Binocular depth perception mechanisms in tongue-projecting salamanders

Tongue-projecting salamanders (Bolitoglossini) combine extreme speed and high precision in prey capture. They possess all requirements for stereoscopic depth perception: frontally oriented eyes, a substantial amount of direct ipsilateral projection in addition to the contralateral one, and binocularly driven neurons. Extracellular recordings were made from retinal afferents in the tectum as well as from the somata of tectal neurons. RF-sizes of afferents and tectal neurons were determined, and the response properties of tectal neurons were tested under monocular and binocular conditions with stimuli of different size and velocity. While RF-sizes and response properties of binocular neurons during binocular and contralateral stimulation were similar, ipsilaterally stimulated neurons exhibited much smaller RFs, lower spike rates and different size preferences.Furthermore, the contralateral retinotectal projection from one eye and the ipsilateral from the other are in register. While retinal afferents are distributed linearly over the tectal surface, most tectal neurons are activated by a retinal area corresponding to the frontal visual field; this results in a magnification of this region. The two monocular receptive fields of binocular neurons exhibit zero disparities (horopter) at distances that coincide with the maximum reach of the tongue. We hypothesize that bolitoglossine salamanders (as well as amphibians in general) make use of two kinds of disparities: (1) between the maps in the left and right tectal hemisphere, coding for the lateral eccentricity of an object, and (2) between the ipsilateral and contralateral retinotectal map, coding for the distance. The presence of substantial direct ipsilateral afferents in bolitoglossine salamanders appears to be the basis for a fast computation of object distance, which is characteristic of these animals.Abbreviations Ax/Ay coordinates of a recorded afference - Nx/Ny coordinates of a recorded neuron - RF receptive field - RFc contralateral receptive field - RFi ipsilateral receptive field - RFx/RFy coordinates of a receptive field center - RGC retinal ganglion cell     

Innate colour preferences of flower visitors

Freshly emerged flower visitors exhibit colour preferences prior to individual experience with flowers. The understanding of innate colour preferences in flower visitors requires a detailed analysis, as, on the one hand, colour is a multiple-signal stimulus, and, on the other hand, flower visits include a sequence of behavioural reactions each of which can be driven by a preferential behaviour. Behavioural reactions, such as the distant approach, the close-range orientation, the landing, and the extension of mouthparts can be triggered by colour stimuli. The physiological limitations of spectral sensitivity, the neuro-sensory filters, and the animals' different abilities to make use of visual information such as brightness perception, wavelength-specific behaviour and colour vision shape colour preferences. Besides these receiverbased factors, there are restrictions of flower colouration due to sender-based factors such as the absorption properties of floral pigments and the dual function of flower colours triggering both innate and learned behaviour. Recordings of the spectral reflection of coloured objects, which trigger innate colour preferences, provide an objective measure of the colour stimuli. Weighting the spectral reflection of coloured objects by the spectral composition of the ambient light and the spectral sensitivity of the flower visitors' photoreceptors allows the calculation of the effective stimuli. Perceptual dimensions are known for only a few taxa of flower visitors.     

Homology modeling of histidine-containing phosphocarrier protein and eosinophil-derived neurotoxin: Construction of models and comparison with experiment

Homology modeling methods have been used to construct models of two proteins—the histidine-containing phosphocarrier protein (HPr) from Mycoplasma capricolum and human eosinophil-derived neurotoxin (EDN). Comparison of the models with the subsequently determined X-ray crystal structures indicates that the core regions of both proteins are reasonably well reproduced, although the template structures are closer to the X-ray structures in these regions—possible enhancements are discussed. The conformations of most of the side chains in the core of HPr are well reproduced in the modeled structure. As expected, the conformations of surface side chains in this protein differ significantly from the X-ray structure. The loop regions of EDN were incorrectly modeled—reasons for this and possible enhancements are discussed. © 1995 Wiley-Liss, Inc.     

