Biology subjects

Seelig, J.

Publications and source records attributed to Seelig, J..

8 recordsLinked to original sources

bioRxiv2024

Pressure protein denaturation compared to thermal and chemical unfolding - Analyses with cooperative models

The thermodynamics of pressure induced protein unfolding (denaturation) could so far not be directly compared with protein unfolding induced by temperature or chemical agents. Here we provide a new cooperative model for pressure induced protein denaturation, that allows the quantitative comparison of the three denaturing processes based on their free energy, enthalpy, entropy, and cooperativity. As model proteins we use apolipoprotein A-1 and lysozyme. The comparison shows that heat-induced unfolding is the most cooperative process. It is characterized by large positive enthalpies and entropies and (due to enthalpy-entropy compensation) a small negative free energies. Pressure denaturation is less cooperative. The entropies and enthalpies are less positive, and the resulting free energies are more negative. Chemically induced unfolding is least cooperative and shows the most negative free energies, in particular, if guanidinium hydrochloride (exhibiting a high binding affinity to certain proteins) is used as a denaturant. The three unfolding processes not only differ with respect to their cooperativity and the thermodynamic parameters, but also with respect to the volume changes. Whereas thermal and chemical denaturation increase the protein volume, denaturation by pressure reduces the protein volume, suggesting significant structural differences of the denatured proteins. Using cooperative models for protein analyses thus yields significant new insights into the protein unfolding/folding processes.

biophysics↗

bioRxiv2023

A rotational velocity estimate constructed through visuomotor competition updates the fly's neural compass

Navigating animals continuously integrate velocity signals to update internal representations of their directional heading and spatial location in the environment. How neural circuits combine sensory and motor information to construct these velocity estimates and how these self-motion signals, in turn, update internal representations that support navigational computations are not well understood. Recent work in Drosophila has identified a neural circuit that performs angular path integration to compute the flys head direction, but the nature of the velocity signal is unknown. Here we identify a pair of neurons necessary for angular path integration that encode the flys rotational velocity with high accuracy using both visual optic flow and motor information. This estimate of rotational velocity does not rely on a moment-to-moment integration of sensory and motor information. Rather, when visual and motor signals are congruent, these neurons prioritize motor information over visual information, and when the two signals are in conflict, reciprocal inhibition selects either the motor or visual signal. Together, our results suggest that flies update their head direction representation by constructing an estimate of rotational velocity that relies primarily on motor information and only incorporates optic flow signals in specific sensorimotor contexts, such as when the motor signal is absent.

neuroscience↗

bioRxiv2023

Chemical protein unfolding - A simple cooperative model

Chemical unfolding with guanidineHCl or urea is a common method to study the conformational stability of proteins. The analysis of unfolding isotherms is usually performed with an empirical linear extrapolation method (LEM). The method contradicts however, common expectations. A large positive free energy is assigned to the native protein which is usually considered to be a minimum of the free energy. Here we present a multistate cooperative model, which addresses specifically the binding of the denaturant to the protein and the cooperativity of the protein unfolding equilibrium. The model is based on a molecular statistical-mechanical partition function of the ensemble but simple solutions for the calculation of the binding isotherm and the associated free energy are presented. The model is applied to 23 published unfolding isotherms of small and large proteins. For a given denaturant, the binding constant depends on temperature and pH, but shows little protein specificity. Chemical unfolding is less cooperative than thermal unfolding. The cooperativity parameter {sigma} is two orders of magnitude larger than that of thermal unfolding. The multistate cooperative model predicts a zero free energy for the native protein, which becomes strongly negative beyond the midpoint concentration of unfolding. The free energy to unfold a cooperative unit corresponds exactly to the diffusive energy of the denaturant concentration gradient necessary for unfolding. The temperature dependence of unfolding isotherms yields the denaturant-induced unfolding entropy and, in turn, the unfolding enthalpy. The multistate cooperative model provides molecular insight, is as simple to apply as the LEM, but avoids the conceptual difficulties of the latter.

biophysics↗

bioRxiv2022

Protein unfolding. Thermodynamic perspectives and unfolding models.

Protein unfolding is a dynamic cooperative process with many short-lived intermediates. Cooperativity means that similar molecular elements act dependently on each other. The thermodynamics of protein unfolding can be determined with differential scanning calorimetry (DSC). The measurement of the heat capacity provides the temperature profiles of enthalpy, entropy and free energy. The thermodynamics of protein unfolding is completely determined with these thermodynamic properties. We emphasise the model-independent analysis of the heat capacity. The temperature profiles of enthalpy H(T), entropy S(T) and free energy G(T) can be obtained directly by a numerical integration of Cp(T). In evaluating different models for protein unfolding. It is essential to simulate all thermodynamic properties, not only the heat capacity. A chemical equilibrium two-state model is a widely used approximation to protein unfolding. The model assumes a chemical equilibrium between only two protein conformations, the native protein (N) and the unfolded protein (U). The model fits the heat capacity Cp(T) quite well, but fails in simulating the other thermodynamic properties. In this review we propose a modification of the chemical equilibrium two-state model, which removes these inconsistencies. We also propose a new statistical-mechanical two-state model based on a simple, two-parameter partition function Z(T), from which all thermodynamic parameters can be derived. The thermodynamic predictions of the new models are compared to published DSC-experiments obtained with lysozyme, a globular protein, and {beta}-lactoglobulin, a {beta}-barrel protein. Good fits to all thermodynamic properties are obtained. In particular, the models predict a zero free energy for the native protein, which is confirmed experimentally by DSC. This is in contrast to the often-cited chemical equilibrium two-state model, which predict a positive free energy for the native protein. Two-state models use macroscopic fit parameters, the conformational enthalpy and the heat capacity difference between native and unfolded protein. These simulations provide no molecular insight. The review therefore includes a recently published multistate cooperative model based on physicality well-defined molecular parameters only.

