hp120131

タンパク質間相互作用ネットワークの推定とその応用に関する研究
Large-scale protein-protein interaction network
prediction by an exhaustive rigid docking system
MEGADOCK松崎 由理
Yuri Matsuzaki
東京工業大学 情報生命博士教育院
matsuzaki@acls.titech.ac.jp■課題代表者■参加者

秋山 泰 （東工大）
Yutaka Akiyama (Tokyo Institute of Technology)
松崎由理，石田貴士，大上雅史 （東工大），内古閑伸之（中央大）
Yuri Matsuzaki, Takashi Ishida, Masahito Ohue (Tokyo Institute ofTechnology), Nobuyuki Uchikoga (Chuo University)

The number of available protein
tertiary structures is growing rapidly• More than 100,000 protein structures
are available on Protein Data Bank• Supercomputers can contributeunderstanding interactome utilizing
this rich data source

RCSB PDB(as of 2014-09-22)2What we do: Protein-protein interaction (PPI) network
prediction using proteins tertiary structure data
Input: Protein structures

Output: PPI networks

Target: Millions of combinations of proteins
Strategy: Exhaustive rigid docking among target proteins• Feasible assuming massively parallel
computing environments• Prediction of complex structures canalso be provided

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MEGADOCK system: PPI network prediction
by an ultra-fast rigid-docking tool
Exhaustive docking & post-docking analysis
P1P2

P3

P4

Prediction of binders
P5P1MEGADOCK• Rigid-docking software designed for
exhaustive docking studies• Suitable for running on supercomputers• K, SCLS, TSUBAME2.5 (CPU/GPUversion)• Open-source
http://www.bi.cs.titech.ac.jp/megadock/Ohue, et al., Bioinformatics, accepted.

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PPI network

Rigid Docking• Evaluates docking scores mainly based on thecomplementarity of protein tertiary structure11

Δθ is 15 degree→3600 patterns ofLigand rotations

1111

1
1

best score = 8111
1

1

best score = 5Katchalski-Katzir E, et al. PNAS, 1992.
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A compact score function ofMEGADOCK
Compress three terms into one
complex number:• Shape complementarity• Hydrophobic interaction• Electrostatic interaction
1+H 2+H 3+H 3+H 3+H 2+H 1+H+iφ +iφ +iφ +iφ +iφ +iφ +iφ2+H+iφ -452+H-45 -45 -45 -45 +iφ3+H+iφ -45-453+H+iφ -455+H 2+H 1+H-45 -45 +iφ +iφ +iφ1+iq 1+iq 1+iq 1+iq 1+iq3+H+iφ -455+H 2+H 1+H-45 -45 +iφ +iφ +iφ1+iq 1+iq 1+iq 1+iq 1+iq3+H+iφ -452+H-45 -45 -45 -45 +iφ2+H+iφ -452+H-45 -45 -45 -45 +iφ2+H-45 -45 -45 +iφ1+H 2+H 3+H 3+H 3+H 2+H 1+H+iφ +iφ +iφ +iφ +iφ +iφ +iφReceptor protein

1+iq 1+iq1+iq 1+iq

Convolution can becalculated fast by FFT(Katchalski-Katzirmodel)
Ligand protein

Ohue et al., Lecture Note in Bioinformatics, 2012.

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Implementation by hybridparallelization
MPI parallelization

OpenMP parallelization
Receptor voxelization

Receptor
LigandReceptor FFT
Loop for Ligand rotations

Worker
Node

WorkerNode

Ligand voxelization

OpenMPMaster
Node

WorkerNode…

Ligand FFT
Convolution
Score calculation (Inverse FFT)
Save high scoring docking poses
Matsuzaki, et al., Source Code Biol Med, 2013.

…

Performance of protein docking byMEGADOCK
Docking accuracy wascomparable to conventional tools(Benchmark 4.0, 176 bound complexes)

8.8 times speedup
compared to ZDOCK 3.0(single node, single thread)
Ohue, et al., Lecture Note in Bioinformatics,
2012.

