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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...

Protein Families

Protein Families

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

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...

Mechanical Protein Function

Mechanical Protein Function

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Updated: Apr 4, 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

iPFPi: A System for Improving Protein Function Prediction through Cumulative Iterations.

Kamal Taha, Paul D Yoo, Mohammed Alzaabi

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|September 11, 2015

Summary

This summary is machine-generated.

We developed iPFPi, a novel system for predicting protein functions. It leverages semantic similarity between protein characteristics and Gene Ontology (GO) terms, improving accuracy over time.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate protein function prediction is crucial for understanding biological systems.
Un-annotated proteins represent a significant gap in biological knowledge.
Existing methods often struggle with the nuances of protein function annotation.

Purpose of the Study:

To introduce iPFPi, an innovative classifier system for predicting the functions of un-annotated proteins.
To develop a novel semantic similarity measure for robust protein function prediction.
To enhance the accuracy and efficiency of protein function annotation.

Main Methods:

iPFPi represents proteins and Gene Ontology (GO) terms by their characteristic GO terms from biomedical literature abstracts.
A novel semantic similarity measure considers the dominance and scores of characteristic terms using a pairwise comparison.
The system iteratively updates characteristic term scores and dominant sets (F and F/) based on successful annotations.

Main Results:

Experimental evaluation demonstrated marked improvement in prediction accuracy compared to two recent systems.
The iterative updating mechanism enhances the system's predictive power over time.
iPFPi effectively leverages semantic similarity for accurate protein function prediction.

Conclusions:

iPFPi offers a significant advancement in predicting un-annotated protein functions.
The system's iterative learning approach leads to continuously improving accuracy.
iPFPi provides a valuable tool for biological research and functional genomics.