1. Two methods to predict domain boundary sequence positions from sequence information alone
SnapDRAGON: protein 3D prediction-based
DOMAINATION: based on PSI-BLAST

2. structural domains in sequence data
Combining protein secondary and tertiary structure prediction to predict
structural domains in sequence data
SnapDRAGON
Richard A. George
Jaap Heringa
George, R.A. & Heringa, J. (2002) J.Mol.Biol. 316,
George R.A. and Heringa, J. (2002) J. Mol. Biol., 316,

3. Protein structure evolution
Insertion/deletion of secondary structural elements can ‘easily’ be done at loop sites

4. Flavodoxin family - TOPS diagrams
(Flores et al., 1994) 4 3 2 5 4 3 1 2 5 1

5. Protein structure evolution
Insertion/deletion of structural domains can ‘easily’ be done at loop sites N C

6. A domain is a: Compact, semi-independent unit (Richardson, 1981).
Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).
Recurring functional and evolutionary module (Bork, 1992).
“Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977).

7. The DEATH Domain
Present in a variety of Eukaryotic proteins involved with cell death.
Six helices enclose a tightly packed hydrophobic core.
Some DEATH domains form homotypic and heterotypic dimers.

8. Delineating domains is essential for:
Obtaining high resolution structures (x-ray, NMR)
Sequence analysis
Multiple sequence alignment methods
Prediction algorithms (SS, Class, secondary/tertiary structure)
Fold recognition and threading
Elucidating the evolution, structure and function of a protein family (e.g. ‘Rosetta Stone’ method)
Structural/functional genomics
Cross genome comparative analysis

9. Structural domain organisation can be nasty…
Pyruvate kinase
Phosphotransferase
b barrel regulatory domain
a/b barrel catalytic substrate binding domain
a/b nucleotide binding domain
1 continuous + 2 discontinuous domains

10. Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
TERTIARY STRUCTURE (fold)
QUATERNARY STRUCTURE

11. Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
TERTIARY STRUCTURE (fold)
QUATERNARY STRUCTURE

12. Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
TERTIARY STRUCTURE (fold)
QUATERNARY STRUCTURE

13. Protein structure hierarchical levels
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
TERTIARY STRUCTURE (fold)
QUATERNARY STRUCTURE

14. Distance Regularisation Algorithm for Geometry OptimisatioN
Domain prediction using DRAGON
Distance Regularisation Algorithm for Geometry OptimisatioN
(Aszodi & Taylor, 1994)
Folds proteins based on the requirement that (conserved) hydrophobic residues cluster together.
First constructs a random high dimensional Ca distance matrix.
Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues.

15. The DRAGON target matrix is inferred from:
A multiple sequence alignment of a protein (old)
Conserved hydrophobicity
Secondary structure information (SnapDRAGON)
predicted by PREDATOR (Frishman & Argos, 1996).
strands are entered as distance constraints from the N-terminal Ca to the C-terminal Ca.

16. Predicted secondary structure
Multiple alignment
C distance
matrix
Target
matrix
Predicted secondary structure N N 3 N N
100 randomised
initial matrices
100 predictions
CCHHHCCEEE
Input data N
The C distance matrix is divided into smaller clusters.
Seperately, each cluster is embedded into a local centroid.
The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures.

17. Predicted secondary structure
SnapDragon
Generated folds by Dragon
Multiple alignment
Boundary recognition
Predicted secondary structure
CCHHHCCEEE
Summed and Smoothed Boundaries

18. SnapDRAGON
Domains in structures assigned using method by Taylor (1997) 1 2 3
Domain boundary positions of each model against sequence
Summed and Smoothed Boundaries (Biased window protocol)

19. Prediction assessment
Test set of 414 multiple alignments;183 single and 231 multiple domain proteins.
Sequence searches using PSI-BLAST (Altschul et al., 1997) followed by redundancy filtering using OBSTRUCT (Heringa et al.,1992) and alignment by PRALINE (Heringa, 1999)
Boundary predictions are compared to the region of the protein connecting two domains (min 10 residues)

20. Average prediction results per protein
Coverage is the % linkers predicted (TP/TP+FN)
Success is the % of correct predictions made (TP/TP+FP)

21. SnapDRAGON
Is very slow (can be hours for proteins>400 aa) – cluster computing implementation
Uses consistency in the absence of standard of truth
Goes from primary+secondary to tertiary structure to ‘just’ chop protein sequences
SnapDRAGON webserver is underway

22. DOMAINATION Integrating protein sequence database
searching and domain recognition
DOMAINATION
Richard A. George
Protein domain identification and improved sequence searching using PSI-BLAST
(George & Heringa, Prot. Struct. Func. Genet., in press; 2002)

23. Domaination
Current iterative homology search methods do not take into account that:
Domains may have different ‘rates of evolution’.
Common conserved domains, such as the tyrosine kinase domain, can obscure weak but relevant matches to other domain types
Premature convergence (false negatives)
Matrix migration / Profile wander (false positives).

