Porter: a new, accurate server for protein secondary structure prediction
Reads0
Chats0
TLDR
Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date.Abstract:
Summary: Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date.
Availability: Porter is available as a public web server at http://distill.ucd.ie/porter/
Contact: gianluca.pollastri@ucd.ieread more
Citations
More filters
Journal ArticleDOI
JPred4: a protein secondary structure prediction server
TL;DR: JPred4 as discussed by the authors is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure predictions.
Journal ArticleDOI
JPred4: A protein secondary structure prediction server
TL;DR: The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices and the help-pages have been updated and tool-tips added as well as step-by-step tutorials.
Journal ArticleDOI
ESpritz: accurate and fast prediction of protein disorder.
TL;DR: An ensemble of protein disorder predictors based on bidirectional recursive neural networks and trained on three different flavors of disorder, including a novel NMR flexibility predictor called ESpritz, which can be especially useful for high-throughput applications.
Journal ArticleDOI
Comparative sequence analysis of leucine-rich repeats (LRRs) within vertebrate toll-like receptors
Norio Matsushima,Takanori Tanaka,Purevjav Enkhbayar,Tomoko Mikami,Tomoko Mikami,Masae Taga,Masae Taga,Keiko Yamada,Yoshio Kuroki +8 more
TL;DR: The super-repeat in the TLR7 family suggests strongly that "bacterial" and "typical" LRRs evolved from a common precursor and is inferred to play a key role in the structure and/or function of their TLRs.
Journal ArticleDOI
A deep learning network approach to ab initio protein secondary structure prediction
TL;DR: An SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which is called DNSS is developed and used to predict SS for a fully independent test dataset of 198 proteins.
References
More filters
Journal ArticleDOI
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
Stephen F. Altschul,Thomas L. Madden,Alejandro A. Schäffer,Jinghui Zhang,Zheng Zhang,Webb Miller,David J. Lipman +6 more
TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch,Chris Sander +1 more
TL;DR: A set of simple and physically motivated criteria for secondary structure, programmed as a pattern‐recognition process of hydrogen‐bonded and geometrical features extracted from x‐ray coordinates is developed.
Journal ArticleDOI
Protein secondary structure prediction based on position-specific scoring matrices
TL;DR: A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST and achieved an average Q3 score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date.
Journal ArticleDOI
Prediction of protein secondary structure at better than 70% accuracy.
Burkhard Rost,Chris Sander +1 more
TL;DR: A two-layered feed-forward neural network is trained on a non-redundant data base to predict the secondary structure of water-soluble proteins with a new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences.
Journal ArticleDOI
GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences.
TL;DR: The method has been applied to the genome of Mycoplasma genitalium, and analysis of the results shows that as many as 46 % of the proteins derived from the predicted protein coding regions have a significant relationship to a protein of known structure.
Related Papers (5)
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch,Chris Sander +1 more