1. Prediction of Protein Structure and Function on a Proteomic Scale
Jeff Skolnick
Director
Center of Excellence in Bioinformatics

2. General Approach

3. Prediction of Protein Structure

4. Overview of CASP5 Results:

5. Comparative Modeling (CM) Results

6. T0153 CM COORDINATE SUPERPOSITION RMSD = 1.74 Å ( 129 / 134 aa )
NATIVE (discontinuous line) :
PREDICTED (continuous line) :
1mq7 A
rank #1

7. Fold Recognition (FR) results

8. T0135 FR(A) GLOBAL COORDINATE SUPERPOSITION
RMSD = 4.80 Å ( 106 / 106 aa )
NATIVE (discontinuous line) :
PREDICTED (continuous line) :
rank #1

9. T0135 FR(A) GLOBAL COORDINATE SUPERPOSITION
RMSD = 4.80 Å ( 106 / 106 aa )
NATIVE (discontinuous line) :
PREDICTED (continuous line) : rank #1
Yellow line: region originally aligned to the template (1h6kX )

10. New Fold (NF) results

11. T0181
(NF)
PREDICTED: rank #2

13. How representative is the set of solved PDB structures?

14. The PDB is a covering set of protein structures at low resolution
Results from a new structure
alignment program, SAL
Kihara & Skolnick, J. Mol. Biol, 2003:333:

15. Structural alignments to proteins of different secondary structure Different CATH ids
100 residue proteins

17. Use of best structural alignments
Can we build good models starting from protein templates with average sequence id of 7%?

18. TASSER:Threading/ASSEmbly/Refinement

19. Very large scale structure prediction benchmark

20. Comprehensive benchmark set of PDB structures
Length range: 41~200
Sequence identity cut-off: 35%
In total: 1489

21. Summary of Results

22. Summary of Overall Folding Results
SAL
TASSER
MODELLER
Besta
Alignb
Top-5c
Top-1d
<RMSD>e
2.510
1.877
2.246
2.352
2.708
3.740
4.318
<COV>f
82%
100%
NRMSD<6.0
NRMSD<5.5
NRMSD<5.0
NRMSD<4.5
NRMSD<4.0
NRMSD<3.5
NRMSD<3.0
NRMSD<2.5
NRMSD<2.0
NRMSD<1.5
NRMSD<1.0
1489
1485
1472
1440
1369
1255
1064
776
498
218 46
1488
1476
1422
1250
922
411 83
1487
1481
1468
1447
1396
1259
987
623
253 52
1475
1464
1450
1423
1359
1206
928
582
241 49
1462
1431
1395
1336
1141
1008
750
520
244 37
1326
1266
1195
1116
984
834
647
475
300
124 20
1202
1138
1060
962
841
697
551
397 85 15

23. Some Examples:

24. Summary
At low resolution, the PDB is most likely complete for single domain proteins
Can build acceptable full length models in the majority of cases
Can refine the initial structures to move closer to native, even starting from the best structural alignment

25. Results from threading/refinement
“Real Life” situation

26. TASSER:Threading/ASSEmbly/Refinement

27. “Easy” Cases:
At least two threading templates identified with significant consensus region or
One template with z-score that is
highly significant

28. “Medium ” Cases:
At least two threading templates identified without any significant consensus region or
One template with z-score above threshold for correct fold assignment

29. Composite Threading Results
We can identify the correct global fold in 92% of the entire representative set of small PDB structures
Can generate good template alignments in 59% of the cases
Good substructures 67% of the cases

30. Summary of Results

31. Examples of Alignment improvement
Medium
Easy
Template
Final model
Template
Final model
Thin lines: Native; thick lines: Template/model
Two factors mainly contribute to the improvement:
geometric connectivity
Better packing of local structure and side group because of the force field

32. Comparison to Ensemble of NMR Structures
(Predicted Structure to Centroid/Farthest NMR Structure to Centroid)
Thick Line is Predicted Structure

33. Benchmark set of larger proteins (201-300 residues)
487 Single-domain proteins
236 two-domain proteins
22 three-, four-domain proteins
745

34. Successful Predictions of Transmembrane Proteins

35. Application to ORFS <201 residues in E. coli
61% Easy
(829/1360)
38% Medium
(521/1360)
10 Hard
TASSER
68%
(920/1360)
Good models

36. Summary Acceptable model in about 2/3 of the cases (969/1489)
Application to E coli Yields similar results ~2/3 of proteins should have good model -Almost all (90%) have a good template

37. Development of Active Site Descriptors

38. Representation of an Automated Functional Template [ AFT ]
Types of
functional
sites from
SwissProt:
METAL
BINDING
ACT_SITE
SITE cm
SCj cm
SCi
Cai+1
Caj-1
Caj
Cai
Caj+1
Cai-1
Set of distances between: cm
SCk
Cak
Ca atoms and center of mass of the side chains corresponding to 3 to 5 functional residues,
Cak-1
Cak+1
Ca atoms corresponding to the adjacent residues.

