Literature DB >> 31283071

Assessing predictions on fitness effects of missense variants in calmodulin.

Jing Zhang1, Lisa N Kinch2, Qian Cong1, Panagiotis Katsonis3, Olivier Lichtarge3,4, Castrense Savojardo5, Giulia Babbi5, Pier Luigi Martelli5, Emidio Capriotti5, Rita Casadio5, Aditi Garg6, Debnath Pal6, Jochen Weile7,8,9, Song Sun7,8,9, Marta Verby7,8,9, Frederick P Roth7,8,9,10, Nick V Grishin1,2.   

Abstract

This paper reports the evaluation of predictions for the "CALM1" challenge in the fifth 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 tolerated 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.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  CAGI; calmodulin; disease; missense variants; predictors

Mesh:

Substances:

Year:  2019        PMID: 31283071      PMCID: PMC6744288          DOI: 10.1002/humu.23857

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.700


  31 in total

1.  dbSNP: the NCBI database of genetic variation.

Authors:  S T Sherry; M H Ward; M Kholodov; J Baker; L Phan; E M Smigielski; K Sirotkin
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  INPS-MD: a web server to predict stability of protein variants from sequence and structure.

Authors:  Castrense Savojardo; Piero Fariselli; Pier Luigi Martelli; Rita Casadio
Journal:  Bioinformatics       Date:  2016-04-10       Impact factor: 6.937

Review 3.  Calmodulin in action: diversity in target recognition and activation mechanisms.

Authors:  Klaus P Hoeflich; Mitsuhiko Ikura
Journal:  Cell       Date:  2002-03-22       Impact factor: 41.582

4.  Mutations in calmodulin cause ventricular tachycardia and sudden cardiac death.

Authors:  Mette Nyegaard; Michael T Overgaard; Mads T Søndergaard; Marta Vranas; Elijah R Behr; Lasse L Hildebrandt; Jacob Lund; Paula L Hedley; A John Camm; Göran Wettrell; Inger Fosdal; Michael Christiansen; Anders D Børglum
Journal:  Am J Hum Genet       Date:  2012-10-05       Impact factor: 11.025

Review 5.  Calmodulin: an introduction.

Authors:  F C Stevens
Journal:  Can J Biochem Cell Biol       Date:  1983-08

Review 6.  Calmodulin in a heartbeat.

Authors:  Anders B Sorensen; Mads T Søndergaard; Michael T Overgaard
Journal:  FEBS J       Date:  2013-06-18       Impact factor: 5.542

7.  OrthoDB v8: update of the hierarchical catalog of orthologs and the underlying free software.

Authors:  Evgenia V Kriventseva; Fredrik Tegenfeldt; Tom J Petty; Robert M Waterhouse; Felipe A Simão; Igor A Pozdnyakov; Panagiotis Ioannidis; Evgeny M Zdobnov
Journal:  Nucleic Acids Res       Date:  2014-11-26       Impact factor: 16.971

8.  The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity.

Authors:  Dominik G Grimm; Chloé-Agathe Azencott; Fabian Aicheler; Udo Gieraths; Daniel G MacArthur; Kaitlin E Samocha; David N Cooper; Peter D Stenson; Mark J Daly; Jordan W Smoller; Laramie E Duncan; Karsten M Borgwardt
Journal:  Hum Mutat       Date:  2015-03-26       Impact factor: 4.878

9.  A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness.

Authors:  Panagiotis Katsonis; Olivier Lichtarge
Journal:  Genome Res       Date:  2014-09-12       Impact factor: 9.043

10.  A framework for exhaustively mapping functional missense variants.

Authors:  Jochen Weile; Song Sun; Atina G Cote; Jennifer Knapp; Marta Verby; Joseph C Mellor; Yingzhou Wu; Carles Pons; Cassandra Wong; Natascha van Lieshout; Fan Yang; Murat Tasan; Guihong Tan; Shan Yang; Douglas M Fowler; Robert Nussbaum; Jesse D Bloom; Marc Vidal; David E Hill; Patrick Aloy; Frederick P Roth
Journal:  Mol Syst Biol       Date:  2017-12-21       Impact factor: 11.429

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  5 in total

Review 1.  Interpreting protein variant effects with computational predictors and deep mutational scanning.

Authors:  Benjamin J Livesey; Joseph A Marsh
Journal:  Dis Model Mech       Date:  2022-06-23       Impact factor: 5.732

2.  CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.

Authors:  Panagiotis Katsonis; Olivier Lichtarge
Journal:  Hum Mutat       Date:  2019-08-07       Impact factor: 4.878

Review 3.  Genome interpretation using in silico predictors of variant impact.

Authors:  Panagiotis Katsonis; Kevin Wilhelm; Amanda Williams; Olivier Lichtarge
Journal:  Hum Genet       Date:  2022-04-30       Impact factor: 5.881

4.  Circuit topology predicts pathogenicity of missense mutations.

Authors:  Jaie Woodard; Sumaiya Iqbal; Alireza Mashaghi
Journal:  Proteins       Date:  2022-04-23

5.  Mutation severity spectrum of rare alleles in the human genome is predictive of disease type.

Authors:  Jimin Pei; Lisa N Kinch; Zbyszek Otwinowski; Nick V Grishin
Journal:  PLoS Comput Biol       Date:  2020-05-15       Impact factor: 4.475

  5 in total

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