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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 Organization

Protein Organization

Protein Organization

Protein Organization

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

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Updated: Mar 21, 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

Gleaning structural and functional information from correlations in protein multiple sequence alignments.

Andrew F Neuwald¹

¹Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, 801 West Baltimore St., BioPark II, Room 617, Baltimore, MD 21201, United States.

Current Opinion in Structural Biology

|May 15, 2016

Summary

This summary is machine-generated.

Direct Coupling Analysis (DCA) and Bayesian Partitioning with Pattern Selection (BPPS) leverage protein sequence data to uncover evolutionary constraints. Their combined application enhances understanding of protein structure and function by analyzing correlated residue patterns.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Computational Biology and Bioinformatics
Molecular Evolution and Genomics
Protein Structure and Function Prediction

Background:

Large-scale protein sequence data enables the identification of statistical correlations.
These correlations are often attributed to evolutionary pressures like compensatory mutations.
Existing methods like Direct Coupling Analysis (DCA) have shown success in predicting protein properties.

Purpose of the Study:

To explore the utility of Bayesian Partitioning with Pattern Selection (BPPS) as a complementary approach to DCA.
To differentiate between correlations arising from compensatory mutations versus those from broader evolutionary divergence.
To enhance the understanding of structural and functional constraints influencing protein sequence evolution.

Main Methods:

Application of Direct Coupling Analysis (DCA) to identify correlations potentially due to compensatory mutations.
Utilizing Bayesian Partitioning with Pattern Selection (BPPS) to group proteins based on correlated residue patterns.
Hierarchical partitioning of protein families to distinguish different types of evolutionary constraints.

Main Results:

DCA has demonstrated success in predicting 3D contacts, protein structures, binding sites, and conformational heterogeneity.
BPPS identifies hierarchical subgroups based on correlated residue patterns, suggesting constraints from evolutionary divergence.
The joint application of DCA and BPPS offers a more nuanced view of sequence correlations.

Conclusions:

Combining DCA and BPPS provides a powerful framework for dissecting the origins of sequence correlations in proteins.
This integrated approach aids in distinguishing between compensatory mutations and other evolutionary constraints.
Improved understanding of these constraints can lead to more accurate predictions of protein structure and function.