight zone). The focus is then mainly on outer membrane proteins. This is performed by using an integrated suite of programs (Hunter) specifically developed for predicting the occurrence of signal peptides in proteins of Gram-negative bacteria and the topography of all-alpha and all-beta membrane proteins. Hunter is tested on the well and partially annotated proteins (2160 and 760, respectively) of Escherichia coli K 12 scoring as high as 95.6% in the correct assignment of each chain to the category. Of the remaining 1253 nonannotated sequences, 1099 are predicted globular, 136 are all-alpha, and 18 are all-beta membrane proteins. In Escherichia coli 0157:H7 we filtered 1901 nonannotated proteins. Our analysis classifies 1564 globular chains, 327 inner membrane proteins, and 10 outer membrane proteins. With Hunter, new membrane proteins are added to the list of putative membrane proteins of Gram-negative bacteria. The content of outer membrane proteins per genome (nine are analyzed) ranges from 1.5% to 2.4%, and it is one order of magnitude lower than that of inner membrane proteins. The finding is particularly relevant when it is considered that this is the first large-scale analysis based on validated tools that can predict the content of outer membrane proteins in a genome and can allow cross-comparison of the same protein type between different species.     

Sequence alignments and pair hidden Markov models using evolutionary history

This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.     

Heat capacities and a snapshot of the energy landscape in protein GB1 from the pre-denaturation temperature dependence of backbone NH nanosecond fluctuations

Protein stability is usually characterized calorimetrically by a melting temperature and related thermodynamic parameters. Despite its importance, the microscopic origin of the melting transition and the relationship between thermodynamic stability and dynamics remains a mystery. Here, NMR relaxation parameters were acquired for backbone 15NH groups of the 56 residue immunoglobulin-binding domain of streptococcal protein G over a pre-denaturation temperature range of 5-50 degrees C. Relaxation data were analyzed using three methods: the standard three-Lorentzian model free approach; the F(omega)=2omegaJ(omega) spectral density approach that yields motional correlation time distributions, and a new approach that determines frequency-dependent order parameters. Regardless of the method of analysis, the temperature dependence of internal motional correlation times and order parameters is essentially the same. Nanosecond time-scale internal motions are found for all NHs in the protein, and their temperature dependence yields activation energies ranging up to about 33kJ/mol residue. NH motional barrier heights are structurally correlated, with the largest energy barriers being found for residues in the most "rigid" segments of the fold: beta-strands 1 and 4 and the alpha-helix. Trends in this landscape also parallel the free energy of folding-unfolding derived from hydrogen-deuterium (H-D) exchange measurements, indicating that the energetics for internal motions occurring on the nanosecond time-scale mirror those occurring on the much slower time-scale of H-D exchange. Residual heat capacities, derived from the temperature dependence of order parameters, range from near zero to near 100J/mol K residue and correlate with this energy landscape. These results provide a unique picture of this protein's energy landscape and a relationship between thermodynamic stability and dynamics that suggests thermosensitive regions in the fold that could initiate the melting process.     

Capillaries within compartments: microvascular interpretation of dynamic positron emission tomography data

Measurement of exchange of substances between blood and tissue has been a long-lasting challenge to physiologists, and considerable theoretical and experimental accomplishments were achieved before the development of the positron emission tomography (PET). Today, when modeling data from modern PET scanners, little use is made of earlier microvascular research in the compartmental models, which have become the standard model by which the vast majority of dynamic PET data are analysed. However, modern PET scanners provide data with a sufficient temporal resolution and good counting statistics to allow estimation of parameters in models with more physiological realism. We explore the standard compartmental model and find that incorporation of blood flow leads to paradoxes, such as kinetic rate constants being time-dependent, and tracers being cleared from a capillary faster than they can be supplied by blood flow. The inability of the standard model to incorporate blood flow consequently raises a need for models that include more physiology, and we develop microvascular models which remove the inconsistencies. The microvascular models can be regarded as a revision of the input function. Whereas the standard model uses the organ inlet concentration as the concentration throughout the vascular compartment, we consider models that make use of spatial averaging of the concentrations in the capillary volume, which is what the PET scanner actually registers. The microvascular models are developed for both single- and multi-capillary systems and include effects of non-exchanging vessels. They are suitable for analysing dynamic PET data from any capillary bed using either intravascular or diffusible tracers, in terms of physiological parameters which include regional blood flow.     

