The look and feel of the JPred4 web server has been changed significantly compared to JPred3 by embracing contemporary web technologies, the Bootstrap framework ( and custom JavaScript. Results are returned either interactively through a web page or as a summary email that directs the user to results on the JPred4 website. The user can submit a single protein sequence, a multiple sequence alignment (MSA) or a batch of single protein sequences for prediction. The basic usage pattern for JPred4 is the same as for JPred3 ( 11). In this paper we summarize the current performance and features of the upgraded JPred server (JPred4) which incorporates the secondary structure and solvent accessibility prediction program JNet v.2.3.1. There is no recent blind prediction test for the PROFphd secondary structure prediction algorithm in the PredictProtein ( 29) secondary structure prediction method, though the earlier PROFsec reported 76% ( 30). 3.0) ( 11) gave 81.5% three-state accuracy (Q 3), PSIPRED v.3.0 ( 28) reported accuracy of 81.4%, while the current PSIPRED V 3.2 server, which includes a broad suite of prediction algorithms, quotes 81.6%. Secondary structure predictions can also help in the identification of functional domains and may be used to guide the rational design of site-specific or deletion mutation experiments.Īlthough hundreds of papers have been published describing methods for protein secondary structure prediction, three of the most widely used are JPred, PSIPRED and PredictProtein. Although knowledge of the secondary structure alone is not as useful as a full three-dimensional model, secondary structure predictions provide important constraints for fold-recognition techniques ( 13– 17) as well as in homology modelling ( 18, 19), ab initio ( 20– 24) and constraint-based tertiary structure prediction methods ( 25– 27). all other states) have increased in accuracy from around 50% in 1983 ( 7) to over 80% today ( 8– 11) which is close to the estimated maximum for prediction from multiple alignment ( 12). Over the last 30 years, techniques to predict the three-state secondary structure of the protein (α-helix, β-strand and coil: i.e. As a consequence, there is a need for accurate methods to predict structural and functional features from the amino acid sequence. The routine use of massively parallel DNA sequencing technologies today means knowledge of protein sequences will continue to outpace structural biology for the foreseeable future. Although recent developments in structural biology ( 1– 4) have led to an acceleration in the rate of three-dimensional structure determination by X-ray crystallography, nuclear magnetic resonance and 3D-EM techniques, in January 2015 there were still just 105 732 protein structures known ( ) ( 5) compared to almost 90 million sequences ( ) ( 6). Knowledge of a protein's three-dimensional structure is central to understanding the protein's detailed function. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. JPred4 ( ) 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 prediction.
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