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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...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

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

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Related Experiment Video

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Improving Protein Fold Recognition by Deep Learning Networks.

Taeho Jo^1,2, Jie Hou¹, Jesse Eickholt³

¹Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

Scientific Reports

|December 5, 2015

Summary

This summary is machine-generated.

A new deep learning method, DN-Fold, accurately predicts protein structural folds using sequence and structural features. This computational tool aids in understanding protein evolution and function.

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Area of Science:

Computational biology
Structural bioinformatics
Machine learning in proteomics

Background:

Accurate protein fold recognition is crucial for understanding protein function and evolution.
Existing methods face challenges in distinguishing between closely related and distant protein folds.

Purpose of the Study:

To develop and evaluate a deep learning network method (DN-Fold) for accurate protein fold recognition.
To assess DN-Fold's performance across different evolutionary distances and classification levels (family, superfamily, fold).

Main Methods:

Utilized a deep learning network (DN-Fold) that processes protein sequence and structural features.
Evaluated DN-Fold on benchmark datasets (Lindahl's and SCOP 1.75) with approximately one million protein pairs.
Compared DN-Fold's performance against 18 other methods at various fold recognition levels.

Main Results:

Ensembled DN-Fold achieved high Top 1 recognition rates: 84.5% (family), 61.5% (superfamily), and 33.6% (fold).
Top 5 recognition rates for ensembled DN-Fold were 91.2% (family), 76.5% (superfamily), and 60.7% (fold).
Single DN-Fold (DN-FoldS) demonstrated comparable performance to the ensemble method, particularly at family and superfamily levels.

Conclusions:

DN-Fold offers a robust and accurate approach for protein fold recognition using deep learning.
The method shows promise in both binary classification and real-value regression tasks for fold prediction.
DN-Fold is accessible via a web server, facilitating broader research applications.