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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

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...

Molecular Chaperones and Protein Folding

Molecular Chaperones and Protein Folding

Mechanical Protein Functions

Mechanical Protein Functions

Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

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Related Experiment Video

Updated: Apr 22, 2026

Thermodynamics of Membrane Protein Folding Measured by Fluorescence Spectroscopy

Thermodynamics of Membrane Protein Folding Measured by Fluorescence Spectroscopy

Published on: April 28, 2011

Learning Protein Folding Energy Functions.

Wei Guan¹, Arkadas Ozakin², Alexander Gray¹

¹College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332.

Proceedings. IEEE International Conference on Data Mining

|October 15, 2014

Summary

This summary is machine-generated.

This study optimizes protein energy functions using machine learning, specifically learning-to-rank and novel non-negative support vector machine methods. The developed approach improves protein folding efficiency and reliability compared to current state-of-the-art methods.

Keywords:

ab initio protein folding energy function learning-to-rank non-negativity constrained SVM optimization support vector machine

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Last Updated: Apr 22, 2026

Thermodynamics of Membrane Protein Folding Measured by Fluorescence Spectroscopy

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Published on: April 28, 2011

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

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational Biology
Biophysics
Machine Learning

Background:

Protein folding is crucial for biological function, but designing accurate energy functions remains a challenge.
Current methods for protein energy function design face limitations in efficiency and reliability.

Purpose of the Study:

To address the open problem of protein energy function design.
To develop a machine learning approach for optimizing protein energy functions.
To improve the efficiency and reliability of protein folding simulations.

Main Methods:

Framing protein energy function design as a weight optimization problem.
Utilizing a learning-to-rank machine learning approach, specifically RankingSVM.
Developing two novel non-negative support vector machine (NNSVM) methods (L2-norm and L1-norm) with non-negativity constraints.

Main Results:

The developed energy function demonstrates improved ordering concerning structure dissimilarity to the native state.
The new method is more efficient and reliable for learning on large protein datasets.
The proposed energy function is qualitatively superior to the current state-of-the-art.

Conclusions:

Machine learning, particularly learning-to-rank and NNSVM, offers a powerful approach to protein energy function design.
The novel NNSVM methods provide physically meaningful constraints for energy function optimization.
This work advances the field of *ab initio* protein folding by providing a more effective energy function.