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Related Concept Videos

Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

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The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
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Conservation of Protein Domains Over Different Proteins02:26

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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.
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Protein Folding Quality Check in the RER01:29

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Protein Organization01:24

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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.
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Related Experiment Video

Updated: Jun 23, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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A Stochastic Landscape Approach for Protein Folding State Classification.

Michael Faran1, Dhiman Ray2, Shubhadeep Nag1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.

Journal of Chemical Theory and Computation
|June 26, 2024
PubMed
Summary

This study introduces Stochastic Landscape Classification (SLC), an automated algorithm that analyzes protein folding dynamics. SLC reveals protein folding pathways by segmenting collective variables, aiding in understanding disease mechanisms and protein behavior.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Biochemistry

Background:

  • Protein folding is crucial for biological function; misfolding is linked to diseases like Alzheimer's.
  • Understanding protein folding dynamics is vital for disease mechanism research and therapeutic development.

Purpose of the Study:

  • To introduce Stochastic Landscape Classification (SLC), an automated, non-learning algorithm for quantitative analysis of protein folding dynamics.
  • To segment collective variables (CVs) into distinct macrostates, revealing protein folding pathways from molecular dynamics (MD) simulations.

Main Methods:

  • Developed the Stochastic Landscape Classification (SLC) algorithm.
  • Segmented CV trajectories by analyzing trend changes and applying DBSCAN clustering.
  • Validated SLC accuracy using standard classification metrics against ground-truth data for Chignolin and Trp-Cage proteins.

Main Results:

  • SLC accurately segments CVs, revealing protein folding pathways explored in MD simulations.
  • The algorithm demonstrated high accuracy in capturing intricate protein dynamics.
  • SLC provides a method for evaluating and selecting the most informative CVs.

Conclusions:

  • SLC offers a quantitative method to describe protein folding processes.
  • This technique has significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical applications.
  • SLC enhances the study of protein dynamics and disease-related misfolding.