若手研究者インターナショナルトレーニング プログラム(ITP)	  	  バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム	 報告書	 	 	  	  Name：Peiying Ruan
Title：Prediction of heterodimeric protein complexes using protein-protein interaction networks
Institute: Bioinformatics Center (Akutsu Laboratory), Kyoto University Institute for Chemical
Research; Kyoto, Uji, Gokasho 611-0011, Japan
Partner institute: Centre for Computational Biology, Mines ParisTech
Duration: September 4, 2013 – November 24, 2013

Report:1. Introduction of Laboratory:
During the International Training Program, I was staying in Dr. Jean-Philippe’s laboratory inCentre for Computational Biology building at Curie Institute (Figure 1: top), which belongs to
Mines ParisTech. The members of several groups in the building share the rooms andcomputers. The computers and seats are free to be chosen since every member has a personalaccount to login all the computers. There are three postdoctoral fellows and about 10 PhD
students in our group, including two new coming
postdoctoral fellows at the time I was leaving.
We have a basic weekly group meeting on every
Tuesday and machine learning reading club on
every Thursday. Besides that, we sometimes havesmile seminars in other buildings to listen toothers’ talks (Figure 1: down). In the weekly
group meeting, we first discuss some news on thelab things and then talk about current researchand new progress one by one, and sometimes wecan give presentations if we want to. Everyone isactive and they always make jokes during talking,so we can totally enjoy the seminar in a relaxedmood.
Curie institute locates in the central position in
Paris, with Seine River and Luxembourg Garden
around. Therefore, I could enjoy a nice viewFigure	  1.	  Environment	  of	  the	  lab	  everyday when I went to the laboratory. I wasliving outside Paris during my stay. It took me near 50 minutes to go to the lab (train+walk).
But I really enjoy the time when I was taking the train. There are nice views outside andsometimes people play music in the train. While listening to the music, I looked at thebeautiful scenery outside and felt peaceful. It was a good time to think about the research,even sometimes a new idea could come out.

2. Research project:
The research interest of Dr. Jean-Philippe mainly concerns the development of statistical andmachine learning methods for computational biology. My previous research used kernel

若手研究者インターナショナル

トレーニング プログラム(ITP)	  	  バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム	 報告書	 	 	  	  Report	  (Continued)：	  functions, and Dr. Jean-Philippe is an expert in this area. So we considered continuing myprevious research to improve the results. My previous work is on prediction of heterodimeric	  protein complexes using protein-protein interaction networks. As we know, protein
complexes play crucial roles in a variety of biological processes, such as ribosomes forprotein biosynthesis, molecular transmission and evolution of interactions between	   proteins.
In fact, many proteins come to be functional only after they interact with their specificpartners and are assembled into protein complexes. Hence, much effort has been made forpredicting protein complexes from protein-protein interaction (PPI) networks inbioinformatics, such as MCL, MCODE, RNSC, PCP, RRW, and NWE.
These methods have dealt with only complexes with size of more than three because themethods often are based on some density of subgraphs. However, heterodimeric protein
complexes that consist of two distinct proteins occupy a large part according to severalcomprehensive databases of known complexes. In our previous work, we proposed several
feature space mappings from protein-protein interaction data, in which each interaction isweighted based on reliability. Furthermore, we made use of prior knowledge on protein
domains to develop feature space mappings, domain composition kernel and its combination
kernel with our proposed features.
The results suggest that our proposed kernel considerably outperforms other methods forpredicting heterodimeric protein complexes.
Through the discuss with Dr. Jean-Philippe, we improved the performance for prediction
heterodimers by the following steps:(1) Using Kernels: Domain composition kernel that we proposed in the previous work is abinary kernel related to domain composition. We wonder if some existing pairwisekernels may perform better by comparing domain composition of each two complexes,
such as tensor product pairwise kernel (TPPK) (1), metric learning pairwise kernel(MLPK) (2) and Tanimoto Kernel (3). The results indicate that MLPK improved theprediction accuracy, while TPPK and Tanimoto Kernel did not.
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(2) Add other information: In the previous work, the features we designed mostly based on
the interactions between protein pairs. However, much other information such as
sequence, expression, localization and phylogenetic profile, is not considered yet.
Therefore, we collected these data from databases and employed proper kernel function
for each (Figure 2).

若手研究者インターナショナル

トレーニング プログラム(ITP)	  	  バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム	 報告書	 	 	  	  Figure	  2:	  add	  other	  information	  (3) Combine multiple kernels: Construct a kernel model to combine these kernels, with
optimal weighting coefficients for each base kernel.
(4) Design	  a	  new	  kernel:	  We	  have	  proposed	  domain	  composition	  kernel,	  which	  improved	  the	  prediction	  accuracy	  a	  lot.	  However,	  domain	  composition	  kernel	  is	  a	  simple	  binary	  kernel.	  Hence,	  if	  possible,	  we	  will	  try	  to	  design	  a	  new	  kernel	  with	  more	  information.
(5) Experimental results & Discussion:

We performed ten-fold cross-validation computational experiments. By using the informationand kernels mentioned above, and adding the feature mappings we made in the previouswork, we obtained the results in the following table. Note that the best accuracy, evaluated byF-measure, we achieved in the previous work is 0.631. The first row is the result obtained by
the method [Feature mappings + TPPK kernel], we can see that the result was not improved.
But by the method [Feature mappings + MLPK kernel], the accuracy was improved to 0.656.
In addition, we (i) added the expression information, the result became worse,(ii) added the phylogenetic profile, the result was improved.
Then, we added sequence information and phylogenetic profile together, the result wasfurther improved up to 0.672 (Table 1).

Table	  1:	  results	  of	  combined	  methods	  3. Other activities:
Besides the research, we also had some parties during my stay in Paris (Figure 3: down left
and right), I really enjoy the food there, especially like various French cakes. All the members

若手研究者インターナショナル

トレーニング プログラム(ITP)	  	  バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム	 報告書	 	 	  	  Plan	  (Continued)	  in the group are very kind and offered me a lot of help. It’s really hard to say goodbye withthem. On my last weekday in Paris, we took a picture together (Figure 4). We wanted to takepicture outside the building, but Dr. Jean-Philippe suggested us to take a picture in thelaboratory, he smiled and said, ”because we are always working”, making us laugh out
loudly. In my spare time, I visited Louvre Museum (Figure 3: top left), Eiffel Tower (Figure3: top right) and many other famous architectures.	  

Figure	  4.	  Members	  in	  the	  lab	  	   	  Figure	  3.	  Some	  activities	  Acknowledgement
First of all, I would like to express my thanks to my supervisor Professor Akutsu for allowing
me participating this program, program director Professor Mamitsuka and Professor Kanehisa
for providing me such a valuable opportunity. Also, I would like to thank Professor
Jean-Philippe for giving me so many valuable advices about my research and all the groupmembers for their kindness and supports during my stay in Paris.