1. Protein Fold recognition
Morten Nielsen,
CBS, BioCentrum,
DTU

2. %ID is a very poor measure to determine if a protein can be modeled
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

3. How to decide when to use homology modeling
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

4. 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

5. Homology modeling and the human genome
~ proteins

6. Swiss-Prot database ~200.000 in Swiss-Prot
~ if include Tremble

7. PDB New Fold Growth Old folds New folds
New PDB structures
New folds
The number of unique folds in nature is fairly small (possibly a few thousands)
90% of new structures submitted to PDB in the past three years have similar structural folds in PDB

8. Identification of fold
If sequence similarity is high proteins share structure (Safe zone)
If sequence similarity is low proteins may share structure (Twilight zone)
Most proteins do not have a high sequence homologous partner
Rajesh Nair & Burkhard Rost Protein Science, 2002, 11,

9. Why %id is so bad!!
1200 models sharing 25-95% sequence identity with the submitted sequences (

10. 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

11. What are P and E values? E-value P-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(Score)
Score 150
10 hits with higher score (E=10)
10000 hits in database => P=10/10000 = 0.001

12. How to do it Identify fold (template) for modeling
Find the structure in the PDB database that resembles your new protein the most
Can be used to predict function
Align protein sequence to template
Simple alignment methods
Sequence profiles
Threading methods
Pseudo force fields
Model side chains and loops

13. Protein structure classification
Protein world
Protein superfamily
Protein fold
Protein family
New Fold

14. Superfamilies Proteins which are (remote) evolutionarily related Fold
Sequence similarity low
Share function
Share special structural features
Relationships between members of a superfamily may not be readily recognizable from the sequence alone
Fold
Superfamily
Family
Proteins

15. 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)

16. 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

17. Sequence profiles G-G: 6 H-H: 8
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI
TKAVVLTFNTSVEICLVMQGTSIVAAESHPLHLHGFNFPSNFNLVDPMERNTAGVP
a)TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP
b)TKAVVLTFNTSVEICLVMQ-GTSIVAAESHPLHLHGFNFPSNFNLVDPMERNTAGVP
G-G: 6
H-H: 8

18. Sequence profiles Matching any thing but G => large negative score
Conserved
Non-conserved
ADDGSLAFVPSEF--SISPGEKIVFKNNAGFPHNIVFDEDSIPSGVDASKISMSEEDLLN
TVNGAI--PGPLIAERLKEGQNVRVTNTLDEDTSIHWHGLLVPFGMDGVPGVSFPG---I
-TSMAPAFGVQEFYRTVKQGDEVTVTIT-----NIDQIED-VSHGFVVVNHGVSME---I
IE--KMKYLTPEVFYTIKAGETVYWVNGEVMPHNVAFKKGIV--GEDAFRGEMMTKD---
-TSVAPSFSQPSF-LTVKEGDEVTVIVTNLDE------IDDLTHGFTMGNHGVAME---V
ASAETMVFEPDFLVLEIGPGDRVRFVPTHK-SHNAATIDGMVPEGVEGFKSRINDE----
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI
TVNGQ--FPGPRLAGVAREGDQVLVKVVNHVAENITIHWHGVQLGTGWADGPAYVTQCPI
TKAVVLTFNTSVEICLVMQGTSIV----AAESHPLHLHGFNFPSNFNLVDPMERNTAGVP
Matching any thing but G => large negative score
Any thing can match

19. 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!)

20. 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 < Rmsd=3.3

21. Sequence profiles Score = 97.1 bits (241), Expect = 9e-22 Rmsd=3.3 Å
Identities = 13/107 (12%), Positives = 27/107 (25%), Gaps = 17/107 (15%)
1PLC._: 3 ADDGSLAFVPSEFSISPGEKI------VFKNNAGFPHNIVFDEDSIPSGVDASKIS 56
F G N G + +
1GYC.A: VFPSPLITGKKGDRFQLNVVDTLTNHTMLKSTSIHWHGFFQAGTNWADGP 79
1PLC._: 57 MSEEDLLNAKGETFEVAL---SNKGEYSFYCSP--HQGAGMVGKVTV 98
A G +F G G+ G V
1GYC.A: 80 AFVNQCPIASGHSFLYDFHVPDQAGTFWYHSHLSTQYCDGLRGPFVV 126
Rmsd=3.3 Å
Structure red
Template blue

22. Profile-profile alignment
Query
Template
Compare amino acid preference for the two proteins and pair similar positions
(HHpred)

23. Including structure
Sequence with in 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

24. Using structure Sequence&structure 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

25. Structure biased alignment (3D-PSSM)

26. Threading Alignment score from structural fitness (pair potential)
How well does K fit environment at P6?
If P8 is acidic then fine, if P8 is basic then poor
Deletions 7 4 6 2 5 8 10 9 1 3
.. A T N L Y K E T L ..
Insertion

27. Threading does not work!
The average protein does not exist
Threading can be used in combination with sequence profiles, local structural features to improve alignment

28. 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

29. CASP6 results

30. 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!

31. 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

32. 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 hits
If many different prediction methods all have some fold among the top hits, this fold is probably correct

33. 3D-Jury (Best group) 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 model in a list of prediction from several predictors
Use output from a set of servers
Superimpose all pairs of structures
Similarity score Sij = # of Ca pairs within 3.5Å (if #>40;else Sij=0)
3D-Jury score = SijSij/(N+1)
Similar methods developed by A Elofsson (Pcons) and D Fischer (3D shotgun)

34. 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

35. Meta server. 3d-Jury

36. Meta Server

37. Flying to the moon has not made man conquer space
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

38. Ab initio protein modeling
Modelling of new fold proteins
Only when every thing else fails
Challenge
Close to impossible to model Natures folding potential

39. Challenge. Folding potential
New folds are in general constructed from a set of subunits, where each subunit is part of a known fold.
The subunits are small compared to the overall fold of the protein. No objective function exists to guide the global packing of the subunits.
Objective function
sij = 120aa
dij = 6Å
Nazca lines in Peru

40. Fragments with correct local structure
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

41. Example (Rosetta web server)
Rosetta prediction
Structure

42. 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 every things fail, use ab initio methods

43. Examples
Iterative Blast
Sequence

44. Examples
HHpred