24. PSI-BLAST
Query sequence is first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition (e.g. TM regions or coiled coils) likely to lead to spurious hits, which are excluded from alignment.
Initially operates on a single query sequence by performing a gapped BLAST search
Then takes significant local alignments found, constructs a ‘multiple alignment’ and abstracts a position specific scoring matrix (PSSM) from this alignment.
Rescans the database in a subsequent round to find more homologous sequences -- Iteration continues until user decides to stop or search converges

25. PSI-BLAST iteration Q Q Database hits PSSM PSSM Database hits
Query sequence
xxxxxxxxxxxxxxxxx
Gapped BLAST search Q
Query sequence
xxxxxxxxxxxxxxxxx
Database hits A C D . Y
PSSM Pi Px
Gapped BLAST search A C D . Y
PSSM Pi Px
Database hits

26. DOMAINATION
Chop and Join
Domains

27. Post-processing low complexity
Remove local fragments with > 15% LC

28. Identifying domain boundaries
Sum N- and C-termini of
gapped local alignments
True N- and C- termini are
counted twice (within 10 residues)
Boundaries are smoothed using two
windows (15 residues long)
Combine scores using biased
protocol:
if Ni x Ci = 0
then Si = Ni+Ci
else Si = Ni+Ci +(NixCi)/(Ni+Ci)

29. Identifying domain deletions
Deletions in the query (or insertion in the DB sequences) are identified by
two adjacent segments in the query align to the same DB sequences (>70% overlap), which have a region of >35 residues not aligned to the query (remove N- and C- termini) DB
Query

30. Identifying domain permutations
A domain shuffling event is declared
when two local alignments (>35 residues) within a single DB sequence match two separate segments in the query (>70% overlap), but have a different sequential order.
b a DB
Query
a b

31. Identifying continuous and discontinuous domains
Each segment is assigned an independence score (In).
If In>10% the segment is assigned as a continuous domain.
An association score is calculated between non-adjacent
fragments by assessing the shared sequence hits to the
segments. If score > 50% then segments are considered as
discontinuous domains and joined.

32. Create domain profiles
A representative set of the database sequence fragments that overlap a putative domain are selected for alignment using OBSTRUCT (Heringa et al. 1992) > 20% and < 60% sequence identity (including the query seq).
A multiple sequence alignment is generated using PRALINE (Heringa 1999).
Each domain multiple alignment is used as a profile in further database searches using PSI-BLAST (Altschul et al 1997).
The whole process is iterated until no new domains are identified.

33. Domain boundary prediction accuracy
Set of 452 multidomain proteins
56% of proteins were correctly predicted to have more than one domain
42% of predictions are within 20 residues of a true boundary
49.9% (44.6%) correct boundary predictions per protein

34. For discontinuous proteins 34.2% of linkers were identified
23.3% of all linkers found in 452 multidomain proteins. Not a surprise since:
Structural domain boundaries will not always coincide with sequence domain boundaries
Proteins must have some domain shuffling
For discontinuous proteins 34.2% of linkers were identified
30% of discontinuous domains were successfully joined

35. Change in domain prediction accuracy using
various PSI-BLAST E-value cut-offs

36. Benchmarking versus PSI-BLAST
A set 452 non-homologous multidomain protein structures.
Each protein was delineated into its structural domains. Database searches of the individual domains were used as a standard of truth.
We then tested to what extent PSI-BLAST and DOMAINATION, when run on the full-length protein sequences, can capture the sequences found by the reference PSI-BLAST searches using the individual domains.

37. Two sets based on individual domain searches:
Reference set 1: consists of database sequences for which PSI-BLAST finds all domains contained in the corresponding full length query.
Reference set 2: consists of database sequences found by searching with one or more of the domain sequences
Therefore set 2 contains many more sequences than set 1
Ref set Ref set 2
Query
DB seqs

38. Sequences found over Reference sets 1 and 2

39. Reference 1 Reference 2 PSI-BLAST finds 97.9% of sequences
Domaination finds 99.1% of sequences
Reference 2
PSI-BLAST finds 83.2% of sequences
Domaination finds 90.6% of sequences

40. Sequences found over Reference sets 1 and 2 from 15 Smart sequences

41. SSEARCH significance test
Verify the statistical significance of database sequences found by relating them to the original query sequence.
SSEARCH (Pearson & Lipman 1988). Calculates an E-value for each generated local alignment.
This filter will lose distant homologies.
Use the 452 proteins with known structure.

42. Significant sequences found in database searches
At an E-value cut-off of 0.1 the performance of DOMAINATION
searches with the full-length proteins is 15% better than PSI-BLAST

43. Summary
Domains are recurring evolutionary units: by collecting the N- and C- termini of local alignments we can identify domain boundaries.
By finding domains we can significantly improve database search methods
SnapDRAGON is more sensitive than DOMAINATION but at high computational cost