Protein Fold recognition

Morten Nielsen,Thomas Nordahl

CBS, BioCentrum, DTU

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

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).

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

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

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

Human genome~ 30.000 proteins

Homology modeling and the human genome

Swiss-Prot database

~200.000 in Swiss-Prot~ 2.000.000 if include Tremble

PDB New Fold Growth

New folds

Old folds

New

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PDB New Fold GrowthN

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PD

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PDB New Fold GrowthN

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Identification of fold

Rajesh Nair & Burkhard Rost Protein Science, 2002, 11, 2836-47

Why %id is so bad!!

1200 models sharing 25-95% sequence identity with the submitted sequences

(www.expasy.ch/swissmod)

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

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 Score

P(S

core

)

Score 15010 hits with higher score (E=10)10000 hits in database => P=10/10000 = 0.001

Protein classifications

Protein world

Protein fold

Protein structure classification

Protein superfamily

Protein familyNew Fold

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

Family

Superfamily

Proteins

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)

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

TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

Sequence profiles/blosum62 scores

a)TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP

b)TKAVVLTFNTSVEICLVMQ-GTSIVAAESHPLHLHGFNFPSNFNLVDPMERNTAGVP

Which alignment is most correct a) or b) ?

Blosum62 scores:G-G: 6H-H: 8

TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

Blosum scoring matrix

A R N D C Q E G H I L K M F P S T W Y VA 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASKISMSEEDLLN TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWHGLLVPFGMDGVPGVSFPG---I-TSMAPAFGVQEFYRTVKQGDEVTVTIT-----NIDQIED-VSHGFVVVNHGVSME---IIE--KMKYLTPEVFYTIKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD----TSVAPSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAME---VASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPEGVEGFKSRINDE----TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI

Sequence profiles

Conserved

Non-conserved

Matching any thing but G => large negative score

Any thing can match

TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP

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!)

Example. Sequence profiles

• Alignment of protein sequences 1PLC._ and 1GYC.A– E-value > 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 < 10-22. Rmsd=3.3

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------VFKNNAGFPHNIVFDEDSIPSGVDASKIS 56 F + G++ N+ + +G + +1GYC.A: 26 ------VFPSPLITGKKGDRFQLNVVDTLTNHTMLKSTSIHWHGFFQAGTNWADGP 79 1PLC._: 57 MSEEDLLNAKGETFEVAL---SNKGEYSFYCSP--HQGAGMVGKVTV 98 A G +F G + ++ G+ G V1GYC.A: 80 AFVNQCPIASGHSFLYDFHVPDQAGTFWYHSHLSTQYCDGLRGPFVV 126

Rmsd=3.3 ÅStructure redTemplate blue

Sequence logo / Sequence profile

0 iterations (Blosum62)

2 iterations

1 iterations

3 iterations

Profile-profile alignmentQ

uery

Tem

pla

te

Compare amino acid preference for the two proteins and pair similar

positions(HHpred)

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

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

CASP6 results

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!

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

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

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/

Meta Server

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

Modeling of new protein folds

• Only when everything else fails• Challenge

• Close to impossible to model Natures folding potential

Ab initio protein modeling

Fragments with correct local structure

Natures potential

Empirical potential

A way to solution

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

Example (Rosetta web server)

Rosetta predictionStructure

www.bioinfo.rpi.edu/~bystrc/hmmstr/server.php

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