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Computation-guided optimization of split protein systems


Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. This new technology will streamline the generation and use of split protein systems for diverse applications.

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Fig. 1: Design-driven strategy for tuning split protein systems.
Fig. 2: Computation-guided method development and experimental analysis.
Fig. 3: Evaluation of model-predicted phenotypes for combined mutations.
Fig. 4: Evaluation of model-predicted phenotypes for novel mutations and combinations.
Fig. 5: Model-guided design of a new split TEVp application in a soluble context.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request. Raw experimental data and computation generated data for main text figures are provided as Source data. Raw experimental data for Supplementary figures are provided in Supplementary Data 1. Plasmid maps are provided in Supplementary Data 2, and annotated descriptions of all plasmids are in Supplementary Data 3. The structure of TEVp was obtained from the Research Crystallography for Structural Bioinformatics (RCSB) (PDB: 1LVM). A subset of plasmids used in this study will be made available on Addgene, including complete and annotated GenBank files, at Source data are provided with this paper.

Code availability

Rosetta details and script are provided in Supplementary Note 1. Linear discriminate analysis details and script are provided in Supplementary Software 1. The Jupyter notebook code used to make Fig. 4a is provided in Supplementary Software 2.


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This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering of the NIH under award no. 1R01EB026510 (J.N.L.) and the Northwestern University Flow Cytometry Core Facility supported by a Cancer Center Support Grant (NCI 5P30CA060553). T.B.D was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG). J.D.B. and A.N.P. were supported by the National Science Foundation through Graduate Research Fellowships. J.D.B. and W.K.C. were supported in part by the National Institutes of Health Training Grant (T32GM008449) through Northwestern University’s Biotechnology Training Program. This work is also supported in part by the Great Lakes Bioenergy Research Center, US Department of Energy, Office of Science, Office of Biological and Environmental Research, under award no. DE-SC0018409 (S.R. and A.T.M.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, Department of Defense, Department of Energy or other federal agencies.

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Authors and Affiliations



T.B.D., A.T.M., S.R. and J.N.L. conceptualized the project. T.B.D., J.D.B., W.K.C. and E.E.S. created reagents, designed and performed experiments, and analyzed the data. A.N.P. assisted in analyzing and visualizing the data. A.T.M. developed the computational model and code. T.B.D., A.T.M., S.R. and J.N.L. drafted the manuscript. T.B.D., A.T.M. and A.N.P. created the figures. J.N.L. and S.R. supervised the work. All authors edited and approved the final manuscript.

Corresponding authors

Correspondence to Srivatsan Raman or Joshua N. Leonard.

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Competing interests

J.N.L. is a co-inventor on a patent that covers the MESA technology used in this manuscript (US patent 9,732,392 B2). The other authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14, Tables 1–4, Note and references.

Reporting Summary

Supplementary Data 1

Raw data for Supplementary figures.

Supplementary Software 1

Linear discriminate analysis script. Note that this is a text file with a file extension indicating that it is for use with R.

Supplementary Software 2

Jupyter notebook code for Fig. 4a. Note that this is a text file with a file extension indicating that it is for use with Jupyter.

Supplementary Data 2

Archive of plasmid maps.

Supplementary Data 3

List of all plasmids and annotation of key features.

Source data

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Dolberg, T.B., Meger, A.T., Boucher, J.D. et al. Computation-guided optimization of split protein systems. Nat Chem Biol 17, 531–539 (2021).

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