👥 By considering prediction stability, the selection pool of antibodies can be expanded.
💡 This method allows machine learning algorithms to identify diverse sequences unrelated to training data.
⚖️ The approach balances safety and success by finding a middle ground between prediction accuracy and uncertainty.
🧪 Scientists validated this approach by designing antibodies against galectin-3 and successfully expressing a desired sequence.
🔬 The researchers used their own multi-objective optimization software, but any off-the-shelf optimizer can be used.
🌐 This approach can be particularly useful in situations with limited data.
Introduction:
The article discusses the importance of including prediction stability as a parameter in machine learning algorithms for designing functional biological sequences, specifically antibodies. The researchers propose a whole-spectrum approach to improve black-box optimization by balancing activity and stability and considering prediction stability to identify promising sequences unrelated to training data.
- Adding prediction stability as a parameter in machine learning algorithms can improve the selection pool of antibodies.
- The whole-spectrum approach to prediction uncertainty is particularly useful in situations with limited data.
- The method balances the creation of high-scoring sequences with their likelihood of success, creating a more diverse pool of potential antibodies.
- The researchers designed VHH antibodies against galectin-3 and trained multiple prediction models to determine average activity and standard deviation.
- Their approach, which used a multi-objective optimization solver, resulted in the successful expression of a sequence that possessed the desired binding specificity.
Conclusion:
Including prediction stability as a parameter in machine learning algorithms for designing antibodies can improve the selection process by creating a more diverse pool of potential sequences. The whole-spectrum approach to prediction uncertainty balances the creation of high-scoring sequences with their likelihood of success. This approach has demonstrated success in designing functional biological sequences, especially in situations with limited data. Future research could explore the application of this approach in other areas of biological design.
