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Updated: May 13, 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

Deep learning tools predict variants in disordered regions with lower sensitivity.

Federica Luppino^1,2, Swantje Lenz^1,2, Chi Fung Willis Chow^1,2,3

¹Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307, Dresden, Germany.

|April 12, 2025

Summary

This summary is machine-generated.

Variant effect predictors show lower accuracy for mutations in intrinsically disordered regions (IDRs). New methods are needed to improve variant effect prediction in these crucial, yet unstructured, protein areas.

Keywords:

AlphaMissense Benchmarking Intrinsically disordered regions Methionine start site Variant effect predictors

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Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Structural Biology

Background:

AlphaFold2 advanced 3D protein modeling, aiding protein design and variant effect prediction.
Intrinsically disordered regions (IDRs) lack stable structures, posing challenges for accurate modeling and variant effect prediction.
Current Variant Effect Predictors (VEPs) like AlphaMissense show high performance but their efficacy on variants within IDRs is unknown.

Purpose of the Study:

To evaluate the performance of state-of-the-art VEPs on variants located in intrinsically disordered regions (IDRs).
To determine if current VEPs accurately predict variant effects in IDRs compared to ordered regions.

Main Methods:

Assessed the accuracy of VEPs, including AlphaMissense and VARITY, for variants in both ordered and disordered protein regions.
Analyzed prediction sensitivity and specificity differences between variants in IDRs and ordered regions.

Main Results:

Variant pathogenicity prediction is less accurate in IDRs than in ordered regions.
VEPs, particularly AlphaMissense and VARITY, exhibit lower sensitivity for mutations in IDRs.
The performance gap between sensitivity and specificity is widest for variants in disordered regions.

Conclusions:

Existing VEPs demonstrate reduced sensitivity and a distinct performance profile for variants in IDRs.
New IDR-specific features and predictive models are required for accurate classification of disease mutations in intrinsically disordered regions.