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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,...

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Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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...

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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

Protein Function Prediction with Incomplete Annotations.

Guoxian Yu, Huzefa Rangwala, Carlotta Domeniconi

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|September 11, 2015

Summary

This summary is machine-generated.

This study introduces ProWL and ProWL-IF, novel computational methods for protein function prediction that address incomplete annotations. These approaches effectively identify missing protein functions, improving accuracy in biological research.

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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:

Computational biology
Bioinformatics
Genomics

Background:

Automated protein function prediction is a significant challenge in computational biology.
Multi-label learning is commonly used but often assumes complete protein annotations, which is unrealistic.
Real-world datasets frequently contain incomplete annotations, where some protein functions may be missing.

Purpose of the Study:

To develop a computational method for protein function prediction that handles incomplete annotations.
To propose a weak-label learning approach to replenish missing protein functions.
To enhance prediction accuracy by incorporating negative constraints (known absent functions).

Main Methods:

Proposed Protein Function Prediction method with Weak-label Learning (ProWL).
Developed a variant, ProWL-IF, incorporating knowledge of absent functions.
Evaluated methods on protein-protein interaction networks and gene expression data.

Main Results:

Both ProWL and ProWL-IF demonstrated effectiveness in predicting protein functions with incomplete annotations.
ProWL-IF showed improved performance by utilizing negative functional constraints.
The methods successfully replenished missing functions for proteins.

Conclusions:

ProWL and ProWL-IF offer robust solutions for protein function prediction with weak labels.
Incorporating negative constraints significantly boosts prediction performance.
These methods advance automated protein function annotation in computational biology.