Jpred

Jpred v.4 is the latest version of the JPred Protein Secondary Structure Prediction Server[1] which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction,[2] that has existed since 1998 in different versions.[3]

In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 134 000 jobs per month and has carried out over 2 million predictions in total for users in 179 countries.[4]

JPred 2

The static HTML pages of JPred 2 are still available for reference.[5]

JPred 3

The JPred v3[6] followed on from previous versions of JPred developed and maintained by James Cuff and Jonathan Barber (see JPred References[7]). This release added new functionality and fixed many 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

JPred 4

The current version of JPred (v4) has the following improvements and updates incorporated:

  • Retrained on the latest UniRef90 and SCOPe/ASTRAL version of Jnet (v2.3.1) - mean secondary structure prediction accuracy of >82%.[2]
  • Upgraded the Web Server to the latest technologies (Bootstrap framework, JavaScript) and updating the web pages – improving the design and usability through implementing responsive technologies.
  • Added RESTful API and mass-submission and results retrieval scripts - resulting in peak throughput above 20,000 predictions per day.[8]
  • Added prediction jobs monitoring tools.[9]
  • Upgraded the results reporting – both, on the web-site, and through the optional email summary reports: improved batch submission, added results summary preview through Jalview results visualization summary in SVG and adding full multiple sequence alignments into the reports.
  • Improved help-pages, incorporating tool-tips, and adding one-page step-by-step tutorials.[10]

Sequence residues are categorised or assigned to one of the secondary structure elements, such as alpha-helix, beta-sheet and coiled-coil.

Jnet uses two neural networks for its prediction. The first network is fed with a window of 17 residues over each amino acid in the alignment plus a conservation number. It uses a hidden layer of nine nodes and has three output nodes, one for each secondary structure element. The second network is fed with a window of 19 residues (the result of first network) plus the conservation number. It has a hidden layer with nine nodes and has three output nodes.[11]

See also

References

  1. ^ "JPred4: A Protein Secondary Structure Prediction Server". Retrieved 16 July 2015.
  2. ^ a b Drozdetskiy, Alexey; Cole, Chris; Procter, James; Barton, Geoffrey (Apr 16, 2015). "JPred4: a protein secondary structure prediction server". Nucleic Acids Research. 43 (W1): W389 – W394. doi:10.1093/nar/gkv332. PMC 4489285. PMID 25883141.
  3. ^ "JPred old news". Oct 25, 1998. Retrieved 16 Jul 2015.
  4. ^ "JPred4 statistics". Retrieved 16 July 2015.
  5. ^ "JPred2: legacy". Retrieved 16 July 2015.
  6. ^ "JPred3: previous version of JPred". Retrieved 16 July 2015.
  7. ^ "JPred4 references". Retrieved 16 July 2015.
  8. ^ "JPred4 RESTful API". Retrieved 16 July 2015.
  9. ^ "JPred4 monitoring tools". Retrieved 16 July 2015.
  10. ^ "JPred4 Help and Tutorials". Retrieved 16 July 2015.
  11. ^ Cuff, JA; Barton, GJ (August 2000). "Application of multiple sequence alignment profiles to improve protein secondary structure prediction". Proteins. 40 (3): 502–11. doi:10.1002/1097-0134(20000815)40:3<502::aid-prot170>3.0.co;2-q. PMID 10861942. S2CID 855816.