SAAMP 2.0: An algorithm to predict genotype-phenotype correlation of lysosomal storage diseases

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Lysosomal storage diseases (LSDs) are genetic disorders caused by enzyme deficiencies.
• Accurate genotype-phenotype correlation is crucial for effective treatment of LSDs.
• Existing bioinformatics tools for predicting amino acid substitution impacts vary in performance.

Purpose of the Study:

• To optimize the SAAMP algorithm for predicting the functional impact of amino acid substitutions.
• To evaluate and refine bioinformatics tools for improved LSD genotype-phenotype correlation.
• To develop an updated guideline for phenotype prediction in monogenic diseases.

Main Methods:

• Evaluated 7 bioinformatics tools for predicting amino acid substitution impacts.
• Developed SAAMP 2.0 by selecting the best-performing tools (PolyPhen, PROVEAN).
• Utilized homology modeling for proteins lacking known structures and analyzed GLB1 gene mutations.

Main Results:

• PolyPhen and PROVEAN demonstrated the highest predictive performance.
• SAAMP 2.0 achieved 94% sensitivity and 90% specificity in predicting mutation impacts.
• Associated GLB1 mutations with specific LSD subtypes (GM1 gangliosidosis, Morquio B).

Conclusions:

• SAAMP 2.0 offers an optimized approach for predicting amino acid substitution effects.
• The updated guideline aids in early diagnosis and treatment allocation for LSDs.
• The methodology can be generalized to predict phenotypes in other monogenic diseases.