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Learning the local landscape of protein structures with convolutional neural networks

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Abstract

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.

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Data availability

Analysis scripts and processed data are available on GitHub: https://github.com/akulikova64/CNN_protein_landscape. Trained neural networks and the training set protein chains and microenvironments have been deposited at the Texas Data Repository and are available at: https://doi.org/10.18738/T8/8HJEF9.

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Acknowledgements

We thank Raghav Shroff for writing our initial PDB to CIF converter.

Funding

This work was supported by grants from the Welch Foundation (F-1654), the Department of Defense – Defense Threat Reduction Agency (HDTRA12010011), and the National Institutes of Health (R01 AI148419). We would like to thank AMD for the donation of critical hardware and support resources from its HPC Fund that made this work possible. C.O.W. also acknowledges funding from the Jane and Roland Blumberg Centennial Professorship in Molecular Evolution and the Dwight W. and Blanche Faye Reeder Centennial Fellowship in Systematic and Evolutionary Biology at UT Austin.

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Authors and Affiliations

  1. Center for Systems and Synthetic Biology and Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA

    Anastasiya V. Kulikova

  2. Center for Systems and Synthetic Biology, Department of Chemistry, and Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA

    Daniel J. Diaz

  3. Center for Systems and Synthetic Biology and Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA

    James M. Loy & Andrew D. Ellington

  4. SparkCognition, Austin, TX, USA

    James M. Loy

  5. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA

    Claus O. Wilke

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  1. Anastasiya V. Kulikova
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  4. Andrew D. Ellington
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  5. Claus O. Wilke
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by A. V. Kulikova, D. J. Diaz, and J. M. Loy. The first draft of the manuscript was written by A. V. Kulikova and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Claus O. Wilke.

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Kulikova, A.V., Diaz, D.J., Loy, J.M. et al. Learning the local landscape of protein structures with convolutional neural networks. J Biol Phys 47, 435–454 (2021). https://doi.org/10.1007/s10867-021-09593-6

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