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Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard Hughes Medical Institute Department of Biochemistry and Molecular Biophysics Center for Computational Biology and Bioinformatics Columbia University

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Page 1: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Using structure in protein function annotation:

predicting protein interactions

Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig

Howard Hughes Medical InstituteDepartment of Biochemistry and Molecular Biophysics Center for Computational Biology and Bioinformatics

Columbia University

Page 2: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Fold

Superfamily

Family

Classification

●●

● ●

●●

●●

●●

●● ●

●●

●●

Page 3: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Discrete islands

Page 4: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

ThioredoxinQ8L5D4

Glutaredoxin-4

protein disulfide oxidoreductase

L-VVVDFS-A-----TWCGPCKMI-KPFFH-SLSEKKSSLVVLY-A-----PWCSFSQAM-DESYN-DVAEK P--ILLYM-KGSPKLPSCGFSAQA-VQALA-AC---

Iron-sulfur cluster assembly

Page 5: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

P22 Cro repressor λ Cro repressor

25%

Afe142%

Xfaso 1

39%

44%

42%

Pfl6

Page 6: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Continuous space

Page 7: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard
Page 8: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Putative active site(SCREEN)

Page 9: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Formyl-CoA transferasefrom O. formigenes

NESG Target TM1055from T. maritima

Coenzyme-A

Page 10: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard
Page 11: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

CoA from Formyl-CoAtransferase

SAH from DNAmethyltransferaseTyrosine from tyrosyl

tRNA synthetaseThiamin diphosphate fromDXP synthetase

TM1055

Page 12: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Structural neighbors of TM1055

• 1793 proteins• 70 SCOP folds• 3 CATH architectures• 10 CATH topologies• 48 CATH homologous superfamilies• ~ 500 distinct ligands

Page 13: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard
Page 14: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

“jelly roll” “β-propeller”“β-prism”

Page 15: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

virus cell bacterium cell

“jelly roll” “β-propeller”

phagosome lyzosome

“β-prism”

Page 16: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Experimental interactions (from BIND+Cellzome)

Modeled interactions Davis FP, Braberg H, et. al. (2006). Nucleic Acids Research 34(10): 2943-52

19,424 12,867

409

Page 17: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

target sequences?

sequence similarity

structural similarity

template complex

Modeled complex

Page 18: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Structures from the same SCOP family (non-redundant): 8 (SCOP domain d.17.4.2)

Structures from the same SCOP superfamily (non-redundant) : 23 (SCOP domain d.17.4)SCOP fold (non-redundant):44 (SCOP domain d.17)

Structural neighbors by structure alignment: 420 (PSD < 0.8, the SCOP domain id of the green structure here is d.17.4.4 )

Page 19: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Structure model

the overlap of modeled interface with predicted (shown in red)

good bad

Page 20: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

B. subtilis lethal factor

Page 21: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

PelleB. Subtilis

lethal factor

Page 22: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

n

i xyi

xyin

iin

IcP

IcPcLRccLR

111 ~

|

|,,

Gene co-expression profiles

RGS4 block RASD1

CKS1A interact SKP2

CD4 bind TFAP2A

GPNMB contain PPFIBP1

TACR1 require PARP1

GeneWays (literature) Structures

Figure 8. Use Bayesian method to integrate PPI evidence from various sources. The likelihood ratio of an interaction between two proteins (x and y), , is inferred from different evidences (ci). Here and represent the probability that a “clue”, ci, is observed for proteins x and y that are known to interact or not (represented as and ).

),,( 1 nccLR xyi IcP | xyi IcP

~|

xyI xyI~

Page 23: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard
Page 24: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

ThioredoxinQ8L5D4

Glutaredoxin-4

protein disulfide oxidoreductase

L-VVVDFS-A-----TWCGPCKMI-KPFFH-SLSEKKSSLVVLY-A-----PWCSFSQAM-DESYN-DVAEK P--ILLYM-KGSPKLPSCGFSAQA-VQALA-AC---

Iron-sulfur cluster assembly

Page 25: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Conclusions

• Structural information needs to be leveraged

• Interactively combining overall function annotation with analysis that depends on local bioinformatic/biophysical features.

• Infrastructure applies equally to analyzing subtle differences within families.

Page 26: Using structure in protein function annotation: predicting protein interactions Donald Petrey, Cliff Qiangfeng Zhang, Raquel Norel, Barry Honig Howard

Acknowledgements

NIH grant U54-GM074958

Honig LabMarkus Fischer

Cliff ZhangKely Norel