Protein Fold recognition

1
Protein Fold recognition

• Morten Nielsen,
• Thomas Nordahl
• CBS, BioCentrum,
• DTU

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IntroductionWhat is a protein fold

• Protein fold
• Protein sequence id
• Protein sequence/structure databases
• Alignment values
• Scores, E-values P-values
• Protein classifications
• Fold, Superfamily, Family protein

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IntroductionWhat is a protein fold

• A protein fold is the scaffold that can be used
  as a template to model a query protein sequence.
• Fold recognition is technique that is used to
  identify the scaffold to be used, from a known
  protein structure. The sequence similarity is low
  and therefore the fold is difficult to recognize
  by use of simple sequence alignment tools
  (blosum62 matrix).

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Outline

• Many textbooks and experts state that ID is the
  only determining factor for successful homology
  modeling
• This is WRONG!
• ID is a very poor measure to determine if a
  protein can be modeled
• Many sequences with sequence homology 10-15 can
  be accurately modeled

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Outline

• Why homology modeling
• How is it done
• How to decide when to use homology modeling
• Why is id such a terrible measure
• What are the best methods

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Why protein modeling?

• Because it works!
• Close to 50 of all new sequences can be homology
  modeled
• Experimental effort to determine protein
  structure is very large and costly
• The gap between the size of the protein sequence
  data and protein structure data is large and
  increasing

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Homology modeling and the human genome
Human genome 30.000 proteins
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Swiss-Prot database
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PDB New Fold Growth
Old folds
New PDB structures
New folds
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PDB New Fold Growth
New PDB structures
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PDB New Fold Growth
New PDB structures
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Identification of fold
Rajesh Nair Burkhard Rost Protein Science,
2002, 11, 2836-47
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Why id is so bad!!
1200 models sharing 25-95 sequence identity with
the submitted sequences (www.expasy.ch/swissmod)
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Identification of correct fold

• ID is a poor measure
• Many evolutionary related proteins share low
  sequence homology
• Alignment score even worse
• Many sequences will score high against every
  thing (hydrophobic stretches)
• P-value or E-value more reliable

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What are P and E values?

• E-value
• Number of expected hits in database with score
  higher than match
• Depends on database size
• P-value
• Probability that a random hit will have score
  higher than match
• Database size independent

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Protein classifications
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Protein structure classification
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Superfamilies

• Proteins which are (remote) evolutionarily
  related
• Sequence similarity low
• Share function
• Share special structural features
• Same evolutionary ancestor
• Relationships between members of a superfamily
  may not be readily recognizable from the sequence
  alone

Fold
Superfamily
Family
Proteins
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Template identification

• Simple sequence based methods
• Align (BLAST) sequence against sequence of
  proteins with known structure (PDB database)
• Sequence profile based methods
• Align sequence profile (Psi-BLAST) against
  sequence of proteins with known structure (PDB)
• Align sequence profile against profile of
  proteins with known structure (FFAS)
• Sequence and structure based methods
• Align profile and predicted secondary structure
  against proteins with known structure (3D-PSSM)

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Sequence profiles

• In conventional alignment, a scoring matrix
  (BLOSUM62) gives the score for matching two amino
  acids
• In reality not all positions in a protein are
  equally likely to mutate
• Some amino acids (active cites) are highly
  conserved, and the score for mismatch must be
  very high
• Other amino acids can mutate almost for free, and
  the score for mismatch is lower than the BLOSUM
  score
• Sequence profiles (just like a HMM) can capture
  these differences

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Sequence profiles/blosum62 scores
a)TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNL
VDPMERNTAGVP
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWAD
GPAYVTQCPI
b)TKAVVLTFNTSVEICLVMQ-GTSIVAAESHPLHLHGFNFPSNFNLVDP
MERNTAGVP
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWAD
GPAYVTQCPI
Which alignment is most correct a) or b)
? Blosum62 scores G-G 6 H-H 8
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Blosum scoring matrix
A R N D C Q E G H I L K M F P
S T W Y V A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1
-1 -1 -2 -1 1 0 -3 -2 0 R -1 5 0 -2 -3 1 0
-2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 N -2 0 6
1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2
-3 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1
0 -1 -4 -3 -3 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1
-3 -1 -2 -3 -1 -1 -2 -2 -1 Q -1 1 0 0 -3 5 2
-2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 E -1 0 0
2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2
-2 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2
0 -2 -2 -3 -3 H -2 0 1 -1 -3 0 0 -2 8 -3 -3
-1 -2 -1 -2 -1 -2 -2 2 -3 I -1 -3 -3 -3 -1 -3 -3
-4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 L -1 -2 -3
-4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1
1 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1
0 -1 -3 -2 -2 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2
-1 5 0 -2 -1 -1 -1 -1 1 F -2 -3 -3 -3 -2 -3 -3
-3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 P -1 -2 -2
-1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3
-2 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1
4 1 -3 -2 -2 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1
-1 -1 -2 -1 1 5 -2 -2 0 W -3 -3 -4 -4 -2 -2 -3
-2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 Y -2 -2 -2
-3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7
-1 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2
-2 0 -3 -1 4
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Sequence profiles
ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASK
ISMSEEDLLN TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWH
GLLVPFGMDGVPGVSFPG---I -TSMAPAFGVQEFYRTVKQGDEVTVTI
T-----NIDQIED-VSHGFVVVNHGVSME---I IE--KMKYLTPEVFYT
IKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD--- -TSVA
PSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAM
E---V ASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPE
GVEGFKSRINDE---- TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAE
NITIHWHGVQLGTGWADGPAYVTQCPI
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWAD
GPAYVTQCPI
TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVD
PMERNTAGVP
Matching any thing but G gt large negative score
Any thing can match
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Sequence profiles

