Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants

Laura Kasak; Constantina Bakolitsa; Zhiqiang Hu; Changhua Yu; Jasper Rine; Dago F Dimster-Denk; Gaurav Pandey; Greet De Baets; Yana Bromberg; Chen Cao; Emidio Capriotti; Rita Casadio; Joost Van Durme; Manuel Giollo; Rachel Karchin; Panagiotis Katsonis; Emanuela Leonardi; Olivier Lichtarge; Pier Luigi Martelli; David Masica; Sean D Mooney; Ayodeji Olatubosun; Predrag Radivojac; Frederic Rousseau; Lipika R Pal; Castrense Savojardo; Joost Schymkowitz; Janita Thusberg; Silvio C E Tosatto; Mauno Vihinen; Jouni Väliaho; Susanna Repo; John Moult; Steven E Brenner; Iddo Friedberg

doi:10.1002/humu.23868

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Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants

Journal article

Open access

Peer reviewed

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants

Laura Kasak, Constantina Bakolitsa, Zhiqiang Hu, Changhua Yu, Jasper Rine, Dago F Dimster-Denk, Gaurav Pandey, Greet De Baets, Yana Bromberg, Chen Cao, …

Human mutation, Vol.40(9), pp.1530-1545

09/2019

DOI: https://doi.org/10.1002/humu.23868

PMID: 31301157

Abstract

Amino Acid Substitution

Computational Biology - methods

Cystathionine - metabolism

Cystathionine beta-Synthase - genetics

Cystathionine beta-Synthase - metabolism

Homocysteine - metabolism

Humans

Phenotype

Precision Medicine

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

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Title: Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants
Creators: Laura Kasak - Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
Constantina Bakolitsa - Department of Plant and Microbial Biology, University of California, Berkeley, California
Zhiqiang Hu - Department of Plant and Microbial Biology, University of California, Berkeley, California
Changhua Yu - Department of Plant and Microbial Biology, University of California, Berkeley, California
Jasper Rine - California Institute for Quantitative Biosciences, University of California, Berkeley, California
Dago F Dimster-Denk - California Institute for Quantitative Biosciences, University of California, Berkeley, California
Gaurav Pandey - Department of Plant and Microbial Biology, University of California, Berkeley, California
Greet De Baets - Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
Yana Bromberg - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Chen Cao - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
Emidio Capriotti - Department of Bioengineering, Stanford University, Stanford, California
Rita Casadio - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Joost Van Durme - VIB Switch Laboratory, Vrije Universiteit Brussel, Brussels, Belgium
Manuel Giollo - Department of Biomedical Sciences, University of Padua, Padua, Italy
Rachel Karchin - Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
Panagiotis Katsonis - Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
Emanuela Leonardi - Department for Woman and Child Health, University of Padua, Italy
Olivier Lichtarge - Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
Pier Luigi Martelli - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
David Masica - Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
Sean D Mooney - Computational Biology Group, Buck Institute for Research on Aging, Novato, California
Ayodeji Olatubosun - Institute of Medical Technology, University of Tampere, Tampere, Finland
Predrag Radivojac - Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
Frederic Rousseau - Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
Lipika R Pal - Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
Castrense Savojardo - Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
Joost Schymkowitz - Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
Janita Thusberg - Computational Biology Group, Buck Institute for Research on Aging, Novato, California
Silvio C E Tosatto - Department of Biomedical Sciences, University of Padua, Padua, Italy
Mauno Vihinen - Institute of Medical Technology, University of Tampere, Tampere, Finland
Jouni Väliaho - Institute of Medical Technology, University of Tampere, Tampere, Finland
Susanna Repo - Department of Plant and Microbial Biology, University of California, Berkeley, California
John Moult - Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
Steven E Brenner - Department of Plant and Microbial Biology, University of California, Berkeley, California
Iddo Friedberg - Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa
Publication Details: Human mutation, Vol.40(9), pp.1530-1545
Date published: 09/2019
Grant note: R01 LM009722 / NIH HHS The Flanders Institute for Science and Technology (IWT) (PhD grant to GDB) PIOF-GA-2009-237751 / Marie Curie International Outgoing Fellowship U01 GM115486 / NIH HHS R01 GM066099 / NIH HHS IUT34-12 / Estonian Research Council R01 GM071749 / NIH HHS GR-2011-02347754 / Italian Ministry of Health ABI 1458359 / National Science Foundation FFABR grant / The Ministry of Education, Universities and Research (MIUR) R01 GM114434 / NIH HHS 18-04 / Fondazione Istituto di Ricerca Pediatrica - Città della Speranza R01 GM120374 / NIH HHS R01 GM079656 / NIH HHS R13 HG006650 / NIH HHS The University of Leuven (KUL) G.0509.13 / The Funds for Scientific Research Flanders (FWO) U41 HG007346 / NIH HHS Flanders Institute for Biotechnology (VIB) P7/16 / The Interuniversity Attraction Pole (IAP) Network from the Science Policy of the Federal Government of Belgium (Belspo)
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031665384804646