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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

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 can...

Protein Folding

Protein Folding

Protein Folding

Protein Folding

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Updated: May 25, 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

Machine learning methods for protein structure prediction.

Jianlin Cheng¹, Allison N Tegge, Pierre Baldi

¹Department, University of Missouri, Columbia, MO 65211, USA. chengji@missouri.edu

IEEE Reviews in Biomedical Engineering

|January 26, 2012

Summary

This summary is machine-generated.

Machine learning advances protein structure prediction across 1-D, 2-D, 3-D, and 4-D levels. This review covers key methods like neural networks and support vector machines in bioinformatics.

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Last Updated: May 25, 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

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

Bioinformatics
Computational Biology
Systems Biology
Structural Biology

Background:

Protein structure prediction is a fundamental challenge in bioinformatics.
The complexity necessitates a multi-level approach: 1-D (sequence features), 2-D (spatial relationships), 3-D (tertiary structure), and 4-D (quaternary structure).

Purpose of the Study:

To review the development and application of machine learning methods for protein structure prediction.
To highlight advancements in tackling the four distinct levels of protein structure prediction.

Main Methods:

Review of supervised and unsupervised machine learning techniques.
Focus on Hidden Markov Models, Neural Networks, Support Vector Machines, Bayesian methods, and Clustering methods.
Application across 1-D, 2-D, 3-D, and 4-D protein structure prediction problems.

Main Results:

Machine learning methods have significantly advanced the state-of-the-art in protein structure prediction.
Diverse ML approaches have been successfully applied to all four levels of prediction.
The review consolidates the impact of various ML algorithms on structural biology.

Conclusions:

Machine learning is integral to modern protein structure prediction.
Continued development of ML methods will further enhance our understanding of protein structures and functions.
This review provides a comprehensive overview of ML's role in addressing fundamental problems in structural biology.