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PredictProtein - Predicting Protein Structure and Function for 29 Years

Authors :
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]
Bernhofer, Michael
Dallago, Christian
Karl, Tim
Satagopam, Venkata
Heinzinger, Michael
Littmann, Maria
Olenyi, Tobias
Qiu, Jiajun
Schütze, Konstantin
Yachdav, Guy
Ashkenazy, Haim
Ben-Tal, Nir
Bromberg, Yana
Goldberg, Tatyana
Kajan, Laszlo
O’Donoghue, Sean
Sander, Chris
Schafferhans, Andrea
Schlessinger, Avner
Vriend, Gerrit
Mirdita, Milot
Gawron, Piotr
Gu, Wei
Jarosz, Yohan
Trefois, Christophe
Steinegger, Martin
Schneider, Reinhard
Rost, Burkhard
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center]
Bernhofer, Michael
Dallago, Christian
Karl, Tim
Satagopam, Venkata
Heinzinger, Michael
Littmann, Maria
Olenyi, Tobias
Qiu, Jiajun
Schütze, Konstantin
Yachdav, Guy
Ashkenazy, Haim
Ben-Tal, Nir
Bromberg, Yana
Goldberg, Tatyana
Kajan, Laszlo
O’Donoghue, Sean
Sander, Chris
Schafferhans, Andrea
Schlessinger, Avner
Vriend, Gerrit
Mirdita, Milot
Gawron, Piotr
Gu, Wei
Jarosz, Yohan
Trefois, Christophe
Steinegger, Martin
Schneider, Reinhard
Rost, Burkhard
Publication Year :
2021

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.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1258960384
Document Type :
Electronic Resource