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Protein Folding

Protein Folding

Protein Folding

Protein Folding

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 Quality Check in the RER

Protein Folding Quality Check in the RER

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...

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....

Protein Organization

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Updated: Apr 21, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Improving protein fold recognition by random forest.

Taeho Jo, Jianlin Cheng

BMC Bioinformatics

|October 29, 2014

Summary

This summary is machine-generated.

RF-Fold, a new method using random forest, accurately recognizes protein structural folds. This machine learning approach shows strong performance in classifying protein structures, aiding in template-based modeling.

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Published on: July 25, 2013

Area of Science:

Computational Biology
Structural Bioinformatics
Machine Learning

Background:

Protein fold recognition is crucial for template-based protein structure modeling.
Machine learning methods are effective for classifying protein folds.
RF-Fold utilizes the random forest algorithm for fold recognition.

Purpose of the Study:

To develop and evaluate RF-Fold, a novel method for protein fold recognition.
To assess the effectiveness of the random forest algorithm in predicting protein structural similarity.

Main Methods:

Development of RF-Fold, a classification model based on random forest.
Training and evaluation on a large dataset of target-template protein pairs (Lindahl's benchmark).
Cross-validation to compare RF-Fold against 17 existing fold recognition methods.

Main Results:

RF-Fold demonstrates performance comparable to top methods across family, superfamily, and fold levels.
Top-one recognition rates: 84.5% (family), 63.4% (superfamily), 40.8% (fold).
Top-five recognition rates: 91.5% (family), 79.3% (superfamily), 58.3% (fold).

Conclusions:

RF-Fold effectively utilizes random forest for accurate protein fold recognition.
The method's performance highlights the potential of machine learning in structural bioinformatics.