Pcons the first consensus fold recognition predictor

Pcons is the first consensus server for fold recognition. It selects the best prediction out of several predictions. For each query sequence predictions from several fold recognition servers is collected. For each of these models a measure that relates to the quality of the model is calculated. The prediction of this new measure is accomplished by utilizing structural comparisons between the models and analyzing the server score for a particular model. Pcons makes at least 10% more correct predictions than the best single method and the specificity is significantly better.

Supplementary information

• Submit a sequence to Pcons
• If you use Pcons please cite:Lundström J, Rychlewski L, Bujnicki J, Elofsson A. Pcons: A neural-network-based consensus predictor that improves fold recognition. Protein Sci. 2001 Nov;10(11):2354-62
• Paper describing Version 2 of Pcons
• LiveBench a continuous benchmarking of fold recognition methods (including Pcons)

FAQ:

• What is a significant Pcons score ?
  Any score higher than 1.5 should be significant
• What is the difference between Pcons1, 2, 3 and 4
  These methods use slightly differente servers and comparison techniques. The performance of Pcons2-4 is similar, so you can use the one you like best.
• What is the difference between Pcons and Pmodeller
  Pmodeller is an extension to Pcons. It makes a homology model using MODELLER and then uses ProQ to evaluate the quality of the model.