Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.

David Baker, Christina K Kim, Sarah J Wait, Michael Rappleye, Justin Daho Lee, Andre Berndt, Sophia Lin, Michael Regnier, Marc Expòsit, Samuel A Colby, Lily Torp, Anthony Asencio, Annette Smith, Farid Moussavi-Harami

Journal: Nature computational science 2024;4(3):224-236

PMID: 38532137

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Abstract

Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP, which achieve both faster kinetics and larger ∆F/F responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.

Address: Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA.; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.; Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA.; Institute for Protein Design, University of Washington, Seattle, WA, USA.; Center for Neuroscience, University of California, Davis, Davis, CA, USA.; Department of Neurology, University of California, Davis, Davis, CA, USA.; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.; Department of Bioengineering, University of Washington, Seattle, WA, USA.; Institute of Pharmacology and Toxicology, University of Zürich, Zurich, Switzerland.; Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA.; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.; Department of Bioengineering, University of Washington, Seattle, WA, USA.; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.; Division of Cardiology, University of Washington, Seattle, WA, USA.; Institute for Protein Design, University of Washington, Seattle, WA, USA.; Department of Biochemistry, University of Washington, Seattle, WA, USA.; Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.; Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA. [email protected].; Institute of Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA. [email protected].; Department of Bioengineering, University of Washington, Seattle, WA, USA. [email protected].; Center for Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, WA, USA. [email protected].

DOI: 10.1038/s43588-024-00611-w

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MeSH Terms: Calcium, Calcium Signaling, Coloring Agents, Indicators and Reagents, Machine Learning