Improved enzyme functional annotation prediction using contrastive learning with structural inference

Yang, Yuxin; Jerger, Abby; Feng, Song; Wang, Zixu; Brasfield, Christina; Cheung, Margaret S.; Zucker, Jeremy; Guan, Qiang

doi:10.1038/s42003-024-07359-z

Title: Improved enzyme functional annotation prediction using contrastive learning with structural inference

Journal Article · 22 December 2024 · Communications Biology

DOI: https://doi.org/10.1038/s42003-024-07359-z · OSTI ID:2483723

; Jerger, Abby;

; Wang, Zixu; Brasfield, Christina; Cheung, Margaret S.;

;

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: DOE BER ; National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2483723

Report Number(s):: PNNL-SA--198399; PNNL-SA-198399; 1690; PII: 7359

Journal Information:: Communications Biology, Journal Name: Communications Biology Journal Issue: 1 Vol. 7; ISSN 2399-3642

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (30)

Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 Haas, Jürgen; Barbato, Alessandro; Behringer, Dario Proteins: Structure, Function, and Bioinformatics, Vol. 86 https://doi.org/10.1002/prot.25431	journal	December 2017
Critical assessment of methods of protein structure prediction (CASP)—Round XIV Kryshtafovych, Andriy; Schwede, Torsten; Topf, Maya Proteins: Structure, Function, and Bioinformatics https://doi.org/10.1002/prot.26237	journal	October 2021
Gaussian Mixture Models Reynolds, Douglas Encyclopedia of Biometrics https://doi.org/10.1007/978-0-387-73003-5_196	book	January 2009
Evaluation measures for hierarchical classification: a unified view and novel approaches Kosmopoulos, Aris; Partalas, Ioannis; Gaussier, Eric Data Mining and Knowledge Discovery, Vol. 29, Issue 3 https://doi.org/10.1007/s10618-014-0382-x	journal	September 2014
Basic local alignment search tool Altschul, Stephen F.; Gish, Warren; Miller, Webb Journal of Molecular Biology, Vol. 215, Issue 3, p. 403-410 https://doi.org/10.1016/S0022-2836(05)80360-2	journal	October 1990
A novel free-living prochlorophyte abundant in the oceanic euphotic zone Chisholm, Sallie W.; Olson, Robert J.; Zettler, Erik R. Nature, Vol. 334, Issue 6180 https://doi.org/10.1038/334340a0	journal	July 1988
Gene Ontology: tool for the unification of biology Ashburner, Michael; Ball, Catherine A.; Blake, Judith A. Nature Genetics, Vol. 25, Issue 1 https://doi.org/10.1038/75556	journal	May 2000
Structure-based protein function prediction using graph convolutional networks Gligorijević, Vladimir; Renfrew, P. Douglas; Kosciolek, Tomasz Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-23303-9	journal	May 2021
Functional annotation of enzyme-encoding genes using deep learning with transformer layers Kim, Gi Bae; Kim, Ji Yeon; Lee, Jong An Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-43216-z	journal	November 2023
Highly accurate protein structure prediction with AlphaFold Jumper, John; Evans, Richard; Pritzel, Alexander Nature https://doi.org/10.1038/s41586-021-03819-2	journal	July 2021
Fast and accurate protein structure search with Foldseek van Kempen, Michel; Kim, Stephanie S.; Tumescheit, Charlotte Nature Biotechnology https://doi.org/10.1038/s41587-023-01773-0	journal	May 2023
Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup Proceedings of the National Academy of Sciences, Vol. 116, Issue 28 https://doi.org/10.1073/pnas.1821905116	journal	June 2019
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences Rives, Alexander; Meier, Joshua; Sercu, Tom Proceedings of the National Academy of Sciences, Vol. 118, Issue 15 https://doi.org/10.1073/pnas.2016239118	journal	April 2021
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Altschul, Stephen F.; Madden, Thomas L.; Schäffer, Alejandro A. Nucleic Acids Research, Vol. 25, Issue 17, p. 3389-3402 https://doi.org/10.1093/nar/25.17.3389	journal	September 1997
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models Varadi, Mihaly; Anyango, Stephen; Deshpande, Mandar Nucleic Acids Research, Vol. 50, Issue D1 https://doi.org/10.1093/nar/gkab1061	journal	November 2021
The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003 Boeckmann, B. Nucleic Acids Research, Vol. 31, Issue 1 https://doi.org/10.1093/nar/gkg095	journal	January 2003
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes Ruepp, A. Nucleic Acids Research, Vol. 32, Issue 18 https://doi.org/10.1093/nar/gkh894	journal	October 2004
UniProt: a worldwide hub of protein knowledge Consortium, The UniProt Nucleic Acids Research https://doi.org/10.1093/nar/gky1049		November 2018
ImageNet: A large-scale hierarchical image database Deng, Jia; Dong, Wei; Socher, Richard 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2009.5206848	conference	June 2009
Deep Residual Learning for Image Recognition He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.90	conference	June 2016
Swin Transformer V2: Scaling Up Capacity and Resolution Liu, Ze; Hu, Han; Lin, Yutong 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR52688.2022.01170	conference	June 2022
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework Zhang, Shu; Xu, Ran; Xiong, Caiming 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR52688.2022.01616	conference	June 2022
ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing Elnaggar, Ahmed; Heinzinger, Michael; Dallago, Christian IEEE Transactions on Pattern Analysis and Machine Intelligence https://doi.org/10.1109/TPAMI.2021.3095381	journal	January 2021
Evolutionary-scale prediction of atomic-level protein structure with a language model Lin, Zeming; Akin, Halil; Rao, Roshan Science, Vol. 379, Issue 6637 https://doi.org/10.1126/science.ade2574	journal	March 2023
Enzyme function prediction using contrastive learning Yu, Tianhao; Cui, Haiyang; Li, Jianan Canal Science, Vol. 379, Issue 6639 https://doi.org/10.1126/science.adf2465	journal	March 2023
ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature Dalkiran, Alperen; Rifaioglu, Ahmet Sureyya; Martin, Maria Jesus BMC Bioinformatics, Vol. 19, Issue 1 https://doi.org/10.1186/s12859-018-2368-y	journal	September 2018
HH-suite3 for fast remote homology detection and deep protein annotation Steinegger, Martin; Meier, Markus; Mirdita, Milot BMC Bioinformatics, Vol. 20, Issue 1 https://doi.org/10.1186/s12859-019-3019-7	journal	September 2019
Learning local feature descriptors with triplets and shallow convolutional neural networks Balntas, Vassileios; Riba, Edgar; Ponsa, Daniel Procedings of the British Machine Vision Conference 2016 https://doi.org/10.5244/C.30.119	conference	January 2016
Code and Data for CLEAN-Contact: Contrastive Learning Enabled Enzyme Functional Annotation Prediction with Structural Inference Yang, Yuxin Zenodo https://doi.org/10.5281/zenodo.14194685	dataset	January 2024
ProteInfer, deep neural networks for protein functional inference Sanderson, Theo; Bileschi, Maxwell L.; Belanger, David eLife, Vol. 12 https://doi.org/10.7554/eLife.80942	journal	February 2023

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