39. Specificity parameters of AFTs30 28 26 24 22 20 18 16 14 12 10 8 6 4 2
Positive hits
Negative hits
Restrictive cutoff:
average value of
DRMSDMaxPos and
DRMSDMinNeg.
Permissive cutoff:
expected number of
false positive matchs
is less than in a
random structure.
Number of hits in the subset of PDB
High confidence
DRMSD interval
Low confidence
DRMSD interval
DRMSDMaxPos
DRMSDMinNeg
0.0
0.5
1.0
1.5
2.0
2.5
DRMSD [ Å ]

40. Fraction of decoys correctly annotated vs
Fraction of decoys correctly annotated vs. ranking of the best true positive hit
Global Ca crmsd from the native structure
Local Ca drmsd from the native structure
73%
56%
48%
35%
The recognition by an AFT matching the first three components of the true EC number is considered a true positive hit.

41. Threading of Entire Genomes

42. Summary of Fold Assignments
Organism
Total Protein ORFs
ORF Coverage
(%)
Amino Acid Coverage %
FASTA (%)
PSI-BLAST – PDB (%)
PSI-BLAST – PDBseq (%)
GTOP (%)
Pedant (%)
Gerstein (%) M.
genitalium
484
387 (80.0)
48.1
231 (47.7)
205 (42.4)
259 (53.5)
273 (56.4)
214 (44.2)
E. coli
4289
3356 (78.2)
50.2
1660 (38.7)
1516 (35.3)
1906 (44.4)
2032 (58.5)
1954 (45.6)
1191 (27.8)
B. subtilis
4106
2988 (72.8)
47.2
1465 (35.7)
1314 (32.0)
1732 (42.4)
1947 (60.2)
1963 (47.7)
1121 (27.3)
A. aeolicus
1522
1297 (85.2)
48.0
646 (42.4)
592 (38.9)
771 (50.7)
827 (53.1)
800 (52.6)
527 (34.6) S.
cerevisiae
6343
4610 (72.7)
30.0
1962 (30.9)
1804 (28.4)
2422 (38.2)
2694 (42.5)
2766 (42.9)
1699 (27.3)

43. Comments on fold distribution
Protein folds can be assigned to 72-85% of genes in each genome.
30-50% of the total amino acids in a genome are covered by the assigned folds.
Generally, distribution of folds are similar in the 5 organisms.
Folds of a/b type are abundant.
Folds of multi-functions are abundant in a genome.
Kinase fold shows up in top 5 only in S.cerevisiae.

44. MULTIPROSPECTOR: Prediction of Protein-Protein Interactions
L. Lu, H. Lu, J. Skolnick. Proteins, 2002, 49,

45. Overall Idea of Multimer Threading
Monomer
threading A
X: GELPIAPIGRIIKNA
GAERVSDDARIALAK
VLEEMGEEIASEAVK
LAKHAGRKTIKAEDI
KLARKMFK
Y: GEVPIAPLGRIIKNA
VLEEMGEEIASEAIR
LAKHAGRKTIKAEDV
KLAKKMFK B A B
X: GELPIAPIGRIIKNA
GAERVSDDARIALAK
VLEEMGEEIASEAVK
LAKHAGRKTIKAEDI
KLARKMFK
Y: GEVPIAPLGRIIKNA
VLEEMGEEIASEAIR
LAKHAGRKTIKAEDV
KLAKKMFK X Y
Assign fold on the basis of
Z score and Interface Energy
Multimer
Threading
Multimer Structure Library A B

46. Preliminary test on Known Dimers and Monomers
Homodimers: 58
Heterodimers: 20
Monomers: 96 96 5 91 20 20 54 4 58
Proteins predicted to be dimers
Proteins predicted to be monomers

47. Procedure for genomic scale prediction of protein-protein interactions by MULTIPROSPECTOR

48. Comparison of colocalization index for different methods

49. Distribution of predicted interactions in functional categories

50. Conclusions

51. Completeness of the PDB
Conclusions
Completeness of the PDB
PDB is a covering set of single domain proteins at low to moderate resolution
Protein Structure prediction problem can be solved with more powerful threading algorithms!!

52. TASSER For single domain proteins:
In almost all cases, for all ranges of initial RMSD, even when starting from the “best” structural alignment, the final results are better than the initial template- the models move closer to native
Based on a comprehensive folding benchmark, we expect low resolution structures for ~ 2/3 of proteins with low sequence identity to PDB structures
Weak dependence on secondary structure type

53. Structure to Function
Low resolution structures can be used to identify active sites.
Genome scale threading – greater than 70% of ORFs can be assigned to known folds
Extension to protein-protein interactions
Comparable accuracy to agreement between two experimental methods

54. Acknowledgements http://bioinformatics.buffalo.edu/
Center of Excellence
in Bioinformatics
Yang Zhang
Adrian Arakaki
Purdue University
Daisuke Kihara
Yale University
Long Lu
University of Illinois
Hui Lu
$$$$$
NIH, NSF &
The Oishei Foundation