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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...

Conservation of Protein Domains

Conservation of Protein Domains

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Structural Protein Function

Structural Protein Function

Mechanical Protein Functions

Mechanical Protein Functions

Proteins perform many mechanical functions in a cell. These proteins can be classified into two general categories- proteins that generate mechanical forces and proteins that are subjected to mechanical forces. Proteins providing mechanical support to the structure of the cell, such as keratin, are subjected to mechanical force, whereas proteins involved in cell movement and transport of molecules across cell membranes, such as an ion pump, are examples of generating mechanical force.

Mechanical Protein Function

Mechanical Protein Function

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Related Experiment Video

Updated: Oct 6, 2025

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

Assigning protein function from domain-function associations using DomFun.

Elena Rojano^1,2, Fernando M Jabato^1,2, James R Perkins^3,4,5

¹Department of Molecular Biology and Biochemistry, University of Malaga, Bulevar Louis Pasteur, 31, 29010, Malaga, Spain.

BMC Bioinformatics

|January 16, 2022

Summary

This summary is machine-generated.

DomFun predicts protein function using domain associations. It achieves high accuracy using the Simpson index and Stouffer's method with FunFams, outperforming other methods in benchmarks.

Keywords:

CAFA CATH DomFun Function prediction Protein domains

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Identification of Functional Protein Regions Through Chimeric Protein Construction

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Identification of Functional Protein Regions Through Chimeric Protein Construction

Identification of Functional Protein Regions Through Chimeric Protein Construction

Published on: January 8, 2019

Area of Science:

Bioinformatics
Computational Biology

Background:

Protein function prediction is a significant challenge in bioinformatics.
Protein domain composition is a key determinant of protein function.
Existing methods for predicting protein function often struggle with accuracy and scope.

Purpose of the Study:

To introduce DomFun, a novel Ruby gem for protein function prediction.
To leverage tripartite network analysis for calculating domain-function associations.
To combine these associations at the protein level for improved prediction accuracy.

Main Methods:

Analyzed 16 tripartite networks linking CATH-Gene3D domains (superfamilies and FunFams) with functional annotations (Gene Ontology, KEGG, Reactome).
Calculated domain-function associations using multiple indices, including the Simpson index.
Combined associations using Stouffer's method and validated predictions using the CAFA 3 benchmark and a custom Pathway Prediction Performance (PPP) procedure.

Main Results:

The combination of Simpson index for FunFam domain-function associations and Stouffer's method demonstrated superior performance across various evaluation scenarios.
Using FunFams outperformed superfamilies, and predictions for Gene Ontology molecular function were more accurate than for biological process terms.
DomFun achieved performance comparable to top methods in CAFA 3 evaluations and showed good accuracy for KEGG and Reactome annotations via the PPP benchmark.

Conclusions:

DomFun offers competitive protein function prediction, particularly for Gene Ontology terms, with performance varying by evaluation procedure.
The tool accurately predicts functions for KEGG and Reactome annotations, as validated by the PPP benchmark.
Optimal performance is achieved using FunFams and combining domain-function associations via the Simpson index and Stouffer's method. The tool is extensible for incorporating additional protein features.