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Updated: Oct 15, 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

Functional Annotation from Structural Homology.

Brent W Segelke¹

¹Physical and Life Sciences, Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA. segelke1@llnl.gov.

Methods in Molecular Biology (Clifton, N.J.)

|October 31, 2021

Summary

This summary is machine-generated.

Predictive biology relies on understanding gene function. Structural similarity offers a powerful complement to sequence analysis for annotating gene products, advancing systems biology. This aids in understanding complex biological systems.

Keywords:

3D motif Gene annotation Molecular evolution Structural Superposition Structural homology Structure function

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Area of Science:

Computational Biology
Structural Biology
Genomics

Background:

Modern omics technologies provide detailed biological system data.
Systems biology aims for predictive modeling of molecular mechanisms.
A major challenge is the lack of functional annotation for many genes.

Purpose of the Study:

To explore the utility of structural similarity for inferring gene and protein function.
To complement traditional sequence-based homology methods with structure-based approaches.

Main Methods:

Utilized structural informatics to analyze quaternary structure, domain content, topology, and 3D motifs.
Developed and applied tools for inferring homology based on 3D structural similarity.
Leveraged emerging structure prediction methods.

Main Results:

Demonstrated the application of structural similarity for inferring homology and predicting molecular function.
Provided example cases showcasing the effectiveness of the described tools.
Highlighted the potential of structural informatics in genome annotation.

Conclusions:

Structural homology is a valuable approach to enhance genome annotation accuracy.
Integrating structural and sequence-based methods can improve functional predictions.
Accurate annotation of reference orthologs remains critical for advancing life science understanding.