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Protein Organization

Protein Organization

Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key locations, protein...

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 form...

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 polypeptide...

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 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.

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Updated: Jul 1, 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

Towards automatic clustering of protein sequences.

Jiong Yang¹, Wei Wang

¹T. J. Watson Research Center, IBM, USA. jiyang@us.ibm.com

Proceedings. IEEE Computer Society Bioinformatics Conference

|April 20, 2005

Summary

This study introduces a new protein sequence clustering method using statistical properties and imprecise probabilities. It effectively groups unlabeled protein sequences into meaningful families without needing pre-labeled training data.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein sequence analysis is crucial in modern biology.
Existing methods often focus on classification, requiring labeled data.
Clustering unlabeled protein sequences presents challenges due to the lack of efficient similarity measures.

Purpose of the Study:

To develop an automatic clustering method for unlabeled protein sequences.
To address the limitations of current similarity measures in protein sequence analysis.
To discover inherent groupings and correlations within protein sequence data.

Main Methods:

Proposed a novel clustering model for protein sequences.
Utilized significant statistical properties inherent to protein sequences.

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Integrated imprecise probabilities with probabilistic suffix trees to guide clustering.

Monitored convergence of empirical measurements for effective clustering.

Main Results:

Successfully clustered unlabeled protein sequences into meaningful families.
Demonstrated the discovery of inherent protein families without prior labels.
The proposed method efficiently identifies hidden correlations among protein sequences.

Conclusions:

The novel model provides an effective approach for unsupervised protein sequence clustering.
Statistical properties and imprecise probabilities enhance clustering accuracy and efficiency.
This method eliminates the need for pre-labeled training data in protein family discovery.