The PSIPRED Protein Structure Prediction Server

The PSIPRED protein structure prediction server allows you to submit a protein sequence, perform a prediction of your choice and receive the results of the prediction via e-mail. You may select one of three prediction methods to apply to your sequence: PSIPRED - a highly accurate method for protein secondary structure prediction, MEMSAT - our widely used transmembrane topology prediction method and GenTHREADER - a sequence profile based fold recognition method.

The technology and its advantages

Overview of prediction methods:

Predict Secondary Structure (PSIPRED)

PSIPRED is a simple and reliable secondary structure prediction method, incorporating two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST (Position Specific Iterated - BLAST).

Version 2.0 of PSIPRED includes a new algorithm which averages the output from up to 4 separate neural networks in the prediction process to further increase prediction accuracy.

Using a very stringent cross validation method to evaluate the method's performance, PSIPRED 2.0 is capable of achieving an average Q3 score of nearly 78%. Predictions produced by PSIPRED were also submitted to the CASP4 server 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.

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.

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.

Market opportunity

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Further information

Please contact Mrs Marina Santilli, UCL Business PLC
Tel: +44 (0)20 7679 9000,
email: m.santilli@uclb.com

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