ProteinBERT: a universal deep-learning model of protein sequence and function
Abstract Summary Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a...
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| Vydáno v: | Bioinformatics Ročník 38; číslo 8; s. 2102 - 2110 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Oxford University Press
12.04.2022
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| Témata: | |
| ISSN: | 1367-4803, 1367-4811, 1460-2059, 1367-4811 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Abstract
Summary
Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to long sequences. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data.
Availability and implementation
Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.
Supplementary information
Supplementary data are available at Bioinformatics online. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors wish it to be known that, in their opinion, the Nadav Brandes and Dan Ofer should be regarded as Joint First Authors. |
| ISSN: | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI: | 10.1093/bioinformatics/btac020 |