PREDICTION OF HETERODIMERIC PROTEIN COMPLEXES FROM PROTEIN-PROTEIN INTERACTION NETWORKS USING DEEP LEARNING

Transcription

1 PREDICTION OF HETERODIMERIC PROTEIN COMPLEXES FROM PROTEIN-PROTEIN INTERACTION NETWORKS USING DEEP LEARNING Peiying (Colleen) Ruan, PhD, Deep Learning Solution Architect 3/26/2018

2 Background OUTLINE Method Computational Experiments and Results Conclusions 2

3 BACKGROUND 3

4 BACKGROUND DNA Our works Transcription Translation Forming mrna Protein complexes Biological System? Performing functions Disease Human Cell Protein-protein interactions Keeping healthy 4

5 BACKGROUND What is heterodimer and why predict it? Heterodimers Occupy 40%!!! 5

6 BACKGROUND Structure Domain Composition D 3 P 1 D 1 D 2 P 1 P 1 Interaction D 2 D 2 P 2 Pi: protein Di: domain 6

7 BACKGROUND Structure Domain Composition Weighted PPI Network Pi: protein Di: domain D 1 D 2 P w P 1 P P Interaction P 1 2 D 3 D 2 D 2 P 2 7

8 METHOD 8

9 OVERVIEW OF THE PROBLEM Input:weighted PPI network P i Heterodimer? P j Input data 9

10 MULTIPLE INFORMATION + MULTIPLE DL MODELS Input data involving biological information Protein-protein interaction (PPI) Deep neural network models including Convolutional neural network (CNN) Domain Phylogenetic profile Recurrent neural network (RNN) CNN + RNN 10

11 PROTEIN-PROTEIN INTERACTION (PPI) Table 1. Feature space mapping from two interacting proteins P i, P j and neighbors. The weights of interactions between the focused proteins. The maximum weights of interactions between either of focused proteins and a neighboring protein. The minimum weights of interactions between either of focused proteins The and maximum a smaller weights of interactions with neighboring protein. proteins. The maximum differences of weights among the neighboring weights. P i D n D m w ij w ik P k D r w jk P j Figure 1. Example of a subgraph with an interacting protein pair and their neighboring proteins. 11

12 DOMAIN C i P 1 Sample D 3 D 9 D 8 D 3 D 10 P 2 Domain pair of protein complex C j : D 3 (D 3, D 3 ), (D 3, D 3 ), (D 3, D 10 ), (D 8, D 3 ), (D 8, D 3 ), (D 8, D 10 ), (D 9, D 3 ), (D 9, D 3 ), (D 9, D 10 ) The whole domain pair sets for all complexes in the dataset {(D 1, D 1 ), (D 1, D 2 ),, (D 3, D 3 ),, (D 9, D 10 ),, (D n, D n )} 5295 #domain pair is 5295 [C j ]=[ 0 0,, 2,, 1,, 0 ] 12

13 PHYLOGENETIC PROFILE SC BS EC P P 1 P 4 P 3 P 2 P 1 P P S.Cerevisiae (SC) P 1 P 2 B.Subtilis (BS) P E.Coli (EC) The whole organism for all complexes in the dataset { SC, BS, EC, } 2717 [C j = Q(P 1, P 2 )]=[ 0 0 1, ] Q(a, b)=min(a, b) #organism is

14 COMPUTATIONAL EXPERIMENTS 14

15 Databases CYC2008: A manually curated comprehensive catalogue of yeast protein complexes, including 172(42%) heterodimers. WI-PHI: A PPI database with weights containing interacting protein pairs except self-interactions. Positives and Negatives C 1 C 2 P 1 P 2 P 3 P 4 Positives: (P 1,P 2 ) Negatives: (P 1,P 3 ), (P 2,P 4 ), (P 3,P 4 ) and (P 1,P 4 ) #Sample:

16 INPUT DATA e.x.domain property The whole domain pair set for all complexes in the dataset {(D 1, D 1 ), (D 1, D 2 ),, (D 3, D 3 ),, (D 9, D 10 ),, (D n, D n )} Input data: [C 1 ]=[ 0 0,, 2,, 1,, 0 ] [C 2 ]=[ 0 1,, 0,, 0,, 1 ] [C 5497 ]=[ 0 0,, 2,, 1,, 0 ] Label: ] 16

17 INPUT DATA e.x. Domain + Phylogenetic profile The whole (domain pair set + organism) for all complexes in the dataset {(D 1, D 1 ), (D 1, D 2 ),, (D n, D n ), SC, BS, EC, } Input data: [C 1 ]=[ 0 0,, 0, 0, 0, 1, ] [C 2 ]=[ 0 1,, 1, 1, 0, 0, ] [C 5497 ]=[ 0 0,, 0, 0, 1, 1, ] Label: ] 17

18 MODELS Input data Input data Input data Convolution Neural Network Recurrent Neural Network Convolution Neural Network Output Output Recurrent Neural Network D. Quang et al., DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences, Nucleic Acids Research, 2016 Output 18

19 RESULTS 19

20 PERFORMANCE MEASURES Accuracy = Precision = Recall = F1 = 2 tp + tn tp + tn + fp + fn tp tp + fp tp tp + fn precision recall precision + recall tp: true positive, tn: true negative, fp: false positive, fn: false negative 20

21 COMPARISON OF MODEL + INFORMATION Models Training accuracy Training loss Test accuracy Evaluation score (F1) CNN (domain) CNN (domain+ppi) CNN (domain+ppi+phylogenetic profile) RNN (domain+ppi+phylogenetic profile) CNN+RNN (domain+ppi+phylogenetic profile) Baseline method* SVM(PPI+domain) *P. Ruan et al. Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions, PLoS One,

22 600 CPU VS GPU min DGX Station is 40 times faster!! Time(sec)/Epoch 12 sec CPU DGX Station 22

23 CONCLUSIONS Applied deep learning to predicting heterodimeric protein complexes with multiple biological information The performance of hybrid model with multiple information is better than single model The speed of DGX station is 40 times faster than CPU 23

24 Thank you for your kind attention!