RT Journal Article
SR Electronic
T1 Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.15.153643
DO 10.1101/2020.06.15.153643
A1 Nambiar, Ananthan
A1 Liu, Simon
A1 Hopkins, Mark
A1 Heflin, Maeve
A1 Maslov, Sergei
A1 Ritz, Anna
YR 2020
UL http://biorxiv.org/content/early/2020/06/16/2020.06.15.153643.abstract
AB The 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.Competing Interest StatementThe authors have declared no competing interest.