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Updated: Jan 5, 2026

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

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VarSite: Disease variants and protein structure.

Roman A Laskowski¹, James D Stephenson^1,2, Ian Sillitoe³

¹European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Protein Science : a Publication of the Protein Society

|October 14, 2019

Summary

VarSite maps disease and natural genetic variants onto 3D protein structures. This tool aids in assessing variant pathogenicity by visualizing structural impacts and functional effects.

Keywords:

3D protein structure CATH ClinVar PDB Pfam UniProt VarMap VarSite disease variants gnomAD molecular interactions natural variants schematic diagrams

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Genomics
Structural Biology
Bioinformatics

Background:

Genetic variants, both disease-associated and natural, play crucial roles in human health and evolution.
Understanding the structural and functional impact of these variants is essential for disease diagnosis and drug development.
Existing databases often lack direct integration with 3D protein structures, hindering detailed variant analysis.

Purpose of the Study:

To develop VarSite, a web server for mapping genetic variants onto 3D protein structures.
To provide an image-based analysis of variant locations and potential functional consequences.
To facilitate the assessment of variant pathogenicity by integrating data from UniProt, ClinVar, and gnomAD.

Main Methods:

Integration of disease-associated variants (UniProt, ClinVar) and natural variants (gnomAD) with Protein Data Bank (PDB) structures.
Image-based analyses providing protein-level overviews and specific variant reports.
Visualization of variant 3D locations using 3dmol.js, RasMol, and PyMOL.
Analysis of structural annotations including secondary structure, ligand/metal/nucleic acid/protein interactions, and predicted functional impact.
Agglomerative analyses mapping variants onto protein domains (Pfam, CATH).

Main Results:

VarSite successfully maps diverse genetic variants onto corresponding protein 3D structures.
The server provides detailed structural context for variants, aiding in pathogenicity assessment.
Interactive visualization tools allow users to explore variant locations and their structural environment.
Analysis reveals patterns of variant distribution within protein domains.

Conclusions:

VarSite offers a valuable resource for researchers studying the structural basis of genetic variation.
The platform enhances the interpretation of variant effects by integrating structural and functional information.
VarSite facilitates the distinction between pathogenic and benign variants through detailed structural analysis.