Brief explanation of META PredictProtein

Table of Contents

• User information
• List of all available services
• Services available
  1. Various analyses
  2. Secondary structure prediction
  3. Membrane regions
  4. Threading (detecting remote homologues in PDB)
  5. Homology modelling
• Formats for submitting sequence or alignment

User information

1. Your email Example: rost@embl-heidelberg.de
   Your entire (and entirely correct) email address (e.g. rost@embl-heidelberg.de).
   Note: typos will result in that we shall not be ablet to return the results.
2. One-line name of protein Example: Cytochrome C oxidase
3. Paste, or type your sequence
   Example:

   	MSAQISDSIEEKRGFFTRWFMSTNHKDIGVLYLFTAGLAGLISVTLTVYMRMELQHPGVQ
   	YMCLEGMRLVADAAAECTPNAHL

   Please use only one-letter code amino acids. In particular, avoid numbers or '*', or '.'.
   For other possible input formats click here!
4. SUBMIT or CLEAR
   Click on the button SUBMIT to request a prediction
   Click on the button CLEAR to clear all data you filled in (e.g. to restart, or to send a new request).

List of services available

Services available

Various analyses

1. Server: signalp
   • Site (URL): http://www.cbs.dtu.dk/services/SignalP/
   • About: The SignalP World Wide Web server predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
   • Options:
     
     • If you do not check the organism (gram-positive, gram-negative prokaryotes, or eukaryotes), predictions for all three will be returned.
   • Please quote when using output from signalp:
     1. H Nielsen, J Engelbrecht, S Brunak, and G von Heijne:
        Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering 10, 1-6, 1997
   • Administrator: Kristoffer Rapacki
   • Done by: Henrik Nielsen, Soeren Brunak (both CBS Copenhagen), and Gunnar von Heijne (Univ Stockholm, Sweden)
   • Email address: rapacki@cbs.dtu.dk
2. Server: netoglyc
   • Site (URL): http://www.cbs.dtu.dk/services/NetOGlyc/
   • About: The NetOglyc WWW server produces neural network predictions of mucin type GalNAc O-glycosylation sites in mammalian proteins.
   • Please quote when using output from netoglyc:
     1. JE Hansen, O Lund, N Tolstrup, AA Gooley, KL Williams, and S Brunak:
        NetOglyc: Prediction of mucin type O-glycosylation sites based on sequence context and surface accessibility. Glycoconjugate Journal, 15, 115-130, 1998
     2. JE Hansen, O Lund, K Rapacki, and S Brunak:
        O-glycbase version 2.0 - A revised database of O-glycosylated proteins. Nucleic Acids Research, 25, 278-282, 1997
     3. JE Hansen, O Lund, K Rapacki J Engelbrecht, H Bohr, JO Nielsen, J-E S Hansen, and S Brunak:
        Prediction of O-glycosylation of mammalian proteins: Specificity patterns of UDP-GalNAc:-polypeptide N-acetylgalactosaminyltransferase. Biochemical Journal, 308, 801-813, 1995
   • Administrator: Kristoffer Rapacki
   • Done by: Jan Hansen (CBS, Copenhagen, Denmark)
   • Email address: rapacki@cbs.dtu.dk
3. Server: netpico
   • Site (URL): http://www.cbs.dtu.dk/services/NetPicoRNA/
   • About: The NetPicoRNA World Wide Web server produces neural network predictions of cleavage sites of picornaviral proteases.
   • Please quote when using output from netpico:
     1. N Blom, J Hansen, D Blaas, and S Brunak:
        Cleavage site analysis in picornaviral polyproteins: Discovering cellular targets by neural networks. Protein Science, 5, 2203-2216, 1996
   • Administrator: Kristoffer Rapacki
   • Done by: Nikolaj Blom (CBS, Copenhagen, Denmark)
   • Email address: rapacki@cbs.dtu.dk
4. Server: chlorop
   • Site (URL): http://www.cbs.dtu.dk/services/ChloroP/
   • About: The ChloroP www-server is able to predict two things:
     • 1. cTP or no cTP
       Whether or not an amino acid sequence contains an N-terminal chloroplast transit peptide, cTP.
     • 2. Cleavage site
       The probable site for cleavage of the transit peptide (if it was predicted to exist in the first step).
     Short instructions for use
     • Include the N-terminus
       It is strongly recommended to include the N-terminus of the submitted sequence. The further from the N-terminal residue the submitted sequence starts, the more difficult and unreliable will the prediction be.
     • Submit preferably 100-150 residues
       Submit if possible at least 100 and no more than 150 N-terminal residues. The lower boundary is due to the fact that the "cTP"/"no cTP" predictor was trained with input sequences of length 100 residues. However, shorter sequences may also be satisfactory predicted (it is more important that the N-terminal part is intact). The cleavage site prediction is in itself not influenced by sequence length, but restricting the submitted length to approximately 150 residues prevents the prediction of too long cTP's.
   • Please quote when using output from chlorop:
     1. O Emanuelsson, H Nielsen, and G von Heijne:ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites. Protein Science, 8, 978-984, 1999
   • Administrator: Kristoffer Rapacki
   • Done by: Olof Emanuelsson (CBS Copenhagen, Denmark)
   • Email address: rapacki@cbs.dtu.dk

