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Related Concept Videos

Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein Organization01:13

Protein Organization

Overview
Protein Folding01:22

Protein Folding

Overview
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse.
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Coarse-graining protein structures with local multivariate features from molecular dynamics.

Zhiyong Zhang1, Willy Wriggers

  • 1School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

The Journal of Physical Chemistry. B
|October 16, 2008
PubMed
Summary
This summary is machine-generated.

Local Feature Analysis (LFA) uses a novel Monte Carlo algorithm to coarse-grain protein dynamics from molecular dynamics (MD) simulations. This method improves upon PCA by providing invariant, localized features and assessing sampling efficiency.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Statistical Mechanics

Background:

  • Local Feature Analysis (LFA) is a multivariate statistical method for extracting functionally relevant domains from molecular dynamics (MD) trajectories.
  • LFA provides low-dimensional representations of protein collective motions, contrasting with global principal component analysis (PCA) modes.
  • A challenge in LFA is coarse-graining redundant output functions using seed atoms, with existing sequential algorithms not guaranteeing optimal feature correlation.

Purpose of the Study:

  • To introduce a novel coarse-graining algorithm for proteins that directly minimizes mutual correlation of seed atoms.
  • To assess the algorithm's ability to provide statistically reproducible results and describe functionally relevant dynamics.
  • To offer a localized measure of MD sampling efficiency and improve upon PCA for protein dynamics analysis.

Main Methods:

  • Developed a new coarse-graining algorithm utilizing Monte Carlo (MC) simulations to minimize mutual correlation of seed atoms.
  • Applied the algorithm to MD trajectories of bacteriophage T4 lysozyme and myosin II motor domain S1.
  • Compared the novel coarse-graining approach with traditional PCA methods for analyzing protein dynamics.

Main Results:

  • The novel MC-based coarse-graining algorithm yields statistically reproducible results for protein dynamics.
  • Converged features derived from the new method are invariant across trajectory windows, delineating converged and undersampled protein regions.
  • The algorithm effectively describes functionally relevant dynamics in the tested biological systems.

Conclusions:

  • The proposed coarse-graining algorithm offers a significant advantage over PCA by providing invariant, localized features.
  • This method enhances the analysis of protein dynamics and structure classification from MD simulations.
  • The algorithm serves as a valuable tool for assessing MD sampling efficiency at a localized level.