Automatic consensus‐based fold recognition using Pcons, ProQ, and Pmodeller

CASP provides a unique opportunity to compare the performance of automatic fold recognition methods with the performance of manual experts who might use these methods. Here, we show that a novel automatic fold recognition server, Pmodeller, is getting close to the performance of manual experts. Although a small group of experts still perform better, most of the experts participating in CASP5 actually performed worse even though they had full access to all automatic predictions. Pmodeller is based on Pcons (Lundström et al., Protein Sci 2001; 10(11):2354–2365) the first “consensus” predictor that uses predictions from many other servers. Therefore, the success of Pmodeller and other consensus servers should be seen as a tribute to the collective of all developers of fold recognition servers. Furthermore we show that the inclusion of another novel method, ProQ 2 , to evaluate the quality of the protein models improves the predictions. Proteins 2003;53:534–541. © 2003 Wiley‐Liss, Inc.

[1]  Roland L. Dunbrack,et al.  CAFASP2: The second critical assessment of fully automated structure prediction methods , 2001, Proteins.

[2]  Richard Bonneau,et al.  Rosetta in CASP4: Progress in ab initio protein structure prediction , 2001, Proteins.

[3]  S. Bryant,et al.  Critical assessment of methods of protein structure prediction (CASP): Round II , 1997, Proteins.

[4]  Arne Elofsson,et al.  A study of quality measures for protein threading models , 2001, BMC Bioinformatics.

[5]  D Fischer,et al.  Hybrid fold recognition: combining sequence derived properties with evolutionary information. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[6]  K Karplus,et al.  What is the value added by human intervention in protein structure prediction? , 2001, Proteins.

[7]  D Fischer,et al.  LiveBench‐2: Large‐scale automated evaluation of protein structure prediction servers , 2001, Proteins.

[8]  Leszek Rychlewski,et al.  Improving the quality of twilight‐zone alignments , 2000, Protein science : a publication of the Protein Society.

[9]  Predrag Radivojac,et al.  Protein Structure Prediction: Bioinformatics Approach , 2004 .

[10]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

[11]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[12]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[13]  J. M. Bradshaw,et al.  Mutational investigation of the specificity determining region of the Src SH2 domain. , 2000, Journal of molecular biology.

[14]  C Venclovas,et al.  Processing and analysis of CASP3 protein structure predictions , 1999, Proteins.

[15]  Arne Elofsson,et al.  Structure prediction meta server , 2001, Bioinform..

[16]  M. Sternberg,et al.  Enhanced genome annotation using structural profiles in the program 3D-PSSM. , 2000, Journal of molecular biology.

[17]  D Fischer,et al.  CAFASP‐1: Critical assessment of fully automated structure prediction methods , 1999, Proteins.

[18]  Daniel Fischer,et al.  3D‐SHOTGUN: A novel, cooperative, fold‐recognition meta‐predictor , 2003, Proteins.

[19]  Arne Elofsson,et al.  3D-Jury: A Simple Approach to Improve Protein Structure Predictions , 2003, Bioinform..

[20]  A. Elofsson,et al.  Can correct protein models be identified? , 2003, Protein science : a publication of the Protein Society.

[21]  J Lundström,et al.  Pcons: A neural‐network–based consensus predictor that improves fold recognition , 2001, Protein science : a publication of the Protein Society.

[22]  David C. Jones,et al.  GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. , 1999, Journal of molecular biology.

[23]  D. T. Jones,et al.  A new approach to protein fold recognition , 1992, Nature.

[24]  D Fischer,et al.  LiveBench‐1: Continuous benchmarking of protein structure prediction servers , 2001, Protein science : a publication of the Protein Society.

[25]  Arne Elofsson,et al.  MaxSub: an automated measure for the assessment of protein structure prediction quality , 2000, Bioinform..

[26]  T L Blundell,et al.  FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. , 2001, Journal of molecular biology.