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DeepSS2GO: protein function prediction from secondary structure
Open accessPreprint — published as 10.1093/bib/bbae196 · Paper page
Abstract
Predicting protein function is crucial for understanding biological life processes, preventing diseases, and developing new drug targets. In recent years, methods based on sequence, structure, and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features, while streamlining the redundant data in primary sequences and bypassing the timeconsuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-ofthe-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO .
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Research works printed but not linked: 57
- 2021, Deep learning in bioinformatics and biomedicine, Briefings in bioinformatics, 22, 1513, 10.1093/bib/bbab087 · DOI
- 2022, Understudied proteins: opportunities and challenges for functional proteomics, Nature Methods, 19, 774, 10.1038/s41592-022-01454-x · DOI
- 2018, Predicting human protein function with multi-task deep neural networks, PloS one, 13, e0198216, 10.1371/journal.pone.0198216 · DOI
- 10.1038/75556 · DOI · Paper page
- 10.1093/nar/28.1.304 · DOI
- 10.1093/nar/28.1.27 · DOI · Paper page
- 10.1093/NAR/GKAA913 · DOI · Paper page
- 2022, Using deep learning to annotate the protein universe, Nature Biotechnology, 40, 932, 10.1038/s41587-021-01179-w · DOI · Paper page
- 10.1093/bib/bbw068 · DOI
- 10.1038/d41586-018-02174-z · DOI
- 2021, Predictprotein-predicting protein structure and function for 29 years, Nucleic acids research, 49, W535, 10.1093/nar/gkab354 · DOI
- 10.1186/1471-2105-10-1 · DOI
- 10.1093/nar/25.17.3389 · DOI · Paper page
- 10.1093/nar/gkaa977 · DOI · Paper page
- 10.1016/j.sbi.2006.04.004 · DOI
- 2010, On position-specific scoring matrix for protein function prediction, IEEE/ACM transactions on computational biology and bioinformatics, 8, 308
- 2017, Introduction to convolutional neural networks, National Key Lab for Novel Software Technology. Nanjing University. China, 5, 495
- 2021, A gentle introduction to graph neural networks, Distill, 6, e33
- 10.48550/ARXIV.2006.11239 · DOI · Paper page
- Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Lukasz Kaiser , and Illia Polosukhin . Attention is all you need. Advances in neural information processing systems, 30, 2017.
- 10.1101/2020.12.15.422761 · DOI · Paper page
- Ali Madani , Ben Krause , Eric R Greene , Subu Subramanian , Benjamin P Mohr , James M Holton , Jose Luis Olmos Jr , Caiming Xiong , Zachary Z Sun , Richard Socher , et al. Large language models generate functional protein sequences across diverse families. Nature Biotechnology, pages 1–8, 2023. · DOI · Paper page
- 2020, Protein– protein interaction site prediction through combining local and global features with deep neural networks, Bioinformatics, 36, 1114, 10.1093/bioinformatics/btz699 · DOI
- 10.1093/bioinformatics/btz595 · DOI
- 10.1093/bioinformatics/btab198 · DOI
- 2020, Graph2go: a multi-modal attributed network embedding method for inferring protein functions, GigaScience, 9, giaa081, 10.1093/gigascience/giaa081 · DOI
- 2021, Structure-based protein function prediction using graph convolutional networks, Nature communications, 12, 3168, 10.1038/s41467-021-23303-9 · DOI
- 2019, Netgo: improving large-scale protein function prediction with massive network information, Nucleic acids research, 47, W379, 10.1093/nar/gkz388 · DOI
- 10.1093/nar/gkaa1074 · DOI · Paper page
- 10.1093/bioinformatics/btab270 · DOI
- 10.1126/science.181.4096.223 · DOI · Paper page
- 2008, Protein crystallization: from purified protein to diffraction-quality crystal, Nature methods, 5, 147, 10.1038/nmeth.f.203 · DOI
- 10.1038/s41586-020-2833-4 · DOI
- Constance J Jeffery . Current successes and remaining challenges in protein function prediction. Frontiers in Bioinformatics, 3, 2023. · DOI
- 10.1038/nrd.2018.77 · DOI
