How does tmRNA move through the ribosome?

To test the structure of tmRNA in solution, cross-linking experiments were performed which showed two sets of cross-links in two different domains of tmRNA. Site-directed mutagenesis was used to search for tmRNA nucleotide bases that might form a functional analogue of a codon-anticodon duplex to be recognized by the ribosomal A-site. We demonstrate that nucleotide residues U85 and A86 from tmRNA are significant for tmRNA function and propose that they are involved in formation of a tmRNA element playing a central role in A-site recognition. These data are discussed in the frame of a hypothetical model that suggests a general scheme for the interaction of tmRNA with the ribosome and explains how it moves through the ribosome.     

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.     

What contributions to protein side-chain dynamics are probed by NMR experiments? A molecular dynamics simulation analysis

Molecular dynamics simulations of the structurally homologous proteins TNfn3 and FNfn10 have been used to investigate the contributions to side-chain dynamics measured by NMR relaxation experiments. The results reproduce the variation in core side-chain dynamics observed by NMR and highlight the relevance of anharmonic motion and transitions between local minima for explaining NMR side-chain order parameters. A method is described for calculating converged order parameters by use of replica exchange molecular dynamics in conjunction with an implicit solvent model. These simulations allow the influence of various factors, such as the flexibility of side-chains and their free volume, on the mobility to be tested by perturbing the system. Deletion mutations are found to have the largest effect on the more densely packed FNfn10. Some counterintuitive effects are seen, such as an increase in order parameters close to deletion mutation sites, but these can be rationalized in terms of direct interactions with the modified side-chains. A statistical analysis of published order parameters supports the conclusions drawn from the simulations.     

How long does it take to equilibrate the unfolded state of a protein?

How long does it take to equilibrate the unfolded state of a protein? The answer to this question has important implications for our understanding of why many small proteins fold with two state kinetics. When the equilibration within the unfolded state U is much faster than the folding, the folding kinetics will be two state even if there are many folding pathways with different barriers. Yet the mean first passage times (MFPTs) between different regions of the unfolded state can be much longer than the folding time. This seems to imply that the equilibration within U is much slower than the folding. In this communication we resolve this paradox. We present a formula for estimating the time to equilibrate the unfolded state of a protein. We also present a formula for the MFPT to any state within U, which is proportional to the average lifetime of that state divided by the state population. This relation is valid when the equilibration within U is very fast as compared with folding as it often is for small proteins. To illustrate the concepts, we apply the formulas to estimate the time to equilibrate the unfolded state of Trp-cage and MFPTs within the unfolded state based on a Markov State Model using an ultra-long 208 microsecond trajectory of the miniprotein to parameterize the model. The time to equilibrate the unfolded state of Trp-cage is ∼100 ns while the typical MFPTs within U are tens of microseconds or longer.     

How and why does the areole meristem move in Echinocereus (Cactaceae)?

Background and Aims In Cactaceae, the areole is the organ that forms the leaves, spines and buds. Apparently, the genus Echinocereus develops enclosed buds that break through the epidermis of the stem adjacent to the areole; this trait most likely represents a synapomorphy of Echinocereus. The development of the areole is investigated here in order to understand the anatomical modifications that lead to internal bud development and to supplement anatomical knowledge of plants that do not behave according to classical shoot theory.Methods The external morphology of the areole was documented and the anatomy was studied using tissue clearing, scanning electron microscopy and light microscopy for 50 species that represent the recognized clades and sections of the traditional classification of the genus, including Morangaya pensilis (Echinocereus pensilis).Key Results In Echinocereus, the areole is sealed by the periderm, and the areole meristem is moved and enclosed by the differential growth of the epidermis and surrounding cortex. The enclosed areole meristem is differentiated in a vegetative or floral bud, which develops internally and breaks through the epidermis of the stem. In Morangaya pensilis, the areole is not sealed by the periderm and the areole meristem is not enclosed.Conclusions The enclosed areole meristem and internal bud development are understood to be an adaptation to protect the meristem and the bud from low temperatures. The anatomical evidence supports the hypothesis that the enclosed bud represents one synapomorphy for Echinocereus and also supports the exclusion of Morangaya from Echinocereus.     

How does eutrophication affect the role of grazers in harmful algal bloom dynamics?

