References
Steinegger, M. & Söding, J. Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Steinegger, M. & Söding, J. Clustering huge protein sequence sets in linear time. Nat. Commun. 9, 2542 (2018).
Söding, J. Protein homology detection by HMM–HMM comparison. Bioinformatics 21, 951–960 (2004).
Biegert, A. & Söding, J. Sequence context-specific profiles for homology searching. Proc. Natl Acad. Sci. USA 106, 3770–3775 (2009).
Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).
Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Price, M. N. et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557, 503–509 (2018).
Chang, Y.-C. et al. COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps. Nucleic Acids Res. 44, D330–D335 (2015).
UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).
Hou, J., Adhikari, B. & Cheng, J. DeepSF: deep convolutional neural network for mapping protein sequences to folds. Bioinformatics 34, 1295–1303 (2017).
Kulmanov, M., Khan, M. A. & Hoehndorf, R. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics 34, 660–668 (2017).
Cao, R. et al. ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network. Molecules 22, 1732 (2017).
Li, Y. et al. DEEPre: sequence-based enzyme ec number prediction by deep learning. Bioinformatics 34, 760–769 (2017).
Szalkai, B. & Grolmusz, V. Near perfect protein multi-label classification with deep neural networks. Methods 132, 50–56 (2018).
Zou, Z., Tian, S., Gao, X. & Li, Y. mlDEEPre: multi-functional enzyme function prediction with hierarchical multi-label deep learning. Front. Genet. 9, 714 (2019).
Schwartz, A. S. et al. Deep semantic protein representation for annotation, discovery, and engineering. Preprint at bioRxiv https://doi.org/10.1101/365965 (2018).
Zhang, D. and Kabuka, M. R. Protein family classification with multi-layer graph convolutional networks. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2390–2393 (IEEE, 2018).
Liu, X. Deep recurrent neural network for protein function prediction from sequence. Preprint at https://arxiv.org/abs/1701.08318 (2017).
Asgari, E. & Mofrad, M. R. K. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS ONE 10, e0141287 (2015).
Sinai, S., Kelsic, E., Church, G. M. & Nowak, M. A. Variational auto-encoding of protein sequences. Preprint at https://arxiv.org/abs/1712.03346 (2017).
See Also3.1: Amino Acids and PeptidesAlley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M. & Church, G. M. Unified rational protein engineering with sequence-based deep representation learning. Nat. Methods 16, 1315–1322 (2019).
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).
Littmann, M., Heinzinger, M., Dallago, C., Olenyi, T. & Rost, B. Embeddings from deep learning transfer GO annotations beyond homology. Sci. Rep. 11, 1160 (2021).
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2018).
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative hmm search procedure. BMC Bioinformatics 11, 431 (2010).
Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992).
Campen, A. et al. TOP-IDP-scale: a new amino acid scale measuring propensity for intrinsic disorder. Protein Pept. Lett. 15, 956–963 (2008).
Pace, C. N. & Scholtz, J. M. A helix propensity scale based on experimental studies of peptides and proteins. Biophysical J. 75, 422–427 (1998).
Finn, R. D. et al. Pfam: clans, web tools and services. Nucleic Acids Res. 34, D247–D251 (2006).
Bateman, A. What are these new families with 2, 3, 4 endings? Xfam Blog https://xfam.wordpress.com/2012/01/19/what-are-these-new-families-with-_2-_3-_4-endings/ (2012).
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2015).
Bateman, A. Google research team bring deep learning to Pfam. Xfam Blog https://xfam.wordpress.com/2021/03/24/google-research-team-bring-deep-learning-to-pfam/ (2021).
UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2014).
Li, Y., Jourdain, A. A., Calvo, S. E., Liu, J. S. & Mootha, V. K. CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets. PLoS Comput. Biol. 13, e1005653 (2017).
Hausrath, A. C., Ramirez, N. A., Ly, A. T. & McEvoy, M. M. The bacterial copper resistance protein CopG contains a cysteine-bridged tetranuclear copper cluster. J. Biol. Chem. 295, 11364–11376 (2020).
Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://arxiv.org/abs/1503.02531 (2015).
L.L. Sonnhammer, E., Eddy, S. R. & Durbin, R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins 28, 405–420 (1997).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).
Yu, F. and Koltun, V. Multi-scale context aggregation by dilated convolutions. Preprint at https://arxiv.org/abs/1511.07122 (2015).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
El-Gebali, S., Richardson, L. & Finn, R. Repeats in Pfam. EMBL-EBI Training https://doi.org/10.6019/TOL.Pfam_repeats-t.2018.00001.1 (2018).
UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699 (2018).