The Jpred 3 secondary structure prediction server

By Christian Cole, Jonathan D. Barber, Geoffrey J. Barton. Nucleic Acids Res., 2008

1,603 citations recorded by the source corpus.

Jpred (http://www.compbio.dundee.ac.uk/jpred) is a secondary structure prediction server powered by the Jnet algorithm. Jpred performs over 1000 predictions per week for users in more than 50 countries. The recently updated Jnet algorithm provides a three-state (α-helix, β-strand and coil) prediction of secondary structure at an accuracy of 81.5%. Given either a single protein sequence or a multiple sequence alignment, Jpred derives alignment profiles from which predictions of secondary structure and solvent accessibility are made. The predictions are presented as coloured HTML, plain text, PostScript, PDF and via the Jalview alignment editor to allow flexibility in viewing and applying the data. The new Jpred 3 server includes significant usability improvements that include clearer feedback of the progress or failure of submitted requests. Functional improvements include batch submission of sequences, summary results via email and updates to the search databases. A new software pipeline will enable Jnet/Jpred to continue to be updated in sync with major updates to SCOP and UniProt and so ensures that Jpred 3 will maintain high-accuracy predictions.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eed8d044f38218f08bc7664ec44990efff565f13

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Citations in the corpus

A structural model of the active ribosome-bound membrane protein insertase YidC (2014, methodology): “Additional information based on direct residue–residue interactions (covariance analysis) and secondary structure predictions by Jpred 3 (Cole et al., 2008) were used as structural restraints in MODELLER (Eswar et al., 2008).”
A Unified Multitask Architecture for Predicting Local Protein Properties (2012, methodology): “that employ neural networks [5, 9–13] and probability models such as hidden Markov models [14] and dynamic Bayesian networks [15].”
Computational prediction of secondary and supersecondary structures. (2013, methodology): “Table 1 summarizes 15 methods, including PSIPRED (36, 38, 39), SPINE (93, 94), Frag1D (95), DISSPred (96), SAM-T (97-101), PROTEUS (102, 103), Jpred (104-106), P.S.HMM (107), Porter (108, 109), OS-HMM (110), YASSPP (111), YASPIN (112), SABLE (113), and SSpro (114, 115), that predict the 3-state seco”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and facial recognition as well as the aforementioned usage in protein secondary structure prediction [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [24], [33], [34], [35], [36]”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “solutions such as PSIPRED [21], SSpro [17] and JNET [32] were recently upgraded and modified.”
The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. (2015, neutral): “5 [71,73] (top) andJPRED3 [78] (bottom).”
JPred4: a protein secondary structure prediction server (2015, methodology): “As with JPred3, JPred4 makes secondary structure and residue solvent accessibility predictions by the JNet algo-rithm (11,31).”