4 years ago

Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges

Missense variant pathogenicity predictors generalize well across a range of function-specific prediction challenges
Predrag Radivojac, Sean D. Mooney, Vikas Pejaver
The steady advances in machine learning and accumulation of biomedical data have contributed to the development of numerous computational models that assess the impact of missense variants. Different methods, however, operationalize impact differently. Two common tasks in this context are the prediction of the pathogenicity of variants and the prediction of their effects on a protein's function. These are related but distinct problems, and it is unclear whether methods developed for one are optimized for the other. The Critical Assessment of Genome Interpretation (CAGI) experiment provides a means to address this question empirically. To this end, we participated in various protein-specific challenges in CAGI with two objectives in mind. First, to compare the performance of methods in the MutPred family with the state-of-the-art. Second and more importantly, to investigate the applicability of general-purpose pathogenicity predictors to the classification of specific function-altering variants without additional training or calibration. We find that our pathogenicity predictors performed competitively with other methods, outputting score distributions in agreement with experimental outcomes. Overall, we conclude that binary classifiers learned from disease-causing mutations are capable of modeling important aspects of the underlying biology and the alteration of protein function resulting from mutations. By participating in the Critical Assessment of Genome Interpretation, we demonstrate the direct applicability of missense variant pathogenicity predictors in the task of the prediction of real-valued impact on biochemical, molecular and cellular function, as measured in in vitro experiments. Our work suggests that when a large number of structural and functional features are integrated into a learning algorithm that outputs smooth score distributions, pathogenicity predictors can model the biology shared by both of these prediction tasks.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/humu.23258

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