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Related Experiment Video

Updated: Jul 20, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis.

Francisco J Guzmán-Vega^1,2, Ana C González-Álvarez^3,2, Karla A Peña-Guerra^3,2

¹Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.

Current Protocols

|August 4, 2023

Summary

Investigate the molecular impact of gene variants using 3D protein structures. This approach, enhanced by artificial intelligence, offers a fast, accessible alternative to costly experiments for understanding traits and diseases.

Keywords:

AlphaFold gene variants protein structure structural analysis for non-experts variant analysis

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Published on: December 9, 2015

Area of Science:

Genetics and Bioinformatics
Structural Biology
Computational Biology

Background:

Understanding gene variant effects on protein function is crucial for explaining organismal traits and diseases.
Current bioinformatics tools often lack molecular insights, providing only pathogenicity scores.
Experimental methods for determining molecular effects are slow, expensive, and require specialized resources.

Purpose of the Study:

To provide non-experts with protocols for investigating the molecular effects of genetic variants using online tools and databases.
To enable rapid, cost-free molecular assessment of gene variants through 3D protein structure mapping.
To leverage recent advances in artificial intelligence for predicting protein structures from sequence data.

Main Methods:

Utilizing online resources such as AlphaFold, Protein Structure Database, and UniProt for basic 3D mapping.
Employing alternate protocols involving the Protein Data Bank, SWISS-MODEL, ColabFold, and PyMOL for structure-based variant analysis.
Focusing on accessible, non-expert-friendly methodologies for structure-based variant interpretation.

Main Results:

Demonstrated the feasibility of mapping gene variants onto predicted or experimentally determined 3D protein structures.
Showcased the utility of freely available online tools for molecular-level variant assessment.
Highlighted the impact of AI advancements in protein structure prediction on variant analysis.

Conclusions:

3D protein structure mapping offers a fast, cost-effective method for assessing the molecular impact of gene variants.
The presented protocols empower non-experts to perform structure-based variant analysis, aiding in understanding disease mechanisms and personalizing care.
Advances in AI-driven protein structure prediction have significantly improved the accessibility and accuracy of this analytical approach.