Any two individuals differ from each other by an average of 3 million single-nucleotide polymorphisms. Some polymorphisms have a functional impact on cofactor-using enzymes and therefore represent points of possible therapeutic intervention through elevated-cofactor remediation. Because most known disease-causing mutations affect protein stability, we evaluated how the in vivo impact caused by single amino acid substitutions in a prototypical enzyme of this type compared with physical characteristics of the variant enzymes in vitro. We focused on cystathionine β-synthase (CBS) because of its clinical relevance in homocysteine metabolism and because some variants of the enzyme are clinically responsive to increased levels of its B6 cofactor. Single amino-acid substitutions throughout the CBS protein caused reduced function in vivo, and a subset of these altered sensitivity to limiting B6-cofactor. Some of these B6-sensitive substitutions also had altered sensitivity to limiting heme, another CBS cofactor. Limiting heme resulted in reduced incorporation of heme into these variants, and subsequently increased protease sensitivity of the enzyme in vitro. We hypothesize that these alleles caused a modest, yet significant, destabilization of the native state of the protein, and that the functional impact of the amino acid substitutions caused by these alleles can be influenced by cofactor(s) even when the affected amino acid is distant from the cofactor binding site.

Any two individuals differ from each other by an average of 3 million single-nucleotide polymorphisms. Some polymorphisms have a functional impact on cofactor-using enzymes and therefore represent points of possible therapeutic intervention through elevated-cofactor remediation. Because most known disease-causing mutations affect protein stability, we evaluated how the in vivo impact caused by single amino acid substitutions in a prototypical enzyme of this type compared with physical characteristics of the variant enzymes in vitro. We focused on cystathionine β-synthase (CBS) because of its clinical relevance in homocysteine metabolism and because some variants of the enzyme are clinically responsive to increased levels of its B6 cofactor. Single amino-acid substitutions throughout the CBS protein caused reduced function in vivo, and a subset of these altered sensitivity to limiting B6-cofactor. Some of these B6-sensitive substitutions also had altered sensitivity to limiting heme, another CBS cofactor. Limiting heme resulted in reduced incorporation of heme into these variants, and subsequently increased protease sensitivity of the enzyme in vitro. We hypothesize that these alleles caused a modest, yet significant, destabilization of the native state of the protein, and that the functional impact of the amino acid substitutions caused by these alleles can be influenced by cofactor(s) even when the affected amino acid is distant from the cofactor binding site.

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.

Background

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

Results

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

Conclusions

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.