biophysics↗

bioRxiv2021

Molecular Understanding of Calorimetric Protein Unfolding Experiments

Protein unfolding is a dynamic cooperative equilibrium between short lived protein conformations. The Zimm-Bragg theory is an ideal algorithm to handle cooperative processes. Here, we extend the analytical capabilities of the Zimm-Bragg theory in two directions. First, we combine the Zimm-Bragg partition function Z(T) with statistical-mechanical thermodynamics, explaining the thermodynamic system properties enthalpy, entropy and free energy with molecular parameters only. Second, the molecular enthalpy h0 to unfold a single amino acid residue is made temperature-dependent. The addition of a heat capacity term cv allows predicting not only heat denaturation, but also cold denaturation. Moreover, it predicts the heat capacity increase [Formula] in protein unfolding. The theory is successfully applied to differential scanning calorimetry experiments of proteins of different size and structure, that is, gpW62 (62aa), ubiquitin (74aa), lysozyme (129aa), metmyoglobin (153aa) and mAb monoclonal antibody (1290aa). Particular attention was given to the free energy, which can easily be obtained from the heat capacity Cp(T). The DSC experiments reveal a zero free energy for the native protein with an immediate decrease to negative free energies upon cold and heat denaturation. This trapezoidal shape is precisely reproduced by the Zimm-Bragg theory, whereas the so far applied non-cooperative 2-state model predicts a parabolic shape with a positive free energy maximum of the native protein. We demonstrate that the molecular parameters of the Zimm-Bragg theory have a well-defined physical meaning. In addition to predicting protein stability, independent of protein size, they yield estimates of unfolding kinetics and can be connected to molecular dynamics calculations.

biophysics↗

bioRxiv2019

Thermal and Chemical Unfolding of a recombinant monoclonal IgG1 antibody: Application of the Multi-State Zimm-Bragg Theory

The thermal unfolding of a recombinant monoclonal antibody IgG1 (mAb) was measured with differential scanning calorimetry (DSC). The DSC thermograms reveal a pre-transition at 72{degrees}C with an unfolding enthalpy of {Delta}Hcal [~] 200-300 kcal/mol and a main transition at 85 {degrees}C with an enthalpy of [~]900 - 1000 kcal/mol. In contrast to single-domain molecules, mAb unfolding is a complex reaction that is analysed with the multi-state Zimm-Bragg theory. For the investigated mAb, unfolding is characterised by a cooperativity parameter {sigma} [~]10-4 and a Gibbs free energy of unfolding of gnu [~]100 cal/mol per amino acid. The enthalpy of unfolding provides the number of amino acid residues v participating in the unfolding reaction. On average, v[~]220{+/-}50 amino acids are involved in the pre-transition and v[~]850{+/-}30 in the main transition, accounting for [~]90% of all amino acids. Thermal unfolding was further studied in the presence of guanidineHCl. The chemical denaturant reduces the unfolding enthalpy {Delta}Hcal and lowers the midpoint temperature T0. Both parameters depend linearly on the concentration of denaturant. The guanidineHCl concentrations needed to unfold mAb at 25 {degrees}C are predicted to be 2-3 M for the pre-transition and 5-7 M for the main transition, varying with pH. GuanidineHCl binds to mAb with an exothermic binding enthalpy, which partially compensates the endothermic mAb unfolding enthalpy. The number of guanidineHCL molecules bound upon unfolding is deduced from the DSC thermograms. The bound guanidineHCl-to-unfolded amino acid ratio is 0.79 for the pre-transition and 0.55 for the main transition. The pre-transition binds more denaturant molecules and is more easily destabilised than the main transition.\n\nOverall, the current study shows the strength of the Zimm-Bragg model for the quantitative description of unfolding events of large, therapeutic proteins, such as a monoclonal antibody.\n\nStatement of significanceFirst quantitative thermodynamic study of an antibody with differential scanning calorimetry and analyzed with the multi-state Zimm-Bragg theory.

biophysics↗

bioRxiv2019

Virtual reality for animal navigation with camera-based optical flow tracking

BackgroundVirtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.\n\nNew MethodWe developed an optical flow-based method for tracking treadmil ball motion in real-time using a single high-resolution camera.\n\nResultsTracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 {+/-} 8 ms.\n\nComparison with Existing Method(s)Optical flow based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions - such as ball surface texture, lighting conditions, or ball size - as well as camera alignment and calibration.\n\nConclusionsA fast system for rotational ball motion tracking suitable for virtual reality animal behavior across different scales was developed and characterized.

neuroscience↗

bioRxiv2019

Bessel beam tomography for fast volume imaging

Light microscopy on dynamic samples, for example neural activity in the brain, requires imaging large volumes at high rates. Here, we develop a tomography approach for scanning fluorescence microscopy which allows recording volume images at frame scan rates. Volumes are imaged by simultaneously recording four independent projections at different angles using temporally multiplexed, tilted Bessel beams. From the resulting projections, volumes are reconstructed using inverse Radon transforms combined with three dimensional convolutional neural networks (U-net). This tomography approach is suitable for experiments requiring fast volume imaging of sparse samples, as for example often encountered when imaging neural activity in the brain.

neuroscience↗