High Throughput and Efficient Opsin Engineering by Directed Evolution and Language Modeling

Opsins are light-sensitive proteins that enable precise control of neuronal activity in optogenetic applications. However, the development of opsins with enhanced properties, such as greater photocurrent amplitude, optimized kinetics, and tailored spectral sensitivity, is hindered by the low throughput of traditional screening methods. This project presents a novel approach to overcome these limitations by integrating automated patch clamping and protein language modeling.

Aim 1 focuses on developing a high-throughput, semi-automated platform for screening the biophysical properties of opsins. The system will combine automated patch clamping with a cell-picking robot to enable efficient testing of opsin libraries. First, a robotic platform will be designed and built to patch and pick cells expressing optogenetic proteins (Subaim 1.1). This platform will then be tested using an experimental pipeline wherein cells are screened based on spectral response (Subaim 1.2). The final step will demonstrate the system’s ability to patch and pick a small library of primary neurons expressing mutants of the C1C2 opsin, with the goal of identifying non-functional variants (Subaim 1.3). By directly linking sequence variations to functional outcomes like channel kinetics and photocurrent amplitude, this system will streamline opsin screening.

Aim 2 utilizes Evolutionary Scale Modeling (ESM) to guide the design of new opsins based on natural sequence diversity. First, ESM will be used to predict evolutionarily favorable mutations in three different opsins (Subaim 2.1). These selected mutations will then be introduced into ChrimsonR, where their effects on critical optogenetic traits—such as photocurrent amplitude, kinetics, and spectral sensitivity—will be evaluated (Subaim 2.2). The goal is to validate the predictive power of protein language modeling by demonstrating that these computationally identified mutations lead to functionally enhanced opsins.

By combining high-throughput screening with evolutionarily guided design, this research aims to streamline and enhance the process of optogenetic protein engineering, ultimately advancing the capabilities of optogenetics for neuroscience and other fields.