Random variation and concentration effects in PCR

Even though the efficiency of the polymerase chain reaction (PCR) reaction decreases, analyses are made in terms of Galton-Watson processes, or simple deterministic models with constant replication probability (efficiency). Recently, Schnell and Mendoza have suggested that the form of the efficiency, can be derived from enzyme kinetics. This results in the sequence of molecules numbers forming a stochastic process with the properties of a branching process with population size dependence, which is supercritical, but has a mean reproduction number that approaches one. Such processes display ultimate linear growth, after an initial exponential phase, as is the case in PCR. It is also shown that the resulting stochastic process for a large Michaelis-Menten constant behaves like the deterministic sequence x(n) arising by iterations of the function f(x)=x+x/(1+x).     

A numeric study of the noise-induced tremor in a mathematical model of the stretch reflex

A mathematical model of the stretch reflex for the cat soleus muscle is presented. The time-delay differential equations of the model are solved using the fourth-order Runge-Kutta algorithm, introducing a Gaussian-noise term to simulate the environmental noise. The muscle response dynamics are then studied under various levels of average muscle activation. Finally, the feasibility of explaining the so-called physiological tremor from the properties of the stretch reflex mechanisms is discussed by comparing our results with reported experimental evidence.     

A dual-functional paramyxovirus F protein regulatory switch segment: activation and membrane fusion

Many viral fusion-mediating glycoproteins couple alpha-helical bundle formation to membrane merger, but have different methods for fusion activation. To study paramyxovirus-mediated fusion, we mutated the SV5 fusion (F) protein at conserved residues L447 and I449, which are adjacent to heptad repeat (HR) B and bind to a prominent cavity in the HRA trimeric coiled coil in the fusogenic six-helix bundle (6HB) structure. These analyses on residues L447 and I449, both in intact F protein and in 6HB, suggest a metamorphic region around these residues with dual structural roles. Mutation of L447 and I449 to aliphatic residues destabilizes the 6HB structure and attenuates fusion activity. Mutation of L447 and I449 to aromatic residues also destabilizes the 6HB structure despite promoting hyperactive fusion, indicating that 6HB stability alone does not dictate fusogenicity. Thus, residues L447 and I449 adjacent to HRB in paramyxovirus F have distinct roles in fusion activation and 6HB formation, suggesting this region is involved in a conformational switch.     

Structural identifiability for a class of non-linear compartmental systems using linear/non-linear splitting and symbolic computation

Under certain controllability and observability restrictions, two different parameterisations for a non-linear compartmental model can only have the same input-output behaviour if they differ by a locally diffeomorphic change of basis for the state space. With further restrictions, it is possible to gain valuable information with respect to identifiability via a linear analysis. Examples are presented where non-linear identifiability analyses are substantially simplified by means of an initial linear analysis. For complex models, with four or more compartments, this linear analysis can prove lengthy to perform by hand and so symbolic computation has been employed to aid this procedure.     

On Parametric Stability of Gene Networks Controlling Ontogenetic Processes

The problem of evaluation of parametric stability of three models of pro- and eukaryotic gene networks controlling ontogenetic processes has been defined and solved. Experimental schemes of testing gene networks for parametric stability based on the method of generalized threshold models were developed and realized as a software application. We studied the sensitivity of the functioning modes to random variations of the parameters in three model systems: phage development control system, Arabidopsis thaliana flower morphogenesis control subsystem, and gene subnetwork controlling early ontogeny of Drosophila melanogaster. The parametric stability was quantitatively assessed for these models.