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EVA: details for methods evaluated

Contents



List of all available prediction categories


cm: comparative modelling   CPHmodels SDSC1 SWISS-MODEL
fr: threading   3D-PSSM BLAST FUGUE LOOPP SAUSAGE Superfamily
sec: secondary structure   APSSP APSSP APSSP2 JPred PHD PROFking PROFphd Prospect PSIpred ROBETTA SAM-T02sec SAM-T99sec SAM-T99sec SSpro2
con: inter-residue contacts   CORNET PDGCON CONcons CONhydro



Categories of prediction methods evaluated



Prediction of comparative modelling (cm)


 
Server CPHmodels
Site (URL) http://www.cbs.dtu.dk/services/CPHmodels/
About CPHmodels is a collection of methods and databases developed to predict protein structures. It currently consists of the following tools: Sowhat: A neural network based method to predict contacts between C-alpha atoms from the amino acid sequence. RedHom: A tool to find a subset with low sequence similarity in a database. Databases: Subsets of the Brookhaven Protein Data Bank (PDB) database with low sequence similarity produced using the RedHom tool.
Quote
  1. O Lund, K Frimand, J Gorodkin, H Bohr, J Bohr, J Hansen, and S Brunak:Protein distance constraints predicted by neural networks and probability density functions. Protein Engineering, 10, 1241-1248, 1997"
Authors Ole Lund (CBS, Copenhagen, Denmark)
Contact Kristoffer Rapacki (rapacki@cbs.dtu.dk)
 
Server SDSC1
Site (URL) http://cl.sdsc.edu/hm.html
About Sequence similarity search using intermediate sequence search concept.
Quote no information given
Authors Phil Bourne and Ilya Shindyalov (San Diego Supercomputer Center, USA)
Contact Philip Bourne (bourne@sdsc.edu)
 
Server SWISS-MODEL
Site (URL) http://www.expasy.ch/swissmod/SWISS-MODEL.html
About SWISS-MODEL is an Automated Protein Modelling Server running at three different sites: (1) the GlaxoWellcome Experimental Research in Geneva, Switzerland (click here for more details on how the method works), (2) the Biocentre in Basel, Switzerland, and (3) the Advanced Computing Center NCI, Frederick, MD.
Quote
  1. M C Peitsch: Protein Modelling by E-mail.Bio/Technology, 13, 658-660, 1995.
  2. M C Peitsch: ProMod and Swiss-Model: Internet-based tools for automatedcomparative protein modelling.Biochem Soc Trans, 24, 274-279, 1996.
  3. N Guex, and M C Peitsch: SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modelling. Electrophoresis, 18, 2714-2723, 1997.
Authors Manuel Peitsch (Novartis, Basel), Torsten Schwede (Biocenter, Basel), and Nicolas Guex (Glaxo, Geneva)
Contact Nicolas Guex (ng45767@GlaxoWellcome.co.uk)



Prediction of threading (fr)


 
Server 3D-PSSM
Site (URL) http://www.bmm.icnet.uk/servers/3dpssm/
About Protein fold recognition using 1D and 3D sequence profiles coupled with secondary structure and solvation potential information.
Quote
  1. L A Kelley,R M MacCallum, and MJE Sternberg : Enhanced genome annotation using structural profiles in the program 3D-PSSM. J. Molecular Biology, 299, 501-522, 2000.
Authors Lawrence Kelley, Bob Maccallum and Mike Sternberg (ICRF, London, England)
Contact Lawrence Kelley (L.Kelley@icrf.icnet.uk)
 
Server BLAST
Site (URL) http://www.ncbi.nlm.nih.gov/BLAST
About Simple pairwise BLAST alignment against PDB.
Quote
  1. S Karlin, and SF Altschul: Applications and statistics for multiple high-scoring segments in molecular sequences. Proc. Natl. Acad. Sci. USA, 90, 90, 5873-5877, 1993.
Authors S Karlin & S Altschul (NCBI, NIH, USA)
Contact
 
Server FUGUE
Site (URL) http://www-cryst.bioc.cam.ac.uk/~fugue/
About The program FUGUE searches a sequence or sequence alignment against the library of profiles. Environment-specific substitution tables were derived from the structure-based alignments in the HOMSTRAD database. Each alignment in HOMSTRAD was converted into a scoring template (profile) using the environment-specific substitution tables and environment-dependent gap penalties.
Quote no information given
Authors Kenji Mizuguchi (Univ of Cambridge, England)
Contact Kenji Mizuguchi (kenji@cryst.bioc.cam.ac.uk)
 
Server LOOPP
Site (URL) http://ser-loopp.tc.cornell.edu/loopp.html
Quote
  1. J Meller, and R Elber:The design of an efficient and accurate threading algorithm: choice of energies and statistical verifications. submitted, 2000.
Authors Jaroslaw Meller (Cornell Univ, Ithaka, USA)
Contact Jaroslaw Meller (meller@cs.cornell.edu)
 
