A synthetic protein-level neural network in mammalian cells
Abstract
Artificial neural networks provide a powerful paradigm for information processing that has transformed diverse fields. Within living cells, genetically encoded synthetic molecular networks could, in principle, harness principles of neural computation to classify molecular signals. Here, we combine de novo designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network computation. This "perceptein" circuit includes modules that compute weighted sums of input protein concentrations through reversible binding interactions, and allow for self-activation and mutual inhibition of protein components using irreversible proteolytic cleavage reactions. Altogether, these interactions comprise a network of 310 chemical reactions stemming from 8 expressed protein species. The complete system achieves signal classification with tunable decision boundaries in mammalian cells. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank L. Chong, X. J. Gao, and U. Alon for scientific input; J. Gregrowicz, R. Du, B. Emert, B. Gu, F. Horns, D. Li, A. Lu, K. Luo, Y. Takei, and S. Xia for critical feedback. This research was supported by the National Institute of Health grant R01 MH116508 and the Allen Discovery Center program under Award No. UWSC10142, a Paul G. Allen Frontiers Group advised program of the Paul G. Allen Family Foundation. M.B.E. is a Howard Hughes Medical Institute Investigator. Z.C. is supported by the Damon Runyon Cancer Research Foundation DRG-2388-20 and is a fellow of the Burroughs Wellcome Fund Career Awards at the Scientific Interface. Author contributions: Z.C. and M.B.E. conceived and designed the study; Z.C. performed mathematical modeling with help from R.Z. and M.B.E.; Z.C. and J.M.L. performed experiments; R.Z. constructed the reporter cell line; Z.C. and M.B.E. wrote the manuscript with input from all authors. Data and materials availability: Raw data and code used for simulation and data analysis can be downloaded from https://data.caltech.edu/records/20215. Competing Interest Statement. Z.C. and M.B.E. have filed a provisional patent application based on this work. This article is subject to HHMI's Open Access to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication.Attached Files
Submitted - 2022.07.10.499405v1.full.pdf
Supplemental Material - media-1.pdf
Files
Name | Size | Download all |
---|---|---|
md5:5ce84d99526ee94e8c50bb6d3c9b8944
|
2.2 MB | Preview Download |
md5:9d4a198d6abd331c515a7a5b9e64f64f
|
2.4 MB | Preview Download |
Additional details
- Eprint ID
- 115493
- Resolver ID
- CaltechAUTHORS:20220712-283791000
- NIH
- R01 MH116508
- Paul G. Allen Frontiers Group
- UWSC10142
- Howard Hughes Medical Institute (HHMI)
- Damon Runyon Cancer Research Foundation
- DRG-2388-20
- Burroughs Wellcome Fund
- Created
-
2022-07-13Created from EPrint's datestamp field
- Updated
-
2022-07-13Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering