Lysosomal storage diseases are due to inherited deficiencies in various
enzymes involved in basic metabolic processes. As with other
genetic diseases, accurate structure data for these enzymatic
proteins should help in better understanding the molecular effects of mutations identified in patients with the corresponding lysosomal diseases; however, no such three-dimensional (3D) structure data are available for many lysosomal
enzymes. Thus, we herein intend to illustrate for an audience of molecular geneticists how structure information can nonetheless be obtained via a bioinformatics approach in the case of five human lysosomal
glycoside hydrolases. Indeed, using the two-dimensional hydrophobic cluster analysis method to decipher the sequence information available in data banks for the large group of
glycoside hydrolases (clan GH-A) to which these human lysosomal
enzymes belong, we could deduce structure predictions for their catalytic domains and propose explanations for the molecular effects of mutations described in patients. In addition, in the case of human
beta-glucuronidase for which experimental 3D data have been reported, we also show here that bioinformatics methods relying on the available 3D structure information can be used to obtain further insights into the effects of various mutations described in patients with
Sly disease. In a broader perspective, our
work stresses that, in the context of a rapid increase in
protein sequence information through genome sequencing, bioinformatics approaches might be highly useful for generating structure-function predictions based on sequence-structure interrelationships.