Progress in fold recognition

The prediction experiment reveals that fold recognition has become a powerful tool in structural biology. We applied our fold recognition technique to 13 target sequences. In two cases, replication terminating protein and prosequence of subtilisin, the predicted structures are very similar to the experimentally determined folds. For the first time, in a public blind test, the unknown structures of proteins have been predicted ahead of experiment to an accuracy approaching molecular detail. In two other cases the approximate folds have been predicted correctly. According to the assessors there were 12 recognizable folds among the target proteins. In our postprediction analysis we find that in 7 cases our fold recognition technique is successful. In several of the remaining cases the predicted folds have interesting features in common with the experimental results. We present our procedure, discuss the results, and comment on several fundamental and technical problems encountered in fold recognition. © 1995 Wiley-Liss, Inc.     

Homology modeling by the ICM method

Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on the homologous structure with the conserved portion fixed and the noncon-served portion having standard covalent geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are discussed. © 1995 Wiley-Liss, Inc.     

A model for the effects of primary substrates on the kinetics of reductive dehalogenation

A kinetic model that describes substrate interactions during reductive dehalogenation reactions is developed. This model describes how the concentrations of primary electron-donor and -acceptor substrates affect the rates of reductive dehalogenation reactions. A basic model, which considers only exogenous electron-donor and -acceptor substrates, illustrates the fundamental interactions that affect reductive dehalogenation reaction kinetics. Because this basic model cannot accurately describe important phenomena, such as reductive dehalogenation that occurs in the absence of exogenous electron donors, it is expanded to include an endogenous electron donor and additional electron acceptor reactions. This general model more accurately reflects the behavior that has been observed for reductive dehalogenation reactions. Under most conditions, primary electron-donor substrates stimulate the reductive dehalogenation rate, while primary electron acceptors reduce the reaction rate. The effects of primary substrates are incorporated into the kinetic parameters for a Monod-like rate expression. The apparent maximum rate of reductive dehalogenation (q _{m, ap} ) and the apparent half-saturation concentration (K _ap ) increase as the electron donor concentration increases. The electron-acceptor concentration does not affect q _{m, ap} , but K _ap is directly proportional to its concentration.Definitions for model parameters RX halogenated aliphatic substrate - E-M ⁿ reduced dehalogenase - E-M ⁿ⁺² oxidized dehalogenase - [E-M ⁿ ] steady-state concentration of the reduced dehalogenase (moles of reduced dehalogenase per unit volume) - [E-M ⁿ⁺²] steady-state concentration of the oxidized dehalogenase (moles of reduced dehalogenase per unit volume) - DH₂ primary exogenous electron-donor substrate - A primary exogenous electron-acceptor substrate - A2 second primary exogenous electron-acceptor substrate - X biomass concentration (biomass per unit volume) - f fraction of biomass that is comprised of the dehalogenase (moles of dehalogenase per unit biomass) - stoichiometric coefficient for the reductive dehalogenation reaction (moles of dehalogenase oxidized per mole of halogenated substrate reduced) - stoichiometric coefficient for oxidation of the primary electron donor (moles of dehalogenase reduced per mole of donor oxidized) - stoichiometric coefficient for oxidation of the endogenous electron donor (moles of dehalogenase reduced per unit biomass oxidized) - stoichiometric coefficient for reduction of the primary electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - stoichiometric coefficient for reduction of the second electron acceptor (moles of dehalogenase oxidized per mole of acceptor reduced) - r _RX rate of the reductive dehalogenation reaction (moles of halogenated substrate reduced per unit volume per unit time) - r _d1 rate of oxidation of the primary exogenous electron donor (moles of donor oxidized per unit volume per unit time) - r _d2 rate of oxidation of the endogenous electron donor (biomass oxidized per unit volume per unit time) - r _a1 rate of reduction of the primary exogenous electron acceptor (moles of acceptor reduced per unit volume per unit time) - r _a2 rate of reduction of the second primary electron acceptor (moles of acceptor reduced per unit volume per unit time) - k _RX mixed second-order rate coefficient for the reductive dehalogenation reaction (volume per mole dehalogenase per unit time) - k _d1 mixed-second-order rate coefficient for oxidation of the primary electron donor (volume per mole dehalogenase per unit time) - k _d2 mixed-second-order rate coefficient for oxidation of the endogenous electron donor (volume per mole dehalogenase per unit time) - b first-order biomass decay coefficient (biomass oxidized per unit biomass per unit time) - k _a1 mixed-second-order rate coefficient for reduction of the primary electron acceptor (volume per mole dehalogenase per unit time) - k _a2 mixed-second-order rate coefficient for reduction of the second primary electron acceptor (volume per mole dehalogenase per unit time) - q _m,ap apparent maximum specific rate of reductive dehalogenation (moles of RX per unit biomass per unit time) - K _ap apparent half-saturation concentration for the halogenated aliphatic substrate (moles of RX per unit volume) - k _ap apparent pseudo-first-order rate coefficient for reductive dehalogenation (volume per unit biomass per unit time)     