Population dynamics of harmful algal bloom species are regulated both from the “bottom-up” by factors that affect their growth rate and from the “top-down” by factors that affect their loss rates. While it might seem apparent that eutrophication would have the greatest impact on factors affecting growth rates of phytoplankton (nutrient supply, light availability) the roles of top-down controls, including grazers and pathogens, cannot be ignored in studies of harmful bloom dynamics. Lags between the growth of phytoplankton and zooplankton populations, or disruption of zooplankton populations by adverse environmental conditions may be important factors in the initiation of plankton blooms under eutrophic conditions. Grazers that avoid feeding on harmful species and actively graze on competing species may also play important roles in bloom initiation. Grazers that are not affected by phytoplankton toxins and have growth rates comparable to phytoplankton (e.g. protozoan grazers) may have the potential to control the initiation of blooms. If the inhibition of grazers varies with cell density for blooms of toxic phytoplankton, eutrophication may increase the chances of blooms reaching threshold densities for grazer inhibition. In addition, secondary effects of eutrophication, including hypoxia and change in pH may adversely affect grazer populations, and further release HAB species from top-down control. The Texas brown tide (Aureoumbra lagunensis) blooms provide evidence for the role of grazer disruption in bloom initiation and the importance of high densities of brown tide cells in continued suppression of grazers.     

Interaction models of substrate peptides and β-secretase studied by NMR spectroscopy and molecular dynamics simulation

The formation of β-amyloid peptide (Aβ) is initiated from cleavage of amyloid precursor protein (APP) by a family of protease, α-, β-, and γ-secretase. Sub W, a substrate peptide, consists of 10 amino acids, which are adjacent to the β-cleavage site of wild-type APP, and Sub M is Swedish mutant with double mutations on the left side of the β-cleavage site of APP. Sub W is a normal product of the metabolism of APP in the secretary pathway. Sub M is known to increase the efficiency of β-secretase activity, resulting in a more specific binding model compared to Sub W. Three-dimensional structures of Sub W and Sub M were studied by CD and NMR spectroscopy in water solution. On the basis of these structures, interaction models of β-secretase and substrate peptides were determined by molecular dynamics simulation. Four hydrogen bonds and one water-mediated interaction were formed in the docking models. In particular, the hydrogen bonding network of Sub M-BACE formed spread over the broad region of the active site of β-secretase (P5-P3′), and the side chain of P2-Asn formed a hydrogen bond specifically with the side chain of Arg235. These are more favorable to the cleavage of Sub M by β-secretase than Sub W. The two substrate peptides showed different tendency to bind to β-secretase and this information may useful for drug development to treat and prevent Alzheimer’s disease.     

How long does DNA keep the memory of its conformation? A time-dependent canonical correlation analysis of molecular dynamics simulation

The time dependence of the correlation between motions of different parts of DNA is analyzed from a 200 ps molecular dynamics simulation of the double-stranded self-complementary d(CTGATCAG) in the B form. Each nucleotide is decomposed into three subunits corresponding to the furanose ring (SU), the base (BA), and the backbone (SK). The motion of each subunit is considered as the superimposition of rigid body translation, rigid body rotation, and internal deformation. Canonical time-dependent correlation functions calculated with coordinates describing the different components of the subunits motion are defined and computed. This allows us to probe how long a particular type of motion of one subunit influences the other types of motions of other subunits (cross correlation functions) or how long a particular subunit keeps the memory of its own conformation or location (autocorrelation functions). From auto-correlation analysis it is found that deformation decorrelates within a few tenths of picoseconds, rotational correlation times are on the order of 8 ps, while translational motions are long-time correlated. The deformation of a subunit is not correlated to the deformation of another one (at the 200 ps time scale of our simulation), but influences slightly their translation and orientation as time increases. © 1996 John Wiley & Sons, Inc.     

Drug resistance in Leishmania: does it really matter?

Treatment failure (TF) jeopardizes the management of parasitic diseases, including leishmaniasis. From the parasite’s point of view, drug resistance (DR) is generally considered as central to TF. However, the link between TF and DR, as measured by in vitro drug susceptibility assays, is unclear, some studies revealing an association between treatment outcome and drug susceptibility, others not. Here we address three fundamental questions aiming to shed light on these ambiguities. First, are the right assays being used to measure DR? Second, are the parasites studied, which are generally those that adapt to in vitro culture, actually appropriate? Finally, are other parasite factors – such as the development of quiescent forms that are recalcitrant to drugs – responsible for TF without DR?     