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Scalability on K computer
OpenMP parallelization

4LinearObserved47.33Speedup

Speedup

8

MPI parallelization

3.88

2

1.98
24
#Threads

8

1.97

211

LinearObserved

1

3.76

1.006144

Strong scaling> 0.95 on 24576 nodes

12288#Nodes24576• Less but sufficient scalability (strong scaling 0.91) was observed with 82,944 nodes.
Matsuzaki, et al., Source Code Biol Med, 2013.

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Prediction method 1: clustering based•
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Get 2,000 high scoring models by docking ofeach protein pair
Conduct clustering based on structure
similarity
Define the highest docking score(normalized) of the data included in thecluster Ci : si
Define cluster population (normalized) : mi
Select populated clusters C’ with thresholdm* of population of the cluster
Decide PPI score E
Evaluate each pair of protein combination asinteracting if E is higher then the threshold
E*Matsuzaki, et al., J Bioinform Comput Biol, 2009.

Receptor

LigandNumber of data

•

𝑪𝑪𝟏𝟏

𝑪𝑪𝟐𝟐𝑪𝑪′

𝑺𝑺𝟐𝟐𝑺𝑺𝟏𝟏(Normalizeddocking score)cluster10

Prediction method 2: reranking based
Reranking

S1

ZR1

S2

ZR2

・・・S3

Ohue, et al., Protein Pept Lett, in press.

・・・S6,000・・・Docking
calculation(ZRANK, Pierce, Proteins, 2007)

ZR3,273

Mean of S
S.D. of Sμσ

PPI prediction using generalbenchmark data

PPI

L1 L2 L3・・・ L44

1R10.8・・・

R3NoPPI

TP rate

R2R44

Binding partner prediction from
44x44=1936 dockings and post-docking(Diagonal: interacting pairs)

0.6

Trypsinogen protein
and trypsin inhibitor(PDB id : 1CGI)

0.4
0.2

MEGADOCK
randomPredicted docking pose
Crystal structure

00F-measure : 0.42
Matsuzaki, et al., J Bioinform Comput Biol, 2009.
Ohue, et al., Protein Pept Lett, 2014.

0.2

0.4

0.6

FP rate0.8

β-actin and profilin(PDB id : 2BTF)

1

PPI prediction by MEGADOCK achieved
better than random performance on
general benchmark dataset (monomerpair from protein-protein docking
benchmark 2.0, Mintseris et al, Proteins,2005.)
12Application to bacterial
chemotaxis pathway
CheY
Asp：True Positive：False Negative：False Positive

13 proteins(101 structures)

F-measure : 0.44
Acceptable performance was shown on a real
biology pathway reconstruction problem.

CheCCheD
A suggestion of CheY-CheC
interaction by using “Falsepositive” pair CheY-CheD as amediator

Matsuzaki, et al., J Bioinform Comput Biol, 2009.

13Application to human apoptosis pathway
57 proteins(158 structures)Ozbabacan et al, 2012

F-measure : 0.28
PPI prediction by docking without
any other knowledge showedcomparable results to templatebased search of interaction partners

hsa04210(F-measure 0.30, Ozbabacan et al., J
Struct Biol, 2012).

Interacting

NoInteraction

Positive

88364

Negative

961105Prediction

True Positive
False Positive
False Negative

Ohue, et al., BMC Proc., 2013.

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Application to non-small celllung cancer pathwayCompleted large-scale exhaustive docking

497 structures, all-to-all docking＝247,009 structure pairs

Achieved high PPI prediction performance
Precision 0.29

Recall0.47
F-measure 0.36
44 proteins（497 structures)

PPI prediction of about 250
thousand structure pairs
showed comparable
performance to theapplication to bacterial
chemotaxis (10 thousandpairs).
InteractingNo
Interaction

Positive53131

Negative

59

747

Prediction

Counts are based onprotein species

hsa0522315

Application to lung-cancer drug
related proteinsProteins related to Gefitinib
estimated by Miyano lab., The Univ.
of Tokyo, from microarray analysis

EGFR pathway related tonon-small cell lung cancer
44 proteins（497 structures)

2,000 x 2,000 =4 million
dockings

294 proteins（1424 structures)#TP / (#TP+#FP)