Secondary structure prediction

1. Server: jpred
   • Site (URL): http://circinus.ebi.ac.uk:8081/
   • About: A consensus method for protein secondary structure prediction.
   • Please quote when using output from jpred:
     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
   • Administrator: James Cuff
   • Done by: James Cuff, and Geoff Barton (EBI Hinxton, England)
   • Email address: james@ebi.ac.uk
2. Server: psipred
   • Not available, since under construction
   • Site (URL): http://137.205.156.147/psiform.html
   • About:
   • Please quote when using output from psipred:
     1. David Jones (Warwick Univ, England)
   • Administrator: David Jones
   • Done by: DONEBY_4_psipred
   • Email address: jones@globin.bio.warwick.ac.uk

Membrane regions

1. Server: tmhmm
   • Site (URL): http://www.cbs.dtu.dk/services/TMHMM-1.0/
   • About: Hidden Markov Model predicting the location of transmembrane helices and their topology. (Remark Burkhard Rost: find it to be the most original method for this purpose!)
   • Please quote when using output from tmhmm:
     1. ELL Sonnhammer, G von Heijne, and A Krogh:
        A hidden Markov model for predicting transmembrane helices in protein sequences. Proc of the Sixth Intern Conf on Intelligent Systems for Molecular Biology (ISMB98), 175-182, 1998
   • Administrator: Anders Krogh
   • Done by: Anders Krogh (CBS, Copenhagen, Denmark)
   • Email address: krogh@cbs.dtu.dk
2. Server: toppred
   • Site (URL): http://www.biokemi.su.se/~server/toppred2/
   • About: Prediction of location and orientation of transmembrane helices through an advanced use of hydrophobicity patterns, and by applying the 'positive-inside' rule.
   • Please quote when using output from toppred:
     1. G von Heijne:
        Membrane Protein Structure Prediction, Hydrophobicity Analysis and the Positive-inside Rule. J. Molecular Biology, 225, 487-494, 1992
     2. M Cserzo, E Wallin, I Simon, G von Heijne, and A Elofsson:
        Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method. Protein Engineering, 10, 673-676, 1997
   • Administrator: Erik Wallin
   • Done by: Erik Wallin, and Gunnar von Heijne (Stockholm Univ, Sweden)
   • Email address: erikw@biokemi.su.se
3. Server: das
   • Site (URL): http://www.biokemi.su.se/~server/DAS/
   • About: Prediction of location of transmembrane helices through an advanced use of hydrophobicity patterns.
   • Please quote when using output from das:
     1. M Cserzo, E Wallin, I Simon, G von Heijne, and A Elofsson:
        Prediction of transmembrane alpha-helices in procariotic membrane proteins: the Dense Alignment Surface method. Protein Engineering, 10, 673-676, 1997
   • Administrator: Miklos Cserzo
   • Done by: Miklos Cserzo, Istvan Simon (both Academy of Sciences, Budapest, Hungary), Erik Wallin, Gunnar von Heijne, Arne Elofsson (Stockholm Univ, Sweden)
   • Email address: miklos@pugh.bip.bham.ac.uk
4. Server: memsat
   • Not available, since under construction
   • Site (URL): http://137.205.156.147/psiform.html
   • About:
   • Please quote when using output from memsat:
     1. David Jones (Warwick Univ, England) and Willy Taylor (MRC Mill-Hill, London, England)
   • Administrator: David Jones
   • Done by: David Jones (Warwick Univ, England)
   • Email address: jones@globin.bio.warwick.ac.uk

