Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase

Abstract

Predicting the phenotypes of missense mutations uncovered by large-scale sequencing projects is an important goal in computational biology. High-confidence predictions can be an aid in focusing experimental and association studies on those mutations most likely to be associated with causative relationships between mutation and disease. As an aid in developing these methods further, we have derived a set of random mutations of the enzymatic domains of human cystathionine beta synthase. This enzyme is a dimeric protein that catalyzes the condensation of serine and homocysteine to produce cystathionine. Yeast missing this enzyme cannot grow on medium lacking a source of cysteine, while transfection of functional human CBS into yeast strains missing endogenous enzyme can successfully complement for the missing gene. We used PCR mutagenesis with error-prone Taq polymerase to produce 948 colonies and compared cell growth in the presence or absence of a cysteine source as a measure of CBS function. We were able to infer the phenotypes of 204 single-site mutants, 79 of them deleterious and 125 neutral. This set was used to test the accuracy of six publicly available prediction methods for phenotype prediction of missense mutations: SIFT, PolyPhen, PMut, SNPs3D, PhD-SNP, and nsSNPAnalyzer. The top methods are PolyPhen, SIFT, and nsSNPAnalyzer, which have similar performance. Using kernel discriminant functions, we found that the difference in position-specific scoring matrix values is more predictive than the wild-type PSSM score alone, and that the relative surface area in the biologically relevant complex is more predictive than that of the monomeric proteins.

Figure 1

Histogram of colony growth according to the maximum value of the first two experiments. a: Histogram of Cys−/Cys+ ratio of 948 colonies. b: Histogram of Cys−/Cys+ ratio of 357 colonies with no mutations. c: Histogram of Cys−/Cys+ ratio of 160 colonies with only S19P. d: Histogram of Cys−/Cys+ ratio of 330 colonies with one or more missense mutations.

Figure 2

Two accuracy parameters as a function of the threshold for phenotype determination. a: The overall accuracy. b: The Matthew’s correlation coefficient.

Figure 3

The kernel density estimations and the kernel discriminant functions for neutral and deleterious mutations of Lac repressor using the PSSM scores of the wildtype residue type.

Figure 4

Comparison of structure-based and evolutionary information in determining phenotype using kernel discriminant functions. a: PSSM values of CBS and Lac mutations. b: The difference between wild-type and mutant PSSM scores of CBS and Lac mutations. c: The surface area of the wild-type residue in the monomeric protein. d: The surface area of the wild-type residue in the biological unit.

Figure 5

The deleterious discriminant function for surface areas calculated from monomers and the relevant biological assemblies. a: CBS data. b: Lac data.