The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
PredictProtein - Predicting Protein Structure and Function for 29 Years
Open accessAbstract
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
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Cites
The Pfam protein families database in 2019
El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky995 · Cited by 4,602
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ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
Clustering huge protein sequence sets in linear time
Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875
Self-citationEvaluating Protein Transfer Learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719
MMseqs2 desktop and local web server app for fast, interactive sequence searches
Mirdita, M., Steinegger, M., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1057 · Cited by 618
Self-citationModeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606
Self-citation‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Callaway, E. (2020) · Nature · doi:10.1038/d41586-020-03348-4 · Cited by 499
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ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329
Light attention predicts protein location from the language of life
Stärk, H., Dallago, C., Heinzinger, M., & Rost, B. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab035 · Cited by 130
Self-citationGreven, J. A., Wydra, J. R., Greer, R. A., Zhi, C., Price, D. A., Svoboda, J. D., Camitta, C. L. M., Washington, M., Leung, D. W., Song, Y., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00795-9 · Cited by 42
DeepSS2GO: protein function prediction from secondary structure
Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26
LambdaPP : Fast and accessible protein‐specific phenotype predictions
(2022) · doi:10.1002/pro.4524 · Cited by 19
Kors, S., Schuster, M., Maddison, D. C., Kilaru, S., Schrader, T. A., Costello, J. L., Islinger, M., Smith, G. A., & Schrader, M. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2024.119843 · Cited by 12
Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn
Liebhoff, A.-M., Venkataraman, T., Morgenlander, W. R., Na, M., Kula, T., Waugh, K., Morrison, C., Rewers, M., Longman, R., Round, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45601-8 · Cited by 10
Kramer, D. A., Narvaez-Ortiz, H. Y., Patel, U., Shi, R., Shen, K., Nolen, B. J., Roche, J., & Chen, B. (2023) · eLife · doi:10.7554/elife.88492 · Cited by 7
Shishir, T. A., Jannat, T., & Naser, I. B. (2022) · doi:10.1101/2022.02.21.481269 · Cited by 5
Data‐driven evaluation of suitable immunogens for improved antibody selection
Waury, K., Kvartsberg, H., Zetterberg, H., Blennow, K., Teunissen, C. E., & Abeln, S. (2025) · Protein Science · doi:10.1002/pro.70100 · Cited by 1
A Designed Zn 2+ Sensor Domain Transmits Binding Information to Transmembrane Histidine Kinases
Hatstat, A. K., Kormos, R., Xu, V., Du, G., Liu, L., Zhang, S.-Q., & DeGrado, W. F. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c02273 · Cited by 1
Metadata
- Authors
- Bernhofer, M., Dallago, C., Karl, T., Satagopam, V., Heinzinger, M., Littmann, M., Olenyi, T., Qiu, J., Schütze, K., Yachdav, G., Ashkenazy, H., Ben-Tal, N., Bromberg, Y., Goldberg, T., Kajan, L., O’Donoghue, S., Sander, C., Schafferhans, A., Schlessinger, A., Vriend, G., Mirdita, M., Gawron, P., Gu, W., Jarosz, Y., Trefois, C., Steinegger, M., Schneider, R., & Rost, B.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-05-17
- Volume
- 49
- Issue
- W1
- Pages
- W535-W540
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W3163595068
- Cited by
- 233
- References
- 15
- FWCI
- 12.08
- Type
- journal article
- Language
- en
- Open access
- gold
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