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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| Veröffentlicht in: | Bioinformatics Jg. 38; H. 8; S. 2102 - 2110 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
Oxford University Press
12.04.2022
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| Schlagworte: | |
| ISSN: | 1367-4803, 1367-4811, 1460-2059, 1367-4811 |
| Online-Zugang: | Volltext |
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| Abstract | 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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| AbstractList | 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. 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.SUMMARYSelf-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.Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.AVAILABILITY AND IMPLEMENTATIONCode and pretrained model weights are available at https://github.com/nadavbra/protein_bert.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. 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. Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert. Supplementary data are available at Bioinformatics online. |
| Author | Linial, Michal Brandes, Nadav Rappoport, Nadav Ofer, Dan Peleg, Yam |
| AuthorAffiliation | 4 Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev , Beer Sheva 8410501, Israel 1 School of Computer Science and Engineering, The Hebrew University of Jerusalem , Jerusalem 9190401, Israel 3 Deep Trading Ltd. , Haifa 3508401, Israel 2 Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel |
| AuthorAffiliation_xml | – name: 3 Deep Trading Ltd. , Haifa 3508401, Israel – name: 4 Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev , Beer Sheva 8410501, Israel – name: 1 School of Computer Science and Engineering, The Hebrew University of Jerusalem , Jerusalem 9190401, Israel – name: 2 Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel |
| Author_xml | – sequence: 1 givenname: Nadav orcidid: 0000-0002-0510-2546 surname: Brandes fullname: Brandes, Nadav email: nadav.brandes@mail.huji.ac.il organization: School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel – sequence: 2 givenname: Dan surname: Ofer fullname: Ofer, Dan organization: Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel – sequence: 3 givenname: Yam surname: Peleg fullname: Peleg, Yam organization: Deep Trading Ltd., Haifa 3508401, Israel – sequence: 4 givenname: Nadav orcidid: 0000-0002-7218-2558 surname: Rappoport fullname: Rappoport, Nadav organization: Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel – sequence: 5 givenname: Michal orcidid: 0000-0002-9357-4526 surname: Linial fullname: Linial, Michal organization: Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35020807$$D View this record in MEDLINE/PubMed |
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Summary
Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to... Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences.... |
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| Title | ProteinBERT: a universal deep-learning model of protein sequence and function |
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