Toward Advanced Quantitative Prediction of Missense Variant Effects
- 1Research Unit in Clinical and Translational Bioinformatics, Vall d’Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
- 2Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
Abstract
Predicting how missense variants alter protein function is central to resolving the genotype-to-phenotype equation, a task now addressed by numerous computational predictors. The most accurate are increasingly opaque, however, limiting both their mechanistic value and their adoption in the clinic. Here we present QAFIsplit, a redesign of the Quantitative Assessment of Functional Impact framework that models variant effect in two interpretable stages: the mutation tolerance of a position, and the deviation introduced by a specific substitution. Framed as the prediction of a molecular endophenotype, QAFIsplit reaches state-of-the-art quantitative accuracy on independent deep mutational scanning benchmarks, surpassing black-box predictors such as AlphaMissense in most protein domains. Carried into clinical variant classification, it performs on par with established clinical tools without ever being trained on clinical labels, and complements meta-predictors without penalty. In the CAGI 7 community experiment, QAFI-based strategies were submitted blindly to four independent disease-gene challenges, backed by strong pre-submission clinical validation (AUC up to 0.99).
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BibTeX
@article{ye2026toward,
title = {{Toward Advanced Quantitative Prediction of Missense Variant Effects}},
author = {Shaopei Ye and Selen Özkan and Aitana Díaz-Vásquez and Natàlia Padilla and Xavier de la Cruz},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = sep,
doi = {10.3390/ijms27198777},
url = {https://www.mdpi.com/1422-0067/27/19/8777},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Toward Advanced Quantitative Prediction of Missense Variant Effects
AU - Shaopei Ye
AU - Selen Özkan
AU - Aitana Díaz-Vásquez
AU - Natàlia Padilla
AU - Xavier de la Cruz
PY - 2026
DA - 2026/09/30
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27198777
UR - https://www.mdpi.com/1422-0067/27/19/8777
AB - Predicting how missense variants alter protein function is central to resolving the genotype-to-phenotype equation, a task now addressed by numerous computational predictors. The most accurate are increasingly opaque, however, limiting both their mechanistic value and their adoption in the clinic. Here we present QAFIsplit, a redesign of the Quantitative Assessment of Functional Impact framework that models variant effect in two interpretable stages: the mutation tolerance of a position, and the deviation introduced by a specific substitution. Framed as the prediction of a molecular endophenotype, QAFIsplit reaches state-of-the-art quantitative accuracy on independent deep mutational scanning benchmarks, surpassing black-box predictors such as AlphaMissense in most protein domains. Carried into clinical variant classification, it performs on par with established clinical tools without ever being trained on clinical labels, and complements meta-predictors without penalty. In the CAGI 7 community experiment, QAFI-based strategies were submitted blindly to four independent disease-gene challenges, backed by strong pre-submission clinical validation (AUC up to 0.99).
SN - 1422-0067
ER -