AI in Directed Evolution Case Studies
Machine Learning-Guided Directed Evolution for Protein Engineering and Machine Learning-Assisted Directed Protein Evolution with Combinatorial Libraries are the two applications of AI/ML in directed evolution.
1. Machine Learning-Guided Directed Evolution for Protein Engineering
- Machine learning-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. Machine learning methods use data to predict sequence maps to function without requiring a detailed model of the underlying physics or biological pathways.
- Machine learning methods accelerate directed evolution by learning from information contained in all measured variants and uses that information to select sequences that are likely to be improved.
- In 2019, Denovium and Maxygen entered into a partnership to apply AI in directed evolution for protein engineering. This partnership brings together Maxygen’s expertise in molecular breeding and directed evolution with Denovium’s state-of-art AI engine.
2. Machine Learning-Assisted Directed Protein Evolution with Combinatorial Libraries
- To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, machine learning is incorporated into the directed evolution workflow.
- Combinatorial sequence space can be quite expensive to sample experimentally but machine learning models trained on tested variants provide a fast method for testing sequence space computationally.
- Incorporating machine learning into directed evolution workflow helps to reduce associated expenses in performing experiments in combinatorial sequence space.
- By greatly increasing throughput within silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.
Despite a comprehensive search, we were unable to find case studies illustrating the application of AI in directed evolution. The following strategies were deployed to identify the required data:
First, we analyzed brands/companies that are using AI/ML to incorporate with Directed Evolution. We consulted several business intelligence websites, press releases, and medical publications such Nature.com, NCBI, Forbes, Synbiobeta, PRNewswire among others. We were only able to find about the partnership between Denovium and Maxygen that focuses on how Artificial intelligence is applied into Directed evolution for protein engineering. We tried to find a case study about the progress of this partnership but nothing relevant was found. We believe the partnership is fairly new and no case study is available yet.
Next, since only clinical trials and research studies are the only data available that illustrates the applications of AI into directed evolution, we performed an exhaustive search to identify clinical trials and research studies to find any companies/brands that might be sponsoring to the studies. However, we only found data on research institutes and the authors of the study/clinical trials.
Lastly, we looked for additional data about each identified authors of the clinical trials/research studies we found including Zachary Wua, S. B. Jennifer Kana, Russell D. Lewis, Bruce J. Wittmannb, and Frances H. Arnolda. We followed this approach to find the companies/brands that are funding their clinical trials/research studies. We scanned trusted media interviews by these authors on New York Times, Fair Observer, and others. Nevertheless, we only found a few details around the idea of incorporating AI and machine learning into Directed Evolution.