PredictProtein \textendash Predicting Protein Structure and Function for 29 Years. 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\textquoterightDonoghue, 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. bioRxiv, Cold Spring Harbor Laboratory, 2021.
PredictProtein \textendash Predicting Protein Structure and Function for 29 Years [link]Paper  doi  abstract   bibtex   
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\textquoterights infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; 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.Availability Freely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlabCompeting Interest StatementThe authors have declared no competing interest.
@article {Bernhofer2021.02.23.432527,
	author = {Bernhofer, Michael and Dallago, Christian and Karl, Tim and Satagopam, Venkata and Heinzinger, Michael and Littmann, Maria and Olenyi, Tobias and Qiu, Jiajun and Sch{\"u}tze, Konstantin and Yachdav, Guy and Ashkenazy, Haim and Ben-Tal, Nir and Bromberg, Yana and Goldberg, Tatyana and Kajan, Laszlo and O{\textquoteright}Donoghue, Sean and Sander, Chris and Schafferhans, Andrea and Schlessinger, Avner and Vriend, Gerrit and Mirdita, Milot and Gawron, Piotr and Gu, Wei and Jarosz, Yohan and Trefois, Christophe and Steinegger, Martin and Schneider, Reinhard and Rost, Burkhard},
	title = {PredictProtein {\textendash} Predicting Protein Structure and Function for 29 Years},
	elocation-id = {2021.02.23.432527},
	year = {2021},
	doi = {10.1101/2021.02.23.432527},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {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{\textquoteright}s infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; 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.Availability Freely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlabCompeting Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2021/02/24/2021.02.23.432527},
	eprint = {https://www.biorxiv.org/content/early/2021/02/24/2021.02.23.432527.full.pdf},
	journal = {bioRxiv}
}
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