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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 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....

Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

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Updated: Mar 27, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Sheng Wang^1,2, Jian Peng³, Jianzhu Ma¹

¹Toyota Technological Institute at Chicago, Chicago, IL.

Scientific Reports

|January 12, 2016

Summary

DeepCNF, a novel deep learning method, significantly improves protein secondary structure prediction accuracy. This advancement aids in understanding protein structure and function, surpassing existing methods.

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Area of Science:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

Protein secondary structure (SS) prediction is crucial for understanding protein structure and function.
Current sequence-based SS predictors achieve limited accuracy (~80% Q3) and have seen no improvement for a decade.

Purpose of the Study:

To introduce DeepCNF (Deep Convolutional Neural Fields), a novel deep learning framework for enhanced protein SS prediction.
To leverage deep hierarchical architectures and model interdependencies between adjacent SS labels for improved accuracy.

Main Methods:

DeepCNF integrates Conditional Random Fields (CRF) with deep neural networks, extending shallow Conditional Neural Fields (CNF).
The model utilizes a deep hierarchical architecture to capture complex sequence-structure relationships.

Main Results:

DeepCNF achieved ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy on CASP and CAMEO datasets.
These results significantly outperform existing popular protein SS prediction methods.

Conclusions:

DeepCNF represents a substantial advancement in protein secondary structure prediction accuracy.
The DeepCNF framework is versatile and applicable to predicting other protein structural properties.