Server SAUSAGE
Site (URL) No longer available
About Sausage (Sequence-structure Alignment Using a Statistical Approach Guided by Experiment) is a protein threading program.
Quote
  1. T Huber, A J Russell, D Ayers, Andrew E. Torda : SAUSAGE: Protein threading with flexible force fields. Bioinformatics, 15, 1064-1065, 1999
Authors Anthony Russell and Andrew Torda (, Australia)
Contact Anthony Russell (arussell@rsc.anu.edu.au)
 
Server Superfamily
Site (URL) http://stash.mrc-lmb.cam.ac.uk/SUPERFAMILY/
About Protein domain assignments to SCOP structural superfamilies using a hidden Markov model library.
Quote no information given
Authors Julian Gough (MRC Cambridge)
Contact Julian Gough (jgough@mrc-lmb.cam.ac.uk)



Prediction of secondary structure (sec)


 
Server APSSP
Site (URL) http://imtech.res.in/raghava/apssp/
About Secondary structure prediction based on neural network and example based learning.
Quote no information given
Authors Gajendra Raghava (IMTECH Chandigarh, India)
Contact Gajendra Raghava (raghava@imtech.res.in)
 
Server APSSP
Site (URL) http://imtech.res.in/raghava/apssp/
Quote no information given
Authors Gajendra Raghava (IMTECH Chandigarh, India)
Contact Gajendra Raghava (raghava@imtech.res.in)
 
Server APSSP2
Site (URL) http://www.imtech.res.in/raghava/apssp2/
Quote
  1. G. P. S. Raghava, : Protein secondary structure prediction using nearest neighbor and neural network approach. CASP4: 75-76, 2000).
Authors Gajendra Raghava (IMTECH Chandigarh, India)
Contact Gajendra Raghava (raghava@imtech.res.in)
 
Server JPred
Site (URL) http://jura.ebi.ac.uk:8888/
About A consensus method for protein secondary structure prediction.
Quote
  1. J A Cuff, and G J Barton: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins, 34, 508-519, 1999
Authors James Cuff, and Geoff Barton (EBI Hinxton, England)
Contact James Cuff (james@ebi.ac.uk)
 
Server PHD
Site (URL) http://cubic.bioc.columbia.edu/predictprotein
Quote
  1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996.
Authors Burkhard Rost (Columbia Univ, USA)
Contact Burkhard Rost (rost@columbia.edu)
 
Server PROFking
Site (URL) http://www.aber.ac.uk/~phiwww/prof/
Quote
  1. M Ouali, R King: Cascaded multiple classifiers for secondary structure prediction. Protein Science, 9, 1162-1176, 1999.
Authors Mohammed Ouali, Ross King (Univ of Wales, Aberystwyth)
Contact Ross King (adk@aber.ac.uk)
 
Server PROFphd
Site (URL) http://cubic.bioc.columbia.edu/predictprotein
Quote
  1. B Rost: unpublished.
Authors Burkhard Rost (Columbia Univ, USA)
Contact Burkhard Rost (rost@columbia.edu)
 
Server Prospect
Site (URL) http://www.aber.ac.uk/~phiwww/prof/
Quote
  1. Y Xu, D Xu: Protein threading using PROSPECT: Design and evaluation. Proteins, 40, 343-354, 2000.
  2. Y Xu, E C Uberbacher: A polynomial-time algorithm for a class of protein threading problems. Journal of Computer Applications in Biosciences, 12, 511-517, 1996.
Authors Yin Xu (Oak Ridge Natl Lab)
Contact Yin Xu (xyn@ornl.gov)
 
Server PSIpred
Site (URL) http://insulin.brunel.ac.uk/psiform.html
Quote
  1. D Jones: Protein secondary structure prediction based on position-specific scoring matrices. J Molecular Biology, 292, 195-202, 1999.
Authors David Jones (Brunel Univ, Uxbridge, England)
Contact David Jones (David.Jones@brunel.ac.uk)
 
Server ROBETTA
Site (URL) http://robetta.bakerlab.org/
Quote
  1. Simons et al: J Mol Biol, 268, 209-225, 1997.
Authors Baker lab
Contact Baker lab
 
Server SAM-T02sec
Site (URL) http://www.cse.ucsc.edu/research/compbio/HMM-apps/T99-query.html
Quote
  1. K Karplus, C Barrett, and R Hughey: Hidden Markov Models for Detecting Remote Protein Homologies, Bioinformatics, 14, 846-856, 1998
Authors Kevin Karplus, Christian Barrett, and Richard Hughey (UCSD, Santa Cruz, USA)
Contact SAM-INFO (sam-info@cse.ucsc.edu)
 
Server SAM-T99sec
Site (URL) http://www.cse.ucsc.edu/research/compbio/HMM-apps/T99-query.html
Quote
  1. K Karplus, C Barrett, and R Hughey: Hidden Markov Models for Detecting Remote Protein Homologies, Bioinformatics, 14, 846-856, 1998
Authors Kevin Karplus, Christian Barrett, and Richard Hughey (UCSD, Santa Cruz, USA)
Contact SAM-INFO (sam-info@cse.ucsc.edu)
 