How does tautomerization affect the excited-state dynamics of an amino acid-derivatized corrole?

Photosynthesis Research - In the first two decades of the XXI century, corroles have emerged as an important class of porphyrinoids for photonics and biomedical photonics. In comparison with...     

Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation

In the light of results obtained during the last two decades in a number of laboratories, it appears that some of the tools of nonlinear dynamics, first developed and improved for the physical sciences and engineering, are well-suited for studies of biological phenomena. In particular it has become clear that the different regimes of activities undergone by nerve cells, neural assemblies and behavioural patterns, the linkage between them, and their modifications over time, cannot be fully understood in the context of even integrative physiology, without using these new techniques. This report, which is the first of two related papers, is aimed at introducing the non expert to the fundamental aspects of nonlinear dynamics, the most spectacular aspect of which is chaos theory. After a general history and definition of chaos the principles of analysis of time series in phase space and the general properties of chaotic trajectories will be described as will be the classical measures which allow a process to be classified as chaotic in ideal systems and models. We will then proceed to show how these methods need to be adapted for handling experimental time series; the dangers and pitfalls faced when dealing with non stationary and often noisy data will be stressed, and specific criteria for suspecting determinism in neuronal cells and/or assemblies will be described. We will finally address two fundamental questions, namely i) whether and how can one distinguish, deterministic patterns from stochastic ones, and, ii) what is the advantage of chaos over randomness: we will explain why and how the former can be controlled whereas, notoriously, the latter cannot be tamed. In the second paper of the series, results obtained at the level of single cells and their membrane conductances in real neuronal networks and in the study of higher brain functions, will be critically reviewed. It will be shown that the tools of nonlinear dynamics can be irreplaceable for revealing hidden mechanisms subserving, for example, neuronal synchronization and periodic oscillations. The benefits for the brain of adopting chaotic regimes with their wide range of potential behaviours and their aptitude to quickly react to changing conditions will also be considered.     

How does GAP catalyze the GTPase reaction of Ras? A computer simulation study

The formation of a complex between p21(ras) and GAP accelerates the GTPase reaction of p21(ras) and terminates the signal for cell proliferation. The understanding of this rate acceleration is important for the elucidation of the role of Ras mutants in tumor formation. In principle there are two main options for the origin of the effect of GAP. One is a direct electrostatic interaction between the residues of GAP and the transition state of the Ras-GAP complex and the other is a GAP-induced shift of the structure of Ras to a configuration that increases the stabilization of the transition state. This work examines the relative importance of these options by computer simulations of the catalytic effect of Ras. The simulations use the empirical valence bond (EVB) method to study the GTPase reaction along the alternative associative and dissociative paths. This approach reproduces the trend in the overall experimentally observed catalytic effect of GAP: the calculated effect is 7 +/- 3 kcal/mol as compared to the observed effect of approximately 6.6 kcal/mol. Furthermore, the calculated effect of mutating Arg789 to a nonpolar residue is 3-4 kcal/mol as compared to the observed effect of 4.5 kcal/mol for the Arg789Ala mutation. It is concluded, in agreement with previous proposals, that the effect of Arg789 is associated with its direct interaction with the transition state charge distribution. However, calculations that use the coordinates of Ras from the Ras-GAP complex (referred to here as Ras') reproduce a significant catalytic effect relative to the Ras coordinates. This indicates that part of the effect of GAP involves a stabilization of a catalytic configuration of Ras. This configuration increases the positive electrostatic potential on the beta-phosphate (relative to the corresponding situation in the free Ras). In other words, GAP stabilizes the GDP bound configuration of Ras relative to that of the GTP-bound conformation. The elusive oncogenic effect of mutating Gln61 is also explored. The calculated effect of such mutations in the Ras-GAP complex are found to be small, while the observed effect is very large (8.7 kcal/mol). Since the Ras is locked in its Ras-GAP configuration in our simulations, we conclude that the oncogenic effect of mutation of Gln61 is indirect and is associated most probably with the structural changes of Ras upon forming the Ras-GAP complex. In view of these and the results for the Ras' we conclude that GAP activates Ras by both direct electrostatic stabilization of the transition state and an indirect allosteric effect that stabilizes the GDP-bound form. The present study also explored the feasibility of the associative and dissociative mechanism in the GTPase reaction of Ras. It is concluded that the reaction is most likely to involve an associative mechanism.     

