Prediction of the responsiveness to pharmacological chaperones: lysosomal human alpha-galactosidase, a case of study

Abstract

Background: The pharmacological chaperones therapy is a promising approach to cure genetic diseases. It relies on substrate competitors used at sub-inhibitory concentration which can be administered orally, reach difficult tissues and have low cost. Clinical trials are currently carried out for Fabry disease, a lysosomal storage disorder caused by inherited genetic mutations of alpha-galactosidase. Regrettably, not all genotypes respond to these drugs.

Results: We collected the experimental data available in literature on the enzymatic activity of ninety-six missense mutants of lysosomal alpha-galactosidase measured in the presence of pharmacological chaperones. We associated with each mutation seven features derived from the analysis of 3D-structure of the enzyme, two features associated with their thermo-dynamic stability and four features derived from sequence alone. Structural and thermodynamic analysis explains why some mutants of human lysosomal alpha-galactosidase cannot be rescued by pharmacological chaperones: approximately forty per cent of the non responsive cases examined can be correctly associated with a negative prognostic feature. They include mutations occurring in the active site pocket, mutations preventing disulphide bridge formation and severely destabilising mutations. Despite this finding, prediction of mutations responsive to pharmacological chaperones cannot be achieved with high accuracy relying on combinations of structure- and thermodynamic-derived features even with the aid of classical and state of the art statistical learning methods.We developed a procedure to predict responsive mutations with an accuracy as high as 87%: the method scores the mutations by using a suitable position-specific substitution matrix. Our approach is of general applicability since it does not require the knowledge of 3D-structure but relies only on the sequence.

Conclusions: Responsiveness to pharmacological chaperones depends on the structural/functional features of the disease-associated protein, whose complex interplay is best reflected on sequence conservation by evolutionary pressure. We propose a predictive method which can be applied to screen novel mutations of alpha galactosidase. The same approach can be extended on a genomic scale to find candidates for therapy with pharmacological chaperones among proteins with unknown tertiary structures.

Figure 1

The active site of human lysosomal alpha galactosidase. A monomer of human lysosomal alpha galactosidase is shown as a ribbon. The pocket with the highest proportion of conserved amino acids includes four groups of amino acids: D92, D93, C142, D170, R227, D231 (in red), completely conserved and associated with mutations not responding to DGJ; Y207 (in blue), not conserved and associated with mutations not responding to DGJ; E203, L206, S297 (in yellow) conserved and not associated with mutations tested with DGJ; W47, Y134, K168 (in green) not conserved and not associated with mutations tested with DGJ. R363, the furthest site from active pocket where responsive mutations, R363C and R363 H, have been observed is shown in purple.

Figure 2

Responsiveness to DGJ and solvent accessibility. Occurrences of responsive or non responsive mutations in accessible (blue bars) or non accessible (red bars) residues are reported as percentage: we used a cut-off of 5.0% as a threshold of side-chain solvent accessibility. Differences between percentage shown in blue and red bars are statistically significant (p = 0.03).

Figure 3

Responsiveness to DGJ and mutant stability. Mutants were divided into four equally populated bins (each including 25% mutations) of increasing predicted stability. For each bin a blue bar shows the percentage of responding mutations, a red bar the percentage of non responding ones. Panel A: bin 1 includes mutations with SDM scores ranging from -11.28 to -2.26; bin 2 from -2.26 to -0.86; bin 3 from -0.73 to 0.25; bin 4 from 0.25 to 3.65. Panel B: bin 1 includes mutations with MUPRO scores ranging from -2.46 to -1.25; bin 2 from -1.25 to -0.95; bin 3 from -0.95 to -0.60; bin 4 from -0.57 to 0.42. Bins with p = 0.01 or p = 0.04 are indicated with ** or * respectively. Box plots for the same data are shown in panel C for SDM and in panel D for MUPRO: the difference between the medians of SDM scores associated with responsive mutations and non responsive mutation is statistically significant (p = 0.03).