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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

Protein Organization

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-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein-Protein Interfaces

Protein-Protein Interfaces

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Updated: Dec 31, 2025

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

Improved protein structure prediction using predicted interresidue orientations.

Jianyi Yang¹, Ivan Anishchenko^2,3, Hahnbeom Park^2,3

¹School of Mathematical Sciences, Nankai University, 300071 Tianjin, China.

Proceedings of the National Academy of Sciences of the United States of America

|January 4, 2020

Summary

This summary is machine-generated.

This study introduces a deep residual network and Rosetta-constrained protocol for enhanced protein structure prediction, accurately modeling interresidue orientations and distances. The method excels in benchmark tests and evaluates protein design "ideality".

Keywords:

deep learning protein contact prediction protein structure prediction

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Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Deep learning and coevolutionary data have significantly improved protein structure prediction.
Accurate prediction of interresidue contacts and distances is crucial for modeling protein structures.

Purpose of the Study:

To develop a deep residual network for predicting interresidue orientations and distances.
To create a Rosetta-constrained energy-minimization protocol for rapid and accurate protein structure modeling.
To evaluate the method's performance against existing structure prediction techniques.

Main Methods:

Development of a deep residual network for predicting interresidue orientations and distances.
Integration with a Rosetta-constrained energy-minimization protocol for structure generation.
Benchmarking on datasets from the Critical Assessment of Techniques for Protein Structure Prediction (CASP13) and Continuous Automated Model Evaluation (CAMEO).

Main Results:

The developed method outperforms previous protein structure prediction techniques in benchmark tests.
The network successfully predicts interresidue orientations and distances, guiding structure generation.
The method assigns higher probabilities to de novo-designed proteins and quantifies structural "ideality".

Conclusions:

The novel deep residual network and Rosetta-constrained protocol represent a significant advancement in protein structure prediction.
This approach offers a powerful tool for both protein structure prediction and protein design.
The method provides a quantitative measure for assessing the quality of protein structures.