PredictProtein - Predicting Protein Structure and Function for 29 Years

Michael Bernhofer(Technical University of Munich), Christian Dallago(Technical University of Munich), Tim Karl(Technical University of Munich), Venkata Satagopam(University of Luxembourg), Michael Heinzinger(Technical University of Munich), Maria Littmann(Technical University of Munich), Tobias Olenyi(Technical University of Munich), Jiajun Qiu(Shanghai Jiao Tong University), Konstantin Schütze(Technical University of Munich), Guy Yachdav(Technical University of Munich), Haim Ashkenazy(Tel Aviv University), Nir Ben‐Tal(Tel Aviv University), Yana Bromberg(Rutgers, The State University of New Jersey), Tatyana Goldberg(Technical University of Munich), László Kaján, Séan O’Donoghue(Garvan Institute of Medical Research), Chris Sander(Broad Institute), Andrea Schafferhans(Weihenstephan-Triesdorf University of Applied Sciences), Avner Schlessinger(Icahn School of Medicine at Mount Sinai), Gerrit Vriend(Bicol University), Milot Mirdita(Max Planck Institute for Biophysical Chemistry), Piotr Gawron(University of Luxembourg), Wei Gu(University of Luxembourg), Yohan Jarosz(University of Luxembourg), Christophe Trefois(University of Luxembourg), Martin Steinegger(Seoul National University), Reinhard Schneider(University of Luxembourg), Burkhard Rost(Institute for Advanced Study)
Nucleic Acids Research
May 11, 2021
Cited by 263Open Access
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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'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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