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Published October 23, 2017 | Supplemental Material + Published
Journal Article Open

Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization


There is growing interest in studying and engineering integral membrane proteins (MPs) that play key roles in sensing and regulating cellular response to diverse external signals. A MP must be expressed, correctly inserted and folded in a lipid bilayer, and trafficked to the proper cellular location in order to function. The sequence and structural determinants of these processes are complex and highly constrained. Here we describe a predictive, machine-learning approach that captures this complexity to facilitate successful MP engineering and design. Machine learning on carefully-chosen training sequences made by structure-guided SCHEMA recombination has enabled us to accurately predict the rare sequences in a diverse library of channelrhodopsins (ChRs) that express and localize to the plasma membrane of mammalian cells. These light-gated channel proteins of microbial origin are of interest for neuroscience applications, where expression and localization to the plasma membrane is a prerequisite for function. We trained Gaussian process (GP) classification and regression models with expression and localization data from 218 ChR chimeras chosen from a 118,098-variant library designed by SCHEMA recombination of three parent ChRs. We use these GP models to identify ChRs that express and localize well and show that our models can elucidate sequence and structure elements important for these processes. We also used the predictive models to convert a naturally occurring ChR incapable of mammalian localization into one that localizes well.

Additional Information

© 2017 Bedbrook et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: August 9, 2017; Accepted: September 21, 2017; Published: October 23, 2017. We thank Twist Bioscience for synthesizing and cloning ChR sequences as part of their α and β manufacturing programs. We thank the Gradinaru and Arnold labs for helpful discussions. We also thank Dr. John Bedbrook for critical reading of the manuscript. Imaging was performed in the Biological Imaging Facility, with the support of the Caltech Beckman Institute and the Arnold and Mabel Beckman Foundation. Data Availability: All relevant data are either within the paper and its Supporting Information files or published in ref 5. This work is funded by the National Institute for Mental Health R21MH103824 (VG and FHA) and the Institute for Collaborative Biotechnologies through grant number W911F-09-0001 from the U.S. Army Research Office (FHA). The content is solely the responsibility of the authors and does not necessarily reflect the position or policy of the National Center for Research Resources, the National Institutes of Health, or the Government, and no official endorsement should be inferred. VG is a Heritage Principal Investigator supported by the Heritage Medical Research Institute. CNB and AJR are funded by Ruth L. Kirschstein National Research Service Awards (F31MH102913 and F32GM116319, respectively). KKY is a trainee in the Caltech Biotechnology Leadership Program, and has received financial support from the Donna and Benjamin M. Rosen Bioengineering Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors declare no competing interests. Author Contributions: Conceptualization: Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice, Viviana Gradinaru, Frances H. Arnold. Formal analysis: Claire N. Bedbrook, Kevin K. Yang. Methodology: Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice. Project administration: Frances H. Arnold. Software: Claire N. Bedbrook, Kevin K. Yang. Supervision: Viviana Gradinaru, Frances H. Arnold. Visualization: Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice. Writing ± original draft: Claire N. Bedbrook, Kevin K. Yang. Writing ± review & editing: Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice, Viviana Gradinaru, Frances H. Arnold.

Attached Files

Published - journal.pcbi.1005786.pdf

Supplemental Material - journal.pcbi.1005786.s001.csv

Supplemental Material - journal.pcbi.1005786.s002.tif

Supplemental Material - journal.pcbi.1005786.s003.tif

Supplemental Material - journal.pcbi.1005786.s004.tif

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Supplemental Material - journal.pcbi.1005786.s011.tif

Supplemental Material - journal.pcbi.1005786.s012.tif

Supplemental Material - journal.pcbi.1005786.s013.tif


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August 19, 2023
October 23, 2023