Immunostimulation in crustaceans: does it really protect against infection?

There is a growing need to control, prevent or minimise the devastating effects of disease in crustacean culture without recourse to toxic chemicals or antibiotics. In keeping with approaches to disease control in fish and higher mammals, interest is developing in compounds that confer protection and/or enhance immune reactivity to likely pathogens in shellfish (sometimes, erroneously, referred to as as "shellfish vaccines"). The agents currently under scrutiny for crustaceans include glucans, lipopolysaccharides and killed bacterial cells. They are thought to act as "immuno-stimulants" because of their known effects on the crustacean immune system in vitro. A number of papers are now appearing in the literature claiming to demonstrate their positive impact on immunity and immunity and disease resistance. This review article considers the problem of disease and its control in crustacean farming, describing the types of capability in cultured species. Analysis of the validity of the results of many of the published studies raises questions about the value of these compounds for cost-effective control of infection in aquaculture, especially for long lasting protection in both adults and juveniles. This review further discusses the potential risks to the wellbeing of the stock animals from repeated use of these agents and makes the case for rigorous testing of putative stimulants, at the gene, protein and functional levels, as well as for the need to consider alternative strategies and approaches to disease control.     

How does temporal variation in habitat connectivity influence metapopulation dynamics?

Metapopulation persistence depends on connectivity between habitat patches. While emphasis has been placed on the spatial dynamics of connectivity, much less has been placed on its short‐term temporal dynamics. In many terrestrial and aquatic ecosystems, however, transient (short‐term) changes in connectivity occur as habitat patches are connected and disconnected due, for example, to climatic or hydrological variability. We evaluated the implications of transient connectivity using a network‐based metapopulation model and a series of scenarios representing temporal changes in connectivity. The transient loss of connectivity can influence metapopulation persistence, and more strongly autocorrelated temporal dynamics affect metapopulation persistence more severely. Given that many ecosystems experience short‐term and temporary loss of habitat connectivity, it is important that these dynamics are adequately represented in metapopulation models; failing to do so may yield overly optimistic‐estimates of metapopulation persistence in fragmented landscapes.     

How does the resistance threshold in spatially explicit epidemic dynamics depend on the basic reproductive ratio and spatial correlation of crop genotypes?

We examined the fraction of resistant cultivars necessary to prevent a global pathogen outbreak (the resistance threshold) using a spatially explicit epidemiological model (SIR model) in a finite, two-dimensional, lattice-structured host population. Infectious diseases in our model could be transmitted to susceptible nearest-neighbour sites, and the infected site either recovered or died after an exponentially distributed infectious period. Threshold behaviour of this spatially explicit SIR model cannot be reduced to that of bond percolation, as was previously noted in the literature, unless extreme assumptions (synchronized infection events with a fixed lag) are imposed on infection process. The resistance threshold is significantly lower than that of conventional mean-field epidemic models, and is even lower if the spatial configuration of resistant and susceptible crops are negatively correlated. Finite size scaling applied to the resistance threshold for a finite basic reproductive ratio ρ of pathogen reveals that its difference from static percolation threshold (0.41) is inversely proportional to ρ. Our formula for the basic reproductive ratio dependency of the resistance threshold produced an estimate for the critical basic reproductive ratio (4.7) in a universally susceptible population, which is much larger than the corresponding critical value (1) in the mean-field model and nearly three times larger than the critical growth rate of a basic contact process (SIS model). Pair approximation reveals that the resistance threshold for preventing a global epidemic is factor 1/(1−η) greater with spatially correlated planting than with random planting, where η is initial correlation in host genotypes between nearest-neighbour sites. Thus the eradication is harder with a positive spatial correlation (η>0) in mixed susceptible/resistant plantings, and is easier with a negative correlation (η<0). The effect of finite field size (L), which corresponded to the mean distance between sources of infections, is given by the increased resistance threshold (by the amount L^−0.75) from its infinite size limit. Implications of these results on effective planting strategies in multi-line control plans are discussed.