Template Detection

Michael Tress
Protein Design Group.
Centro Nacional de Biotecnologia (C.N.B.- C.S.I.C.)
Tlf: +34-91-5854570. Fax: +34-91-5854506.
Madrid-May-05

You'll find here

1.- A short Introduction
2.- Prior steps .
3.- Some programs available for detecting structural templates.
4.- An example of a fold recognition procedure.

Introduction

 :::: [BACK TO TOP]


                Fold recognition and hybrid sequence methods are structure prediction techniques that can be used when the twilight zone is reached (meaning there is less than 25% of identity between 2 proteins in a pair-wise alignment). In this case the direct homology modelling approach is often unreliable. "Threading" methods align the target sequence with folds to evaluate the structural fit of the sequence with the folds. Other fold recognition methods use a variety of techniques to go much deeper into the twilight zone than pairwise sequence search methods. The strategies vary between fold recognition programs. They might use for instance secondary structure coincidence or accesibility, or solvatation energy, etc.

            In general, methods of protein fold recognition attempt to detect similarities between 3D features in proteins that show no sequence similarity. Hybrid methods are generally based on sequence pofiles and may include information such as secondary structure prediction to improve their alignments. Unlike sequence-only methods, most fold detection methods use additional structural information from 3D data, allowing them to more accurately predict how well the target sequence fits each fold.


Steps to follow prior to fold detection

.:::: [BACK TO TOP]


Features to consider Method
1.- Does the secondary structure of the protein help to evaluate the fold recognition results? Check 1D characteristics
2.- The classification/comparison 3D structure databases could help to analyse the fold recognition results. 3D databases 3D



An Example Procedure


:::: [BACK TO TOP]
Template Detection Procedure EXAMPLE: HYPOTHETICAL FLAVIN REDUCTASE



Some Template Detection programs:

:::: [BACK TO TOP]


Servers Other Useful Servers

Fold Recognition Servers

  1. 123D+ Combines substitution matrix, secondary structure prediction, and contact capacity potentials.
  2. 3D-PSSM (ICNET). Based on sequence profiles, solvatation potentials and secondary structure.
  3. Fugue 2.0
    4. FUGUE recognises remote homologues by using environment-specific substitution tables and local environment-dependent gap penalties.
  4. GenTHREADER/mGenTHREADER (UCL) GenTHREADER uses a neural network along with contact potentials amd solvation energies.
  5. PHYRE A new fragment-based server.
  6. Raptor - you have to send an email to warn them first
  7. ROBETTA ROBETTA detects fragments with BLAST, FFAS03, or 3DJury, generates alignments with its own K*SYNC method and uses fragment insertion and assembly.
  8. SPARKS2 Sequence, secondary structure Profiles And Residue-level Knowledge-based Score for fold recognition.
  9. SPARKS3 Independent tests suggest that SPARKS3 is better than SPARKS2 for harder targets and vice versa for easier targets.
  10. UCLA-DOE Structure Prediction Server (UCLA). Executes various fold recognition programs and reports a consensus.

Sequence-Based and Hybrid Servers

  1. FFAS03 (Burnham Institute). Based on profile-profile matching algorithms of the query sequence with sequences from clustered PDB database.
  2. META-BASIC Based on consensus alignments of profiles. Combines sequence profiles with predicted secondary structure.
  3. PSI-BLAST The most basic sequence-based detection server, iterated BLAST. Click on the PSI-BLAST link.
  4. Sam-T02 Based on Markov models of alignments of crystalized proteins.
  • PSIPRED (UCL) You can also go here for secondary structure prediction.
  • SQUARE (CNB) A method for evaluatuing alignments with template proteins.
  • CA-GEN (CNB) A method for generating 3D backbones from alignments with template proteins.