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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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Protein Networks

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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.
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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

A survey of computational intelligence techniques in protein function prediction.

Arvind Kumar Tiwari¹, Rajeev Srivastava¹

¹Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India.

International Journal of Proteomics

|January 10, 2015

Summary

Computational intelligence techniques enhance protein function prediction beyond traditional homology methods. Integrating diverse data and using ensemble classifiers significantly improves accuracy for various biological applications.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput technologies have expanded knowledge of unknown proteins.
Traditional homology-based protein function prediction methods have limitations with novel proteins.
Computational intelligence (CI) offers advanced solutions for protein function prediction.

Purpose of the Study:

To provide a comprehensive review of CI techniques for protein function prediction.
To explore the application of CI across diverse biological data types and prediction tasks.
To summarize research findings on improving prediction performance using CI.

Main Methods:

Review of computational intelligence techniques.
Analysis of protein function prediction using sequence, structure, and network data.
Integration of gene expression data for pathway analysis.

Main Results:

CI techniques show promise in overcoming limitations of homology-based methods.
Diverse applications include predicting DNA/RNA binding sites, subcellular localization, and enzyme functions.
Ensemble classifiers and multi-data integration enhance prediction accuracy.

Conclusions:

Computational intelligence is crucial for accurate protein function prediction.
Ensemble methods and heterogeneous data integration are key to improved performance.
This review highlights effective CI strategies for various bioinformatics challenges.