Assessing Computational Predictions of the Phenotypic Effect of Cystathionine-beta-Synthase Variants.

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TitleAssessing Computational Predictions of the Phenotypic Effect of Cystathionine-beta-Synthase Variants.
Publication TypeJournal Article
Year of Publication2019
AuthorsKasak, L, Bakolitsa, C, Hu, Z, Yu, C, Rine, J, Dimster-Denk, DF, Pandey, G, De Baets, G, Bromberg, Y, Cao, C, Capriotti, E, Casadio, R, Van Durme, J, Giollo, M, Karchin, R, Katsonis, P, Leonardi, E, Lichtarge, O, Martelli, PLuigi, Masica, D, Mooney, SD, Olatubosun, A, Pal, LR, Radivojac, P, Rousseau, F, Savojardo, C, Schymkowitz, J, Thusberg, J, Tosatto, SCE, Vihinen, M, Väliaho, J, Repo, S, Moult, J, Brenner, SE, Friedberg, I
JournalHum Mutat
Date Published2019 Jul 13

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. This article is protected by copyright. All rights reserved.

Alternate JournalHum. Mutat.
PubMed ID31301157