Proteins are nature’s most versatile molecular machines. Deep neural networks trained on large protein datasets have recently been used to tackle the unmet complexity of protein sequence–function relationships. The implicit knowledge contained in these networks represents a powerful, but thus far inaccessible, resource for understanding protein biology. Here, we show that occlusion-based sensitivity analysis can leverage the knowledge present in deep-neural-network-based protein sequence classifiers to identify functionally relevant parts of proteins. We first validated our approach by successfully predicting positions that mediate small molecule binding or catalytic activity across different protein classes. Next, we inferred the impact of point mutations on the activity of ERK and HRas, signalling factors frequently deregulated in cancer. Finally, we used our approach to identify engineering hotspots in CRISPR–Cas9 and anti-CRISPR protein AcrIIA4. Our work demonstrates how implicit knowledge in neural networks can be harnessed for protein functional dissection and protein engineering.
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Sensitivity analysis data for all presented proteins, including the ~800 proteins used to calculate spatial homogeneity of sphere variances, as well as weights for DeeProtein classifier, are available on Zenodo (https://doi.org/10.5281/zenodo.2577920 and https://doi.org/10.5281/zenodo.2574979). AcrIIA4–LOV2 expression vectors can be obtained from the corresponding authors on reasonable request.
The code for DeeProtein, including scripts employed for sensitivity analysis, and code for mapping sensitivities to protein 3D structures in PyMol, is available on GitHub under MIT License (https://github.com/juzb/DeeProtein, https://doi.org/10.5281/zenodo.2619339). A stand-alone compute capsule covering central functions of DeeProtein is available on Code Ocean (https://doi.org/10.24433/CO.1473214.v1)65.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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This work was funded by the Klaus Tschira foundation, the German Research Foundation (DFG) and the Federal Ministry of Education and Research. We thank J. Quittek and M. Niepert (both at NEC), T. Wollmann (IPMB, BioQuant and the German Cancer Research Center (DKFZ)) for helpful discussions and M. Hemberger (BioQuant) for support with IT and GPU cluster use. J.U.z.B., T.B., S.H., L.A., C.G., M.K., J.M., P.P., L.P., M.P., M.S., D.H., M.D.H., M.J., C.S., M.W., I.L., D.N. and R.E. represent the iGEM Team Heidelberg 2017.