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Protein Folding

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...

Protein Folding

Protein Folding

Protein Folding

Protein Folding

Protein Organization

Protein Organization

Protein Organization

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.

Molecular Chaperones and Protein Folding

Molecular Chaperones and Protein Folding

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.
The...

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Related Experiment Video

Updated: Jun 30, 2026

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

Published on: September 15, 2010

Peptide folding using multiscale coarse-grained models.

Ian F Thorpe¹, Jian Zhou, Gregory A Voth

¹Center for Biophysical Modeling and Simulation, Department of Chemistry, University of Utah, Salt Lake City, UT 84112-0850, USA.

The Journal of Physical Chemistry. B

|September 24, 2008

Summary

This summary is machine-generated.

Multiscale coarse-graining (MS-CG) models enable efficient peptide refolding simulations. These models accurately capture peptide folding landscapes and offer enhanced sampling compared to all-atom methods.

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Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding

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Published on: September 15, 2010

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Published on: November 21, 2013

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Published on: January 26, 2024

Area of Science:

Computational chemistry
Biophysics
Molecular modeling

Background:

Multiscale coarse-graining (MS-CG) is established for peptide equilibrium properties.
Previous studies have not fully explored MS-CG's potential for peptide folding dynamics.

Purpose of the Study:

To investigate the refolding capabilities of MS-CG models for peptides.
To assess the accuracy of MS-CG models in representing all-atom peptide free energy landscapes.
To evaluate the enhanced sampling efficiency of MS-CG models.

Main Methods:

Simulations of MS-CG models for alpha-helical polyalanine and V 5PGV 5 beta-hairpin.
Analysis of free energy landscapes and folding behavior.
Reconstruction of atomically detailed configurations from MS-CG ensembles.
Comparison with all-atom simulation data.

Main Results:

MS-CG models demonstrated efficient refolding from unfolded states.
Peptide free energy landscapes showed funneling towards folded states and two-state behavior.
MS-CG models provided enhanced sampling compared to all-atom simulations.
Reconstructed all-atom configurations showed good agreement with MS-CG and all-atom data.

Conclusions:

MS-CG models effectively capture peptide folding dynamics and landscapes.
The enhanced sampling capabilities of MS-CG models are significant for studying peptide folding.
MS-CG models show considerable utility for future peptide folding research.