- 2021, The trrosetta server for fast and accurate protein structure prediction, Nature protocols, 16, 5634, 10.1038/s41596-021-00628-9 · DOI
- 10.1093/nar/gku1028 · DOI
- 1983, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolymers: Original Research on Biomolecules, 22, 2577, 10.1002/bip.360221211 · DOI · Paper page
- 2022, Accurate protein function prediction via graph attention networks with predicted structure information, Briefings in Bioinformatics, 23, bbab502, 10.1093/bib/bbab502 · DOI
- 2018, Functional annotation of proteins encoded by the minimal bacterial genome based on secondary structure element alignment, Journal of proteome research, 17, 2511, 10.1021/acs.jproteome.8b00262 · DOI · Paper page
- 2022, Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment, Scientific Reports, 12, 7607, 10.1038/s41598-022-11684-w · DOI
- 10.1038/s41586-023-07004-5 · DOI
- 10.1038/nmeth.3176 · DOI · Paper page
- 2023, Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion, Briefings in Bioinformatics, 24, bbad117, 10.1093/bib/bbad117 · DOI
- Zhongyu Wang , Zhaohong Deng , Wei Zhang , Qiongdan Lou , Kup-Sze Choi , Zhisheng Wei , Lei Wang , and Jing Wu . Mmsmaplus: a multi-view multi-scale multi-attention embedding model for protein function prediction. Briefings in Bioinformatics, page bbad201, 2023. · DOI
- 10.1093/nar/gkaa1100 · DOI · Paper page
- 2021, Prottrans: Toward understanding the language of life through self-supervised learning, IEEE transactions on pattern analysis and machine intelligence, 44, 7112
- Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . Pytorch: An imperative style, high-performance deep learning library. In H. Wallach , H. Larochelle , A. Beygelzimer , F. d’Alché-Buc , E. Fox , and R. Garnett , editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
- 2014, Adam: A method for stochastic optimization, arXiv preprint
- 2013, A largescale evaluation of computational protein function prediction, Nature methods, 10, 221, 10.1038/nmeth.2340 · DOI · Paper page
- 10.1093/bioinformatics/btt228 · DOI
- Jesse Davis and Mark Goadrich . The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233–240, 2006. · DOI · Paper page
- 10.1038/nmicrobiol.2016.33 · DOI
- 10.1074/jbc.M114.582338 · DOI
- 2020, Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning, Nature Methods, 17, 184, 10.1038/s41592-019-0666-6 · DOI
- 2022, Pseudoisolated α-helix platform for the recognition of deep and narrow targets, Journal of the American Chemical Society, 144, 15519, 10.1021/jacs.2c03858 · DOI
- 2022, In pursuit of next-generation therapeutics: Antimicrobial peptides against superbugs, their sources, mechanism of action, nanotechnology-based delivery, and clinical applications, International Journal of Biological Macromolecules, 218, 135, 10.1016/j.ijbiomac.2022.07.103 · DOI
Metadata
- Authors
- Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M.
- Affiliations
- Southern University of Science and Technology (CN)
- Queen Mary University of London (GB)
- MGI (CN)
- Corresponding author
- Song, Fu V.
- Ni, Ming
- Liao, Maofu
- Venue
- Briefings in Bioinformatics
- Publisher
- openRxiv
- Published
- 2024-04-01
- Volume
- 25
- Issue
- 3
- ISSN
- 1467-5463 (ISSN-L)
- PMC
- PMC11066904
- OpenAlex
- W4396627752
- Cited by
- 26
- References
- 1
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 6
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Southern University of Science and Technology
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein function prediction, Gene Ontology, protein secondary structure, deep neural network, sequence-based features, high-throughput sequencing data, computational efficiency
- MeSH terms
- Algorithms, Proteins, Software, Neural Networks, Computer, Protein Structure, Secondary, Computational Biology, Sequence Analysis, Protein, Databases, Protein, Gene Ontology
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