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Hum Mutat. 2019 Jul 8. doi: 10.1002/humu.23857. [Epub ahead of print]

Assessing predictions on fitness effects of missense variants in calmodulin.

Author information

1
Departments of Biophysics and Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, 75390-8816, USA.
2
Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, 75390-9050, USA.
3
Department of Molecular and Human Genetics, Department of Biochemistry & Molecular Biology, Department of Pharmacology, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA.
4
Biocomputing Group, FABIT/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Via F. Selmi 3, Bologna, 40126, Italy.
5
Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, 560 012, India.
6
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, M5G 1X5, Canada.
7
The Donnelly Centre and Departments, Toronto, Ontario, M5S 3E1, Canada.
8
Molecular Genetics, Toronto, Ontario, M5S 3E1, Canada.
9
Computer Science University of Toronto, Toronto, Ontario, M5S 3E1, Canada.

Abstract

This paper reports the evaluation of predictions for the "CALM1" challenge in the 5th round of the Critical Assessment of Genome Interpretation held in 2018. In the challenge, the participants were asked to predict effects on yeast growth caused by missense variants of human calmodulin, a highly conserved protein in eukaryotic cells sensing calcium concentration. The performance of predictors implementing different algorithms and methods is similar. Most predictors are able to identify the deleterious or benign variants with modest accuracy, with a baseline predictor based purely on sequence conservation slightly outperforming the submitted predictions. Nevertheless, we think that the accuracy of predictions remains far from satisfactory, and the field awaits substantial improvements. The most poorly predicted variants in this round surround functional CALM1 sites that bind calcium or peptide, which suggests that better incorporation of structural analysis may help improve predictions. This article is protected by copyright. All rights reserved.

KEYWORDS:

CAGI; calmodulin; disease; missense variants; predictors

PMID:
31283071
DOI:
10.1002/humu.23857

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