Jpred is a Protein Secondary Structure Prediction server and has been in operation since approximately 1998. Jpred incorporates the Jnet algorithm in order to make more accurate predictions. In addition to protein secondary structure Jpred also makes predictions on Solvent Accessibility and Coiled-coil regions (Lupas method).
The current version of Jpred (v3) follows on from previous versions of Jpred developed and maintained by James Cuff and Jonathan Barber (see References). This release adds new functionality and fixes lots of bugs. The highlights are:
- New, friendlier user interface
- Retrained and optimised version of Jnet (v2) - mean secondary structure prediction accuracy of >81%
- Batch submission of jobs
- Better error checking of input sequences/alignments
- Predictions now (optionally) returned via e-mail
- Users may provide their own query names for each submission
- Jpred now makes a prediction even when there are no PSI-BLAST hits to the query
- PS/PDF output now incorporates all the predictions
The static HTML pages of Jpred 2 are still available for reference.
- New data
- The latest SCOP-derived data used to train Jnet will be available for download when the paper is published.
- The original training data used to train the Jnet neural networks as described in the Cuff & Barton (2000) paper.
- The 'blind' data to validate the CB513-trained neural networks. A final Q3 accuracy of 76.4% was achieved.
Both the CB513 and CB406 datasets are internally non-redundant and there is no detectable sequence redundancy between the two datasets either.
An early release of the Jnet v1 C source code can be found here:
The current version of the Jnet C source as used by Jpred will be released as soon as it is in a state that will not offend ;)
Jpred historical and recent usage can be viewed here.