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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 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...

Protein and Protein Structures

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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...

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...

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Updated: Jan 6, 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

Recent developments in deep learning applied to protein structure prediction.

Shaun M Kandathil^1,2, Joe G Greener^1,2, David T Jones^1,2

¹Department of Computer Science, University College London, London, UK.

|October 8, 2019

Summary

This summary is machine-generated.

Deep neural networks (DNNs) are revolutionizing structural bioinformatics, showing significant impact in recent CASP experiments. These models offer accurate predictions, even with limited sequence data, by leveraging key principles for complex biological problems.

Keywords:

deep learning protein structure prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

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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:

Structural Bioinformatics
Computational Biology
Machine Learning

Background:

Neural network models have a long history in structural bioinformatics.
Deep neural network (DNN) models have gained prominence recently due to their success.
DNNs have demonstrated significant impact in Critical Assessment of protein Structure Prediction (CASP) experiments, particularly CASP12 and CASP13.

Purpose of the Study:

To introduce the fundamental principles and properties of DNN models.
To discuss the suitability of DNNs for specific structural bioinformatics challenges.
To explore the methodological advancements enabling DNN successes.

Main Methods:

Introduction to key principles and properties of DNN models.
Discussion of methodological improvements driving DNN performance.
Application of DNNs to the contact prediction task in structural bioinformatics.

Main Results:

DNN models are well-suited for various structural bioinformatics problems.
Methodological improvements have significantly enhanced DNN capabilities.
DNNs can achieve accurate contact predictions even with limited homologous sequences.

Conclusions:

DNN models are highly effective in structural bioinformatics due to their inherent properties.
Understanding DNNs' strengths and potential pitfalls is crucial for their application.
Further research into DNNs promises continued advancements in predicting protein structures.