Server SAM-T99sec
Site (URL) http://www.cse.ucsc.edu/research/compbio/HMM-apps/T99-query.html
Quote
  1. K Karplus, C Barrett, and R Hughey: Hidden Markov Models for Detecting Remote Protein Homologies, Bioinformatics, 14, 846-856, 1998
Authors Kevin Karplus, Christian Barrett, and Richard Hughey (UCSD, Santa Cruz, USA)
Contact SAM-INFO (sam-info@cse.ucsc.edu)
 
Server SSpro2
Site (URL) http://promoter.ics.uci.edu/BRNN-PRED/
About Bi-directional recurrent neural network predicting secondary structure based on PSI-BLAST profiles.
Quote
  1. G Pollastri, D Przybylski, B Rost, P Baldi: Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins, submitted, 2001
Authors Gianluca Pollastri, Pierre Baldi (Univ of California, Irvine)
Contact Gianluca Pollastri (gpollast@ics.uci.edu )



Prediction of inter-residue contacts (con)


 
Server CORNET
Site (URL) http://prion.biocomp.unibo.it/cornet.html
About CORNET is a neural network-based method using correlated mutations, sequence conservation, predicted secondary structure, and evolutionary information. Authors: Fariselli Piero, Olmea Osvaldo, Valencia Alfonso, Rita Casadio. The method comprises an extension of the two following methods: (1) O Olmea & A Valencia (1997) Fold. & Design, 2, S25-32; and P Fariselli & R Casadio (1999) Prot. Engng., 12, 15-21.
Quote
  1. O Olmea, and A Valencia: Folding and Design, 2, S25-32, 1997
  2. P Fariselli, and R Casadio, Prot. Engng, 12, 15-21, 1999
Authors Piero Fariselli (Univ of Bologna, Italy), Olmea Osvaldo (CNB Madrid, Spain), Valencia Alfonso (CNB), and Rita Casadio (Bologna),
Contact Piero Fariselli (farisel@kaiser.alma.unibo.it)
 
Server PDGCON
Site (URL) http://montblanc.cnb.uam.es/cnb_pred/
Quote
  1. U Göbel, C Sander, R Schneider, and A Valencia: Correlated mutations and residue contacts in proteins. Proteins, 18, 309-317, 1994
  2. O Olmea, and A Valencia: Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Folding & Design, 2, S25-S32, 1997
  3. F Pazos, O Olmea, and A Valencia: A graphical interface for correlated mutations and other structure prediction methods. CABIOS, 13, 319-321, 1997
  4. G Casari, C Sander, and A Valencia: A method to predict functional residues in proteins. Nature Struc. Biol., 2, 171-178, 1995
  5. F Pazos, L Sanchez-Pulido, JA García-Ranea, MA Andrade, S Atrian, and A Valencia: Comparative analysis of different methods for the detection of specificity regions in protein families. In D Lundh, B Olsson, and A Narayanan (Eds.) "Biocomputing and Emergent Computation", Singaport: World Scientific, 132-145, 1997
  6. O Olmea, B Rost, and A Valencia: Effective use of sequence correlation and conservation in fold recognition. J. Mol. Biol., 293, 1221-1239, 1999
Authors Florencio Pazos, Olmea Osvaldo, and Valencia Alfonso (Protein Design Group, CNB Madrid, Spain)
Contact Florencio Pazos (pazos@cnb.uam.es)
 
Server CONcons
Site (URL) http://montblanc.cnb.uam.es/cnb_pred/
About CONhydro and CONcons are two virtual servers predicting contacts between all conserved and all hydrophobic residues. These two are included in the evaluation to provide a base-line for trivial contact predictions.
Quote
  1. O Olmea, and A Valencia: Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Folding & Design, 2, S25-S32, 1997
  2. F Pazos, O Olmea, and A Valencia: A graphical interface for correlated mutations and other structure prediction methods. CABIOS, 13, 319-321, 1997
Authors Florencio Pazos, Olmea Osvaldo, and Valencia Alfonso (Protein Design Group, CNB Madrid, Spain)
Contact Florencio Pazos (pazos@cnb.uam.es)
 
Server CONhydro
Site (URL) http://montblanc.cnb.uam.es/cnb_pred/
About CONhydro and CONcons are two virtual servers predicting contacts between all conserved and all hydrophobic residues. These two are included in the evaluation to provide a base-line for trivial contact predictions.
Quote
  1. O Olmea, and A Valencia: Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Folding & Design, 2, S25-S32, 1997
  2. F Pazos, O Olmea, and A Valencia: A graphical interface for correlated mutations and other structure prediction methods. CABIOS, 13, 319-321, 1997
Authors Florencio Pazos, Olmea Osvaldo, and Valencia Alfonso (Protein Design Group, CNB Madrid, Spain)
Contact Florencio Pazos (pazos@cnb.uam.es)




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