Protein structural features predict responsiveness to pharmacological chaperone treatment for three lysosomal storage disorders
| Autor: | Yang Zhang, Jaie Woodard, Wei Zheng |
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| Rok vydání: | 2021 |
| Předmět: |
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Molecular Protein Folding Decision Analysis Protein Conformation Mutant Lysosomal storage disorders Disease medicine.disease_cause Biochemistry Machine Learning Database and Informatics Methods Medical Conditions Medicine and Health Sciences Macromolecular Structure Analysis Missense mutation Biology (General) Precision Medicine Mutation Ecology Pharmaceutics Glycogen Storage Disease Type II Protein Stability Inherited Metabolic Disorders Small molecule Pharmacological chaperone Gaucher's disease Computational Theory and Mathematics Genetic Diseases Modeling and Simulation Engineering and Technology Protein folding Cellular Structures and Organelles Protein topology Management Engineering Research Article Protein Binding medicine.drug Protein Structure Computer and Information Sciences QH301-705.5 Mutation Missense Computational biology Biology Research and Analysis Methods Small Molecule Libraries Cellular and Molecular Neuroscience Drug Therapy Autosomal Recessive Diseases Artificial Intelligence Genetics medicine Humans Molecular Biology Ecology Evolution Behavior and Systematics Clinical Genetics Gaucher Disease Decision Trees Biology and Life Sciences Proteins Computational Biology Cell Biology medicine.disease Fabry disease Cellular kinetics Kinetics Biological Databases Metabolic Disorders Mutation Databases Fabry Disease Mutant Proteins Lysosomes Gaucher's Disease |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 17, Iss 9, p e1009370 (2021) |
| DOI: | 10.1101/2021.07.08.451652 |
| Popis: | Three-dimensional structures of proteins can provide important clues into the efficacy of personalized treatment. We perform a structural analysis of variants within three inherited lysosomal storage disorders, comparing variants responsive to pharmacological chaperone treatment to those unresponsive to such treatment. We find that predicted ΔΔG of mutation is higher on average for variants unresponsive to treatment, in the case of datasets for both Fabry disease and Pompe disease, in line with previous findings. Using both a single decision tree and an advanced machine learning approach based on the larger Fabry dataset, we correctly predict responsiveness of three Gaucher disease variants, and we provide predictions for untested variants. Many variants are predicted to be responsive to treatment, suggesting that drug-based treatments may be effective for a number of variants in Gaucher disease. In our analysis, we observe dependence on a topological feature reporting on contact arrangements which is likely connected to the order of folding of protein residues, and we provide a potential justification for this observation based on steady-state cellular kinetics. Author summary Pharmacological chaperones are small molecule drugs that bind to proteins to help stabilize the folded state. One set of diseases for which this treatment has been effective is the lysosomal storage disorders, which are caused by defective lysosomal enzymes. However, not all genotypes are equally responsive to treatment. For instance, missense mutants that are particularly destabilized relative to WT are less likely to respond. The availability of datasets containing responsiveness data for large numbers of mutants, along with crystal structures of the protein involved in each disease, make machine learning methods incorporating sequence-based and structural data feasible. We hypothesize that data from two diseases, Fabry and Pompe disease, may be useful for predicting responsiveness of variants in the related Gaucher disease. Results suggest that many rare variants in Gaucher disease could be amenable to existing drugs. Results also suggest that drug responsiveness depends on protein topology in such a way that mutations in early-to-fold residues are more likely to be non-responsive to pharmacological chaperone treatment, which is consistent with a simple kinetic model of stability rescue. This study provides an example of how machine learning can be used to inform further studies towards personalized treatment in medicine. |
| Databáze: | OpenAIRE |
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