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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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Updated: Jul 3, 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

Structure-based function prediction: approaches and applications.

Pier Federico Gherardini¹, Manuela Helmer-Citterich

¹Department of Biology, Centre for Molecular Bioinformatics, University of Tor Vergata, Rome, Italy. pier.federico.gherardini@uniroma2.it

Briefings in Functional Genomics & Proteomics

|July 5, 2008

Summary

This summary is machine-generated.

Predicting protein function from structure is crucial. This review covers comparative and physicochemical methods, discussing their real-world applications and limitations, including statistical significance and data dependency.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Structural biology
Bioinformatics
Computational biology

Background:

The rapid growth of protein structure data from structural genomics necessitates advanced function prediction tools.
Current structure-based function prediction methods face challenges in accuracy and data availability.

Purpose of the Study:

To review existing structure-based protein function prediction methods.
To discuss the applications and limitations of these prediction approaches.
To highlight key challenges in assessing statistical significance and data dependency.

Main Methods:

Comparative approaches: identifying structural motifs linked to known functions.
Physicochemical approaches: detecting functional surface patches based on protein characteristics.
Review of real-world case studies applying these methods.

Main Results:

Two primary categories of methods exist: comparative and physicochemical.
Both approaches have demonstrated utility in real-world applications.
Significant challenges remain in statistical validation and reliance on structural data.

Conclusions:

Structure-based function prediction is a rapidly evolving field with significant potential.
Accurate assessment of structural similarity and data quality are critical for reliable predictions.
Further development is needed to overcome current limitations and enhance predictive power.