Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Nambiar, A.; Heflin, M. E.; Liu, S.; Maslov, S.; Hopkins, M.; Ritz, A.

doi:10.1101/2020.06.15.153643

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bioRxiv · 10.1101/2020.06.15.153643

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

Nambiar, A.·Heflin, M. E.·Liu, S.·Maslov, S.·Hopkins, M.·Ritz, A.

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Abstract

AO_SCPLOWBSTRACTC_SCPLOWThe scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.

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Nambiar, A., Heflin, M. E., Liu, S., Maslov, S., Hopkins, M., Ritz, A.. 2020-06-16. Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks. https://doi.org/10.1101/2020.06.15.153643

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