• Align (BLAST) sequence against large sequence
  database (Swiss-Prot)
• Select significant alignments and make profile
  (weight matrix) using techniques for sequence
  weighting and pseudo counts
• Use weight matrix to align against sequence
  database to find new significant hits
• Repeat 2 and 3 (normally 3 times!)

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Example. Sequence profiles

• Alignment of protein sequences 1PLC._ and 1GYC.A
• E-value gt 1000
• Profile alignment
• Align 1PLC._ against Swiss-prot
• Make position specific weight matrix from
  alignment
• Use this matrix to align 1PLC._ against 1GYC.A
• E-value lt 10-22. Rmsd3.3

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Sequence profiles

• Score 97.1 bits (241), Expect 9e-22
• Identities 13/107 (12), Positives 27/107
  (25), Gaps 17/107 (15)
• 1PLC._ 3 ADDGSLAFVPSEFSISPGEKI------VFKNNAGFPHN
  IVFDEDSIPSGVDASKIS 56
• F G N
  G
• 1GYC.A 26 ------VFPSPLITGKKGDRFQLNVVDTLTNHTMLKST
  SIHWHGFFQAGTNWADGP 79
• 1PLC._ 57 MSEEDLLNAKGETFEVAL---SNKGEYSFYCSP--HQG
  AGMVGKVTV 98
• A G F G
  G G V
• 1GYC.A 80 AFVNQCPIASGHSFLYDFHVPDQAGTFWYHSHLSTQYC
  DGLRGPFVV 126

Rmsd3.3 Å Structure red Template blue
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Sequence logo / Sequence profile
0 iterations (Blosum62)
1 iterations
3 iterations
2 iterations
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Profile-profile alignment
Query
Template
Compare amino acid preference for the two
proteins and pair similar positions (HHpred)
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Including structure

• Sequence within a protein superfamily share
  remote sequence homology
• , but they share high structural homology
• Structure is known for template
• Predict structural properties for query
• Secondary structure
• Surface exposure
• Position specific gap penalties derived from
  secondary structure and surface exposure

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Using structure

• Sequencestructure profile-profile based
  alignments
• Template profiles
• Multiple structure alignments
• Sequence based profiles
• Query profile
• Sequence based profile
• Predicted secondary structure
• Position specific gap penalties derived from
  secondary structure

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CASP. Which are the best methods

• 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 December where correct answers are
  revealed

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CASP6 results
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The top 4 homology modeling groups in CASP6

• All winners use consensus predictions
• The wisdom of the crowd
• Same approach as in CASP5!
• Nothing has happened in 2 years!

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The wisdom of the crowd!

• Why the many are smarter than the few
• A general method useful to improve prediction
  accuracy
• No single method or expert will always be the
  best

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The wisdom of the crowd!

• The highest scoring hit will often be wrong
• Not one single prediction method is consistently
  best
• Many prediction methods will have the correct
  fold among the top 10-20 hits
• If many different prediction methods all have
  same fold among the top hits, this fold is
  probably correct

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How to do it? Where is the crowd

• Meta prediction server
• Web interface to a list of public protein
  structure prediction servers
• Submit query sequence to all selected servers in
  one go
• http//bioinfo.pl/meta/

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Meta Server
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From fold to structure

• Flying to the moon has not made man conquer space
• Finding the right fold does not allow you to make
  accurate protein models
• Can allow prediction of protein function
• Alignment is still a very hard problem
• Most protein interactions are determined by the
  loops, and they are the least conserved parts of
  a protein structure

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Ab initio protein modeling
Modeling of new protein folds

• Only when everything else fails
• Challenge
• Close to impossible to model Natures folding
  potential

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A way to solution

• Glue structure piece wise from fragments.
• Guide process by empirical/statistical potential

Fragments with correct local structure
Natures potential
Empirical potential
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Example (Rosetta web server)
www.bioinfo.rpi.edu/bystrc/hmmstr/server.php
Rosetta prediction
Structure
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Take home message

• Identifying the correct fold is only a small step
  towards successful homology modeling
• Do not trust ID or alignment score to identify
  the fold. Use p-values
• Use sequence profiles and local protein structure
  to align sequences
• Do not trust one single prediction method, use
  consensus methods (3D Jury)
• Only if everythings fail, use ab initio methods