Fold Recognition

Ole Lund,

Associate professor,

CBS

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Fold recognition

Find template for modeling– 1st step in comparative modeling

Can be used to predict function

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Template identification

Search with sequence– Blast against proteins with known structure– Psi-Blast against all proteins– Fold recognition methods

Use biological information Functional annotation in databases Active site/motifs

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Blast derivatives: PDB-BLAST

Procedure1. Build sequence profile by iterative PSI-BLAST

search against a sequence database

2. Use profile to search database of proteins with known structure

Advantage– Makes sure hid to protein with known structure is

not hidden behind a lot of hits to other proteins

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BLAST derivatives: Transitive BLAST

Procedure1. Find homologues to query (your) sequence

2. Find homologues to these homologues

3. Etc.– Can be implemented with e.g. BLAST or PSI-

BLAST

Also known as Intermediate Sequence Search (ISS)

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CASP

CASP– Critical Assessment of Structure Predictions– Every second year– Sequences from about-to-be-solved-structures

are given to groups who submit their predictions before the structure is published

– Modelers make prediction– Meeting in Asilomar where correct answers are

revealed

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Target difficulty

CM: Comparative (homology) modeling CM/FR: not PSI-BLAST (but ISS) findable FR(H): Homologous fold recognition FR(A): Analogous fold recognition NF/FR: Partly New fold NF: New Fold (used to be called Ab Initio -

from first principles- prediction)

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CASP5 overview

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Successful fold recognition groups at CASP5

3D-Jury (Leszek Rychlewski) 3D-CAM (Krzysztof Ginalski) Template recombination (Paul Bates) HMAP (Barry Honig) PROSPECT (Ying Xu) ATOME (Gilles Labesse)

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Barry Honig

Sequence&structure profile-profile based alignment– Database of template profiles

Multiple structure alignment Sequence based profiles Position specific gap penalties derived from secondary

structure Calibration to estimate statistical significance

– Query profile Sequence based profile Predicted secondary structure (consensus between PSI-

PRED,PHD,JNET)

Abstract

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Ying Xu

PROSPECT:optimal alignments for a given energy function with any combination of the following terms: 1. mutation energy (including position-specific score

matrix derived from multiple-sequence alignments),

2. singleton energy (including matching scores to the predicted secondary structures),

3. pairwise contact potential

4. alignment gap penalties.

Abstract

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3D-Jury (Rychlewski)

Inspired by Ab initio modeling methods– Average of frequently obtained low energy structures is

often closer to the native structure than the lowest energy structure

Find most abundant high scoring models1. Use output from a set of servers2. Superimpose all pairs of structures3. Similarity score Sij = # of C pairs within 3.5Å

(if #>40;else Sij=0)4. 3D-Jury score = iSij/(N+1)

Similar methods developed by A Elofsson (Pcons) and D Fischer (3D shotgun)

Rychlewski.doc

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3D-CAM (Krzysztof Ginalski)

3D-Consensus Alignment Method– Structural alignment for all members of fold from FSSP– Conservation of specific residues and contacts

responsible for maintaining tertiary structure critical for substrate binding and/or catalysis

– Find homologues with iterative PSI-BLAST– Align with ClustalW – identify conserved residues– Structural integrity of alignments– Manual realignment– Fold recognition for homologues– Modelling– Verification

Visually Computationally (Verify3D, ProsaII, WHAT_CHECK)

Ginalski.doc

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Paul A Bates - In Silico Recombination of Templates, Alignments and Models

Problems– Models rarely better than templates– Manual intervention have marginal effect

Possible solution– Recombination of models

Abstract

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Paul A Bates – Modelling Procedure

Define domains Make models (FAMS/Pmodeller/EsyPred3D)

– Manual inspection/correction of alignments– Alignment of annotated residues (PFAM)– Preferably use alignment with >2 bits/aa

Select pair of models– Superimpose– Crossover or mutate (average coordinates)

Select best proportion– Contact pair potentials– Solvation energies (calculated from solvent accessible area)

Convergence– Minimization and final refinements

Abstract

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LiveBench

The Live Bench Project is a continuous benchmarking program. Every week sequences of newly released PDB proteins are being submitted to participating fold recognition servers. The results are collected and continuous evaluated using automated model assessment programs. A summary of the results is produced after several months of data collection. The servers must delay the updating of their structural template libraries by one week to participate.

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Meta Server

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Meta Server

http://bioinfo.pl/meta/target.pl?id=7296

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Score

# correct

# wrong

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Best servers?

FFA3 3DS5 INBG SHUM 3DPS 3DS3 FUG3 SHGU FUG2 PCO2 PRO2 MGTH SFPP PMO3

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Links to fold recognition servers

Databases of links– http://bioinfo.pl/meta/servers.html – http://mmtsb.scripps.edu/cgi-bin/renderrelres?protmodel

Meta server– http://bioinfo.pl/meta/ (Example: http://bioinfo.pl/meta/target.pl?id=7296 )

3DPSSM – good graphical output– http://www.sbg.bio.ic.ac.uk/servers/3dpssm/

GenTHREADER– http://bioinf.cs.ucl.ac.uk/psipred/

FUGUE2– http://www-cryst.bioc.cam.ac.uk/~fugue/prfsearch.html

SAM– http://www.cse.ucsc.edu/research/compbio/HMM-apps/T99-query.html

FOLD– http://fold.doe-mbi.ucla.edu/

FFAS/PDBBLAST– http://bioinformatics.burnham-inst.org/