A comprehensive computational biology approach to understanding and developing treatments for G6PD deficiency.
Running 100ns simulations to visualize how G6PD mutations affect protein stability and dynamics at the atomic level. Analyzing RMSD, RMSF, and hydrogen bond networks to understand variant pathogenicity.
Leveraging DeepMind's AlphaFold predictions to analyze G6PD protein structure, identify critical functional domains, and understand how mutations affect enzyme stability and NADP+ binding.
Comprehensive mapping of 217+ known G6PD variants with structural annotations, clinical severity classifications, geographic distributions, and predicted functional impacts.
Developing ML models to screen virtual compound libraries and identify potential pharmacological chaperones that could stabilize mutant G6PD enzymes. Using molecular fingerprints, docking scores, and ensemble methods.
Exploring gene editing strategies for correcting G6PD mutations. Analyzing guide RNA design, delivery methods, and the path from sickle cell disease success (Casgevy) to potential G6PD applications.
Understanding at the molecular level why certain drugs trigger hemolysis in G6PD-deficient patients. Analyzing oxidative stress pathways, drug metabolism, and creating evidence-based safety guidelines.
Monitoring ongoing and completed clinical trials related to G6PD deficiency worldwide. Tracking new therapeutic developments, diagnostic improvements, and safety studies.