Threading (detecting remote homologues in PDB)

1. Server: frsvr
   • Site (URL): http://www.doe-mbi.ucla.edu/people/fischer/TEST/getsequence.html
   • About: Prediction-based threading, also incorporating purely sequence-based database searches.
   • Options:
     
     • Include H3P2: requests to include the results of the H3P2 prediction.
     • Include PROFILESEARCH: requests to include the results of the PROFILESEARCH program (Smith-Waterman using a profile of the multiple alignment).
   • Please quote when using output from frsvr:
     1. D Fischer, and DA Eisenberg:
        Fold Recognition Using Sequence-Derived Properties. Protein Science, 5, 947-955, 1996
     2. A Elofsson, D Fischer, DW Rice, S LeGrand, and DA Eisenberg:
        Study of Combined Structure-sequence Profiles. Folding and Design, 1, 451-461, 1996
   • Administrator: Daniel Fischer
   • Done by: Daniel Fischer (Ben Gurion Univ of the Negev, Israel)
   • Email address: dfischer@cs.bgu.ac.il
2. Server: samt98
   • Site (URL): http://www.cse.ucsc.edu/research/compbio/HMM-apps/model-library-search.html
   • About: A new hidden Markov model method (SAM-T98) for finding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (HMM) from the sequence and homologs found using the HMM for database search. SAM-T98 is also used to construct model libraries automatically from sequences in structural databases.
   • Please quote when using output from samt98:
     1. Kevin Karplus, Christian Barrett, and Richard Hughey:
        Hidden Markov Models for Detecting Remote Protein HomologiesBioinformatics, 14, 846-856, 1998
   • Administrator: SAM-INFO
   • Done by: Kevin Karplus, Christian Barrett, and Richard Hughey (UCSD, Santa Cruz, USA)
   • Email address: sam-info@cse.ucsc.edu

Homology modelling

1. Server: swissmodel
   • Site (URL): http://www.expasy.ch/swissmod/SWISS-MODEL.html
   • About: SWISS-MODEL is an Automated Protein Modelling Server running at the GlaxoWellcome Experimental Research in Geneva, Switzerland (click here for more details on how the method works).
   • Please quote when using output from SWISSMODEL:
     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.
   • Administrator: Nicolas Guex
   • Done by: Manuel Peitsch, Torsten Schwede, and Nicolas Guex (Glaxo, Geneva, Switzerland)
   • Email address: ng45767@GlaxoWellcome.co.uk
2. 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.
   • Please quote when using output from cphmodels:
     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
   • Administrator: Kristoffer Rapacki
   • Done by: Ole Lund (CBS, Copenhagen, Denmark)
   • Email address: rapacki@cbs.dtu.dk

Formats for submitting sequence or alignment

• simple sequence in one-letter amino acid code (Example)
• sequence in FASTA format (Example)
• sequence in SWISS-PROT format (Example)
• sequence in GCG format (Example)
• sequence in PIR format (Example)
• sequence in DSSP format (Example)
• alignment in FASTA format (Example)
• alignment in SAF format (Example)
• alignment in MSF format (Example)
• alignment in HSSP format (Example)