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Enhancing protein structural properties through model-guided sequence optimization
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DOI：10.1016/j.ijbiomac.2025.147072.png)
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Abstract
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• Iterative ML-based approach improves protein optimization efficiency over traditional simulation-based methods. • ML models are combined with structural simulation to iteratively refine multi-objective protein sequence optimization. • Genetic algorithm with ML enables efficient search for mutations simultaneously optimizing stability and binding affinity. • AlphaFold-derived scores correlate with thermal stability, showing the potential of AI in mutation effect prediction. • Method extends beyond glutamine-binding protein as a general framework for optimizing biological systems.
Keywords:
Multi-objective protein optimization
Machine learning
Glutamine-binding protein
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