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Journal Article

Learning the shape of protein microenvironments with a holographic convolutional neural network

MPS-Authors
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Pun,  Michael N.
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons296144

Montague,  Zachary
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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LaMont,  Colin H.
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Otwinowski,  Jakub
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Nourmohammad,  Armita
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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pun-et-al-2024.pdf
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Citation

Pun, M. N., Ivanov, A., Bellamy, Q., Montague, Z., LaMont, C. H., Bradley, P., et al. (2024). Learning the shape of protein microenvironments with a holographic convolutional neural network. Proceedings of the National Academy of Sciences of the United States of America, 121(6): e2300838121. doi:10.1073/pnas.2300838121.


Cite as: https://hdl.handle.net/21.11116/0000-000E-6EB3-D
Abstract
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure–function maps could guide design of novel proteins with desired function.