Helix engineering: combining the power of 3DM with AI to disrupt protein engineering
The high dimensionality and practically infinite size of the sequence space requires effective techniques to explore, navigate and improve proteins.
Current techniques are underwhelming in their accuracy and ability to find novel variants. With its 3DM technology Bio-Prodict has long been at the forefront of providing protein engineering solutions. We automated 3DM-based search strategies to smartly select positions to mutate initially. We also developed a deep learning based ensemble architecture for the next rounds. Using multiple datasets we showed that this pipeline outperforms legacy machine learning methods on average by 71.5% when mutations were selected randomly and by 115% when the 3DM-based initial selection step was used. We have shown that with our technology, even as few as 50 initial mutations yield competitive hit rates.
Bio-Prodict B.V.:
- Stephan Heijl
- Jeanine Boot
- Bastiaan Brier
- Tom van den Bergh
- Bas Vroling
- Henk-Jan Joosten
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Helix Engineering: Combining the Power of 3DM with AI to Disrupt Protein Engineering
The high dimensionality and practically infinite size of the sequence space requires effective techniques to explore, navigate and improve proteins.
Current techniques are underwhelming in their accuracy and ability to find novel variants. With its 3DM technology Bio-Prodict has long been at the forefront of providing protein engineering solutions. Bio-Product automated 3DM-based search strategies to smartly select positions to mutate initially. They also developed a deep learning based ensemble architecture for the next rounds. Using multiple datasets we showed that this pipeline outperforms legacy machine learning methods on average by 71.5% when mutations were selected randomly and by 115% when the 3DM-based initial selection step was used. In his talk, Stephan demonstrated that with their technology, even as few as 50 initial mutations yield competitive hit rates.
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