Opsin Engineering Advances Utilizing Automated Intracellular Electrophysiology and Language Models
Author(s)
Ehrlich, Samuel Michael
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Optogenetic tools, particularly channelrhodopsins (ChRs), enable precise control of neuronal activity with light, but designing opsins with desired properties—high photocurrent, fast kinetics, or specific spectral sensitivity—remains challenging due to the vast sequence space and complex sequence-function relationships. Traditional approaches are labor-intensive and intuition-driven, limiting discovery of novel variants. This thesis advances opsin engineering by developing experimental and computational strategies that link sequence to function and enable systematic exploration. An automated patch clamp platform was constructed that measures photocurrent amplitude, spectral response, and channel kinetics while collecting single cells for downstream sequencing. This integrated approach enables, for the first time, direct correlation of functional electrophysiological data with opsin genetic sequence on a single cell basis. This platform was used to analyze more than 100 cells and multiple populations of opsins. Protein language models were used as a tool to guide opsin design. Trained on evolutionary sequence datasets, these models provide zero-shot predictions of plausible amino acid substitutions, allowing prioritization of candidates from a vast mutational landscape. Using single-cell electrophysiology to validate predictions, we show that substitutions such as E300P and E300G in ChrimsonR enhance light sensitivity and sustained photocurrent amplitude by a magnitude. Critically, these mutations would be ignored in conventional protein engineering campaigns as they would be expected to render the opsin non-functional. Together, these studies establish a framework for data-driven opsin engineering. By utilizing automated functional measurements and machine learning–guided predictions, this thesis demonstrates a path for rational, high-throughput exploration of sequence-function relationships and the rapid design of next-generation optogenetic tools.
Sponsor
Date
2025-12
Extent
Resource Type
Text
Resource Subtype
Dissertation (PhD)