Biotechnology
AI and Machine Learning for Protein Sequence–Structure–Function Modeling: A High-Throughput Research Case Study
- machine learning
- artificial intelligence
- AutoML
- protein engineering
- AlphaFold2
- sequence-structure-function
- molecular docking
- explainable AI
- SHAP
- biocatalysis
MLJAR tools were used in the following publication.
Guiding Discovery of Protein Sequence-Structure-Function Modeling
Hussain, Azam, Brooks III, Charles L.
University of Michigan, Macromolecular Science and Engineering Program, Ann Arbor, MI, USA | University of Michigan, Department of Chemistry, Ann Arbor, MI, USA | University of Michigan, Biophysics Program, Ann Arbor, MI, USA
This bioRxiv study presents a high-throughput AI pipeline for protein engineering that links sequence, structure, and function using AlphaFold2, GPU-accelerated docking, and machine learning models. The workflow predicts enzyme stereoselectivity and reactivity across a large ancestral protein library and then applies explainable AI (SHAP) to identify key residues in binding and second-shell regions. Using AutoML-style model selection with ensemble trees (CatBoost, XGBoost, Random Forest), the approach achieves strong agreement with experimental screens and recovers known functional switches. The work illustrates how artificial intelligence and automated modeling can accelerate biocatalyst discovery and rational enzyme design.
Bioinformatics • July 16, 2023
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