The PSIPRED protein structure prediction server.
TLDR
The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web.Abstract:
The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.read more
Citations
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Journal ArticleDOI
Protein structure prediction on the Web: a case study using the Phyre server.
TL;DR: This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre), which can reliably detect up to twice as many remote homologies as standard sequence-profile searching.
Journal ArticleDOI
Comparative Protein Structure Modeling Using MODELLER
Narayanan Eswar,Ben Webb,Marc A. Marti-Renom,Mallur S. Madhusudhan,David Eramian,Min-Yi Shen,Ursula Pieper,Andrej Sali +7 more
TL;DR: This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications.
Journal ArticleDOI
Comparative protein structure modeling using Modeller.
Narayanan Eswar,Ben Webb,Marc A. Marti-Renom,Mallur S. Madhusudhan,David Eramian,Min-Yi Shen,Ursula Pieper,Andrej Sali +7 more
TL;DR: This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications.
Journal ArticleDOI
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
TL;DR: Because MMseqs2 needs no random memory access in its innermost loop, its runtime scales almost inversely with the number of cores used, which enables sensitive protein sequence searching for the analysis of massive data sets.
Journal ArticleDOI
NMRFAM-SPARKY: enhanced software for biomolecular NMR spectroscopy
TL;DR: NMRFAM-SPARKY has been repackaged with current versions of Python and Tcl/Tk, which support new tools for NMR peak simulation and graphical assignment determination, and greatly accelerate protein side chain assignments.
References
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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
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
A new approach to protein fold recognition.
TL;DR: A new approach to fold recognition, whereby sequences are fitted directly onto the backbone coordinates of known protein structures, using a given sequence as a guide for the matching of sequences to backbone coordinates.
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.
Journal ArticleDOI
A model recognition approach to the prediction of all-helical membrane protein structure and topology.
TL;DR: The method employs a set of statistical tables (log likelihoods) complied from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization.