RT Journal Article
SR Electronic
T1 PredictProtein – Predicting Protein Structure and Function for 29 Years
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.23.432527
DO 10.1101/2021.02.23.432527
A1 Michael Bernhofer
A1 Christian Dallago
A1 Tim Karl
A1 Venkata Satagopam
A1 Michael Heinzinger
A1 Maria Littmann
A1 Tobias Olenyi
A1 Jiajun Qiu
A1 Konstantin Schütze
A1 Guy Yachdav
A1 Haim Ashkenazy
A1 Nir Ben-Tal
A1 Yana Bromberg
A1 Tatyana Goldberg
A1 Laszlo Kajan
A1 Sean O’Donoghue
A1 Chris Sander
A1 Andrea Schafferhans
A1 Avner Schlessinger
A1 Gerrit Vriend
A1 Milot Mirdita
A1 Piotr Gawron
A1 Wei Gu
A1 Yohan Jarosz
A1 Christophe Trefois
A1 Martin Steinegger
A1 Reinhard Schneider
A1 Burkhard Rost
YR 2021
UL http://biorxiv.org/content/early/2021/02/24/2021.02.23.432527.abstract
AB 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; 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.