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

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.

PON-P: integrated predictor for pathogenicity of missense variants.

High-throughput sequencing data generation demands the development of methods for interpreting the effects of genomic variants. Numerous computational methods have been developed to assess the impact of variations because experimental methods are unable to cope with both the speed and volume of data generation. To harness the strength of currently available predictors, the Pathogenic-or-Not-Pipeline (PON-P) integrates five predictors to predict the probability that nonsynonymous variations affect protein function and may consequently be disease related. Random forest methodology-based PON-P shows consistently improved performance in cross-validation tests and on independent test sets, providing ternary classification and statistical reliability estimate of results. Applied to missense variants in a melanoma cancer cell line, PON-P predicts variants in 17 genes to affect protein function. Previous studies implicate nine of these genes in the pathogenesis of various forms of cancer. PON-P may thus be used as a first step in screening and prioritizing variants to determine deleterious ones for further experimentation.

Classification of mismatch repair gene missense variants with PON-MMR.

Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases.

Performance of mutation pathogenicity prediction methods on missense variants.

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.

Characterization of the first beta-class carbonic anhydrase from an arthropod (Drosophila melanogaster) and phylogenetic analysis of beta-class carbonic anhydrases in invertebrates.

BACKGROUND: The beta-carbonic anhydrase (CA, EC 4.2.1.1) enzymes have been reported in a variety of organisms, but their existence in animals has been unclear. The purpose of the present study was to perform extensive sequence analysis to show that the beta-CAs are present in invertebrates and to clone and characterize a member of this enzyme family from a representative model organism of the animal kingdom, e.g., Drosophila melanogaster. RESULTS: The novel beta-CA gene, here named DmBCA, was identified from FlyBase, and its orthologs were searched and reconstructed from sequence databases, confirming the presence of beta-CA sequences in 55 metazoan species. The corresponding recombinant enzyme was produced in Sf9 insect cells, purified, kinetically characterized, and its inhibition was investigated with a series of simple, inorganic anions. Holoenzyme molecular mass was defined by dynamic light scattering analysis and gel filtration, and the results suggested that the holoenzyme is a dimer. Double immunostaining confirmed predictions based on sequence analysis and localized DmBCA protein to mitochondria. The enzyme showed high CO2 hydratase activity, with a kcat of 9.5 x 105 s-1 and a kcat/KM of 1.1 x 108 M-1s-1. DmBCA was appreciably inhibited by the clinically-used sulfonamide acetazolamide, with an inhibition constant of 49 nM. It was moderately inhibited by halides, pseudohalides, hydrogen sulfide, bisulfite and sulfate (KI values of 0.67 - 1.36 mM) and more potently by sulfamide (KI of 0.15 mM). Bicarbonate, nitrate, nitrite and phenylarsonic/boronic acids were much weaker inhibitors (KIs of 26.9 - 43.7 mM). CONCLUSIONS: The Drosophila beta-CA represents a highly active mitochondrial enzyme that is a potential model enzyme for anti-parasitic drug development.