Constrained C-terminal hexapeptide neurotensin analogues containing a 3-oxoindolizidine skeleton

Summary In order to enforce different spatial orientations in the C-terminal hexapeptide of neurotensin (NT_8–13) and to gain information about the importance of the 10–11 peptide bond for binding to NT receptors, the Pro¹⁰-Tyr¹¹ fragment has been replaced with (2R,8S,8aR)-, (2S,8S,8aR)-, (2S,8S,8aS)-, (2S,8R,8aS)- and (2R,8R,8aS)-8-amino-2-benzyl-3-oxoindolizidine-2-carboxylic acid. Molecular dynamics calculations and energy minimization studies have shown that, contrarily to the Pro-Tyr moiety, none of these indolizidines display a tendency to adopt type I and III -turns, but those having (8S,8aR) or (8R,8aS) stereochemistry essentially adopt extended conformations and the (8S,8aS) stereoisomer prefers a nonstandard folding. The four diastereomeric NT_8–13 analogues incorporating (8S,8aR) or (8R,8aS) indolizidines displayed binding affinities for the brain NT receptor similar to that of [Ala¹¹]-NT_8–13 and only five- to ninefold lower than that of the corresponding analogue, [Phe¹¹]NT_8–13. Although this slight decrease could be attributed to differences in conformational behavior between these constrained NT_8–13 analogues and [Phe¹¹]NT_8–13 or NT_8–13, it is not clear whether the -turn around Pro¹⁰-AA¹¹ (AA=Phe, Tyr) is conserved upon receptor binding. An excessive restriction in the motions of the aromatic side chain, imposed by the highly steric constraint of the indolizidine moiety, emerges as an alternative explanation. The findings reported here demonstrate the possibility of replacing the Pro¹⁰-Tyr¹¹ dipeptide in NT_8–13 with a non-peptide residue without affecting considerably the affinity for brain NT receptors.     

A minimal,compartmental model for a dendritic origin of bistability of motoneuron firing patterns

Various nonlinear regenerative responses, including plateau potentials and bistable repetitive firing modes, have been observed in motoneurons under certain conditions. Our simulation results support the hypothesis that these responses are due to plateau-generating currents in the dendrites, consistent with a major role for a noninactivating calcium L-type current as suggested by experiments. Bistability as observed in the soma of low- and higher-frequency spiking or, under TTX, of near resting and depolarized plateau potentials, occurs because the dendrites can be in a near resting or depolarized stable steady state. We formulate and study a two-compartment minimal model of a motoneuron that segregates currents for fast spiking into a soma-like compartment and currents responsible for plateau potentials into a dendrite-like compartment. Current flows between compartments through a coupling conductance, mimicking electrotonic spread. We use bifurcation techniques to illuminate how the coupling strength affects somatic behavior. We look closely at the case of weak coupling strength to gain insight into the development of bistable patterns. Robust somatic bistability depends on the electrical separation since it occurs only for weak to moderate coupling conductance. We also illustrate that hysteresis of the two spiking states is a natural consequence of the plateau behavior in the dendrite compartment.     

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better.