The PSIPRED
Protein Structure Prediction Server
Predict
Secondary Structure (PSIPRED)
PSIPRED is a simple and accurate secondary
structure prediction method, incorporating two feed-forward neural networks
which perform an analysis on output obtained from PSI-BLAST
(Position Specific Iterated - BLAST). Using a very stringent cross validation
method to evaluate the method's performance, PSIPRED 2.6 achieves an average Q3
score of 80.7%.
Predictions produced by PSIPRED were also
submitted to the CASP4
evaluation and assessed during the CASP4 meeting, which took place in December
2000 at Asilomar. PSIPRED 2.0 achieved an average Q3 score of 80.6%
across all 40 submitted target domains with no obvious sequence similarity to
structures present in PDB, which ranked PSIPRED top out of 20 evaluated methods
(an earlier version of PSIPRED was also ranked top in CASP3 held in 1998).
It is important to realise, however, that
due to the small sample sizes, the results from CASP are not statistically
significant, although they do give a rough guide as to the current "state
of the art". For a more reliable evaluation, the EVA web site at Columbia University provides a
continuous evaluation. Also see the EVA servlet
to visualize a breakdown of specific types of errors made by PSIPRED and other
secondary structure prediction methods. NOTE that at the time of writing,
the EVA site is no longer being updated.
Downloads: The PSIPRED V2.6 software can be
downloaded from HERE.
Please note that you should read the license terms given in the README
file if you wish to incorporate PSIPRED in another program or Web server.
Older releases of PSIPRED can be downloaded
here HERE.
Predict
Transmembrane Topology (MEMSAT)
MEMSAT V3 is the latest version of the
widely used all-helical membrane protein prediction method MEMSAT. The method
was benchmarked on a test set of transmembrane proteins of known topology. From
sequence data MEMSAT was estimated to have an accuracy of over 78% at
predicting the structure of all-helical transmembrane proteins and the location
of their constituent helical elements within a membrane.
Academic users can download MEMSAT3 code here.
Fold
Recognition (GenTHREADER)
GenTHREADER is a fast and relatively
powerful fold recognition method, which can be applied to either whole,
translated genomic sequences (proteomes) as in the case of the GTD or
individual protein sequences as in the case of the PSIPRED
server. It is not as sensitive at mGenTHREADER but is much faster.
Fold
Recognition (mGenTHREADER)
This method is now our recommended method
for fold recognition and identification of distant homologues. Essentially it
is the based on the original GenTHREADER method, but makes use of
profile-profile alignments and predicted secondary structure (using PSIPRED) as
inputs. This increases both the sensitivity of the method and enhances the
accuracy of alignments, but also makes it much slower than the normal
GenTHREADER method as PSI-BLAST needs to be run on the target sequence before
the search can begin.
Domain
Recognition (pDomTHREADER)
pDomTHREADER is an accurate and sensitive
superfamily discrimination, combining information from both sequence and
structure to produce highly accurate domain alignments. The method employs the
same underlying threading algorithm as pGenTHREADER, however it aligns sequences
to a domain-based template library rather than a chain-based template library.
The use of smaller regions of structure for templates means that different
features of the alignments are required for optimal scoring. The final
prediction score results from an SVM trained on a combination of 5 different
feature inputs; template coverage, alignment score, template length, solvation
and pairwise potentials.
Compared with other superfamily
discrimination methods using Hidden Markov Models and PSI-BLAST profile
alignments, we found that pDomTHREADER provided higher coverage on the CATH S35 superfamilies. Additionally,
pDomTHREADER produced more accurate alignments that can be used to better
predict domain boundaries. For more information regarding the method, please
consult the reference above.
Please note that the pDomTHREADER method is
tuned for performance in fine superfamily discrimination, for fold recognition
problems or structural annotation of very distant sequences, pGenTHREADER
should be used.
Currently
loaded data banks
Sequences: Filtered UNIREF90
(updated weekly)Fold library: 16820 chains (last updated 1/3/2008) + weekly updates
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