A simulated continuous wave electron paramagnetic resonance spectrum of a nitroxide spin label can be obtained from the Fourier transform of a free induction decay. It has been previously shown that the free induction decay can be calculated by solving the time-dependent stochastic Liouville equation for a set of Brownian trajectories defining the rotational dynamics of the label. In this work, a quaternion-based Monte Carlo algorithm has been developed to generate Brownian trajectories describing the global rotational diffusion of a spin-labeled protein. Also, molecular dynamics simulations of two spin-labeled mutants of T4 lysozyme, T4L F153R1, and T4L K65R1 have been used to generate trajectories describing the internal dynamics of the protein and the local dynamics of the spin-label side chain. Trajectories from the molecular dynamics simulations combined with trajectories describing the global rotational diffusion of the protein are used to account for all of the dynamics of a spin-labeled protein. Spectra calculated from these combined trajectories correspond well to the experimental spectra for the buried site T4L F153R1 and the helix surface site T4L K65R1. This work provides a framework to further explore the modeling of the dynamics of the spin-label side chain in the wide variety of labeling environments encountered in site-directed spin labeling studies.
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http://dx.doi.org/10.1529/biophysj.107.125419 | DOI Listing |
Ecol Evol
November 2024
Departamento de Biodiversidade, Evolução e Meio Ambiente Universidade Federal de Ouro Preto Ouro Preto Brazil.
Trait evolution has become a central focus in evolutionary biology, with phylogenetic comparative methods offering a framework to study how and why traits vary among species. Identifying variations in trait evolution rates within phylogenies is important for uncovering the mechanisms behind these differences. Karyotype variation, which is substantial across eukaryotic organisms, plays an essential role in species diversification.
View Article and Find Full Text PDFPhys Rev E
October 2024
Wilczek Quantum Center, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Proc Natl Acad Sci U S A
November 2024
Équipe Méthodes et Algorithmes pour la Bioinformatique, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, CNRS-UMR 5506, Montpellier 34095, France.
Accurate estimation of the dispersal velocity or speed of evolving organisms is no mean feat. In fact, existing probabilistic models in phylogeography or spatial population genetics generally do not provide an adequate framework to define velocity in a relevant manner. For instance, the very concept of instantaneous speed simply does not exist under one of the most popular approaches that models the evolution of spatial coordinates as Brownian trajectories running along a phylogeny.
View Article and Find Full Text PDFSci Rep
October 2024
Division of Hydrologic Sciences, Desert Research Institute, Reno, 89512, USA.
Particle tracking (PT) is a popular technique in microscopy, microfluidics and colloidal transport studies, where image analysis is used to reconstruct trajectories from bright spots in a video. The performance of many PT algorithms has been rigorously tested for directed and Brownian motion in open media. However, PT is frequently used to track particles in porous media where complex geometries and viscous flows generate particles with high velocity variability over time.
View Article and Find Full Text PDFJ Chem Phys
September 2024
Institute for Theoretical Physics, Georg-August-Universität Göttingen, 37073 Göttingen, Germany.
When a probe particle immersed in a fluid with nonlinear interactions is subject to strong driving, the cumulants of the stochastic force acting on the probe are nonlinear functionals of the driving protocol. We present a Volterra series for these nonlinear functionals by applying nonlinear response theory in a path integral formalism, where the emerging kernels are shown to be expressed in terms of connected equilibrium correlation functions. The first cumulant is the mean force, the second cumulant characterizes the non-equilibrium force fluctuations (noise), and higher order cumulants quantify non-Gaussian fluctuations.
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