Machine Learning for Guided Fluorescent Protein Evolution
Machine Learning for Guided Fluorescent Protein Evolution
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Supervised Learning from Large Mutational Datasets
Supervised Learning from Large Mutational Datasets
Vectorized Sequence Data Can Transform Complex Amino Acid Composition into Useful and Predictive Inputs for Model Training that Enable In-Silico Design and Wet-Bench Workflows.
Deep Mutation of Sequences Reveals Sites for Adjusting Key Parameters
Predict Mutations for Brightness
Predict Mutations for Brightness
Predict Mutations for Excitation Maximum
Predict Mutations for Excitation Maximum
Predict Mutations for Emission Maximum
Predict Mutations for Emission Maximum
In-Silico Deep Mutational Scanning and Automated Data Mining Enable Accurate Residue-Level Predictions
Archive and Repeat
Implementing Greedy Search Enables Molecule Design Across Parameters
Implementing Greedy Search Enables Molecule Design Across Parameters
Assisting Directed Evolution Campaigns - It's Super Effective
Our platform supercharges your directed evolution campaigns by combining traditional experimental strategies with modern machine learning tools. Just like a strategic move in a classic game, our guided approach helps you identify the most promising mutations faster, reduce screening burdens, and achieve brighter, more functional fluorescent proteins. It’s not just effective — it’s super effective.
Let us help you level up your protein engineering game.
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