Search arXiv⌕ Search

arXiv · 0809.5272

Collective Molecular Dynamics in Proteins and Membranes

Abstract

The understanding of dynamics and functioning of biological membranes and in particular of membrane embedded proteins is one of the most fundamental problems and challenges in modern biology and biophysics. In particular the impact of membrane composition and properties and of structure and dynamics of the surrounding hydration water on protein function is an upcoming hot topic, which can be addressed by modern experimental and computational techniques. Correlated molecular motions might play a crucial role for the understanding of, for instance, transport processes and elastic properties, and might be relevant for protein function. Experimentally that involves determining dispersion relations for the different molecular components, i.e., the length scale dependent excitation frequencies and relaxation rates. Only very few experimental techniques can access dynamical properties in biological materials on the nanometer scale, and resolve dynamics of lipid molecules, hydration water molecules and proteins and the interaction between them. In this context, inelastic neutron scattering turned out to be a very powerful tool to study dynamics and interactions in biomolecular materials up to relevant nanosecond time scales and down to the nanometer length scale. We review and discuss inelastic neutron scattering experiments to study membrane elasticity and protein-protein interactions of membrane embedded proteins.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maikel C. Rheinstadter. 2008-09-30. Collective Molecular Dynamics in Proteins and Membranes. https://doi.org/10.1116/1.3007992

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models

We present a machine learning approach for predicting the organisation of corneal, glial and fibroblast cells in 3D cultures used for tissue engineering. Our machine-learning-based method uses a generative adversarial network architecture called pix2pix, which we train using results from biophysical contractile network dipole orientation (CONDOR) simulations. In the following, we refer to the machine learning method as the RAPTOR (RApid Prediction of Tissue ORganisation) approach. A training data set containing a range of CONDOR simulations is created, covering a range of underlying model parameters. Predictions of the trained neural network are compared with cultured glial, corneal, and fibroblast tissues, with good agreements for both CONDOR and RAPTOR approaches. An approach is developed to determine CONDOR model parameters for specific tissues using both RAPTOR and CONDOR fits to tissue properties. RAPTOR outputs a variety of tissue properties, including cell densities, cell alignments and tension. RAPTOR yields predictions of tissue properties within fractions of a second. This speed makes it valuable for the design of tethered moulds for tissue growth.

physics.bio-ph↗

How do incorrect ligands help detect a correct ligand?

Intrigued by the response of T cell receptors to the presence of a few agonist ligands, we propose a minimal model that can achieve similar performance. The model consists of a small cluster of immobile receptors that bind reversibly to two types (correct/incorrect) of ligands in the environment, with slightly weaker binding strength for the incorrect one. It features binding-state coupling between nearest-neighbor receptors, and receptors in the bound/free states are activated/deactivated by specific enzymes, with rates that allow kinetic proofreading. It is found that, for a range of binding-state coupling strength, incorrect ligands alone cannot activate the receptors, but the binding of merely one correct ligand to a receptor is sufficient to promote the activation of other receptors via induced binding to incorrect ligands. Both response time and signal amplification increase as the receptor binding-state coupling strength increases until it reaches an optimal range to achieve the most rapid and sensitive response. These results suggest a possible mechanism for a speedy and specific response of receptors to very few correct ligands in biological and artificial systems at the subcellular scale.

physics.bio-ph↗

Spatially Resolved Nucleated Polymerization: A Free-Boundary Model of Protein Aggregation in Concentrated Solutions

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

physics.bio-ph↗