Search novel cancer related PPIs

PPI prediction resultPrecision#TP / (#TP+#FP)

Threshold (E*)
Gene expression regulation• Using threshold of E* = 13.0

• 3873 structure pairs• 175 protein pairs• Evaluated the prediction by 6 public
databases (MIPS, DIP, IntAct, HPRD,BioGRID, MINT)• Undefined positives
35 pairs• Looked up these pairs on cancer gene
regulatory networks derived bycorrelation of transcription data
• Selected highly correlated pairs• Obtained 11 pairs
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Evaluation of 7 potential PPIs by SPR• 7 pairs were sent to assay using surface
plasmon resonance (SPR) spectroscopy– Reference Biolabs Inc.,
Korea– Device: Reichert SR7500DC system• Binding affinities weremeasured except from1 pair

Reference Biolobs Inc.

[Ongoing] Virus-human PPI prediction
4 major enzymes of Dengue virus

Human proteins

Collected human proteins data: >25 residues
 X-ray resolution better than
3.25 Å No mutation

EnzymeProtease
Methyltransferase
Polymerase
Helicase#Proteins (UniProt IDs)

3,353

#Structure data (PDB-chains)

30,5444 × 30,544 = 122,176 dockings
Suggested 28 potential interactions(2013-06-15)

References(Papers contributed by MEGADOCK group)
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Ohue M, Shimoda T, Suzuki S, Matsuzaki Y, Ishida T, Akiyama Y, MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers., Bioinformatics, accepted.

Matsuzaki Y, Ohue M, Uchikoga N, Akiyama Y, Protein-protein interaction network prediction by using rigid-body docking
tools: application to bacterial chemotaxis., Protein and Peptide Letters, 21:790-798, 2014.
Ohue M, Matsuzaki Y, Uchikoga N, Ishida T, Akiyama Y, MEGADOCK: An all-to-all protein-protein interaction prediction
system using tertiary structure data., Protein and Peptide Letters, 21:766-778, 2014.
Matsuzaki Y, Uchikoga N, Ohue M, Shimoda T, Sato T, Ishida T, Akiyama Y, MEGADOCK3.0: A high-performance proteinprotein interaction prediction software using hybrid parallel computing for petascale supercomputing environments., Source
Code for Biology and Medicine, 8:18, 2013.
Uchikoga N, Matsuzaki Y, Ohue M, Hirokawa T, Akiyama Y, Improved post-processing of protein-protein docking data using
profiles of interaction fingerprints., PLoS ONE, 8:e69365, 2013.
Ohue M, Matsuzaki Y, Shimoda T, Ishida T, Akiyama Y, Highly precise protein-protein interaction prediction based onconsensus between template-based and de novo docking methods., BMC Proceedings, 7(Suppl. 7):S6, 2013.

Ohue M, Matsuzaki Y, Ishida T, Akiyama Y, Improvement of the protein-protein docking prediction by introducing a simple
hydrophobic interaction model: an application to interaction pathway analysis., Lecture Note in Bioinformatics, 7632:178187, 2012.
Ohue M, Matsuzaki Y, Akiyama Y, Docking-calculation-based method for predicting protein-RNA interactions., Genome
Informatics, 25:25-39, 2011.
Fleishman SJ, et al., Community-wide assessment of protein-interface modeling suggests improvements to design
methodology., Journal of Molecular Biology, 414:289-302, 2011.
Uchikoga N, Hirokawa T, Analysis of protein-protein docking decoys using interaction fingerprints: application to thereconstruction of CaM-ligand complexes., BMC Bioinformatics, 11:236, 2010.
Ohue M, Matsuzaki Y, Matsuzaki Y, Sato T, Akiyama Y, MEGADOCK: an all-to-all protein-protein interaction prediction systemusing tertiary structure data and its application to systems biology study., IPSJ Transactions on Mathematical Modeling andIts Applications, 3: 91-106, 2010.
Matsuzaki Y, Matsuzaki Y, Sato T, Akiyama Y, In silico screening of protein-protein interactions with all-to-all rigid docking andclustering: an application to pathway analysis., Journal of Bioinformatics and Computational Biology, 7:991-1012, 2009.
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