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 protein complex structures using neural networks
Institute: Bioinformatics Center (Akutsu Laboratory), Kyoto University Institute for Chemical
Research; Kyoto, Uji, Gokasho 611-0011, Japan
Partner institute: Macromolecular Modelling Group (Knapp Laboratory), Free University of Berlin;
Fabeckstrassen 36a, D-14195, Germany, Berlin-Dahlem
Duration : April 8, 2011~July 5, 2011
Report：Introduction of laboratory
During the international training program, I was staying in Knapp laboratory at Free University ofBerlin, whose research is mainly about protein
structure prediction, calculation of free energy ofprotein association and so on. Knapp laboratory is alaboratory in a separated small building from otherlaboratories. Professor Knapp has his own office while
two or more other members share one room. I wassharing a room with two Ph.D., one of whom is mycollaborator. The computer environment in Knapp lab
is good enough for them to run programs they need.
TheyruntheirownLinuxclusterwhichiscontinuously upgraded. Currently, they have *tens of*dual and single CPU PC's (x86 and AMD64/Opteron)with up to 3.0 GHz connected simultaneously withGbit switches. They hold seminar once a week, also
attend other seminars from time to time. All of theKnapp lab members are very nice. They helped me alot when I first reached this strange place that I felt
so warm. It takes about 30 minutes to go to the lab

Figure1. The road I walked everyday in FU.
The house I was living in.

(bus + train + walk) from the place where I was living.

Research objectives
In Knapp laboratory, the research I was working on is based on a defined project, which is aboutprotein-protein docking. What I was supposed to do
was to improve that program. First, I would like to。

0 ~ 6Α

introduce some related work. Figure 1 shows theoverview of the knowledge-based potentials. There are
191 Training protein-protein complexes ( 48 complexes

Figure2. Atoms contacts

from Benchmark 3.0[1] and 143 complexes fromHuang et. al[2] ) and 2000 near-native decoys

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):。

( 0 ~ 6 Α interface RMSD) for each complex of training set. For atoms contacts (Figure 2), 22
different atom types (20 heavy atoms and 2 hydrogen atoms) (Figure 3) were used to contact. Also,
contacts were separated into 8 distance bins. Therefore, there were
22 * 23
exactly* 8  2024 classes of contacts in total. Each decoy was2
described by its number of contacts for each class using vector ofsize 2024. As the last subfigure of Figure4 showed, one neuron
standed for each of the 2024 contact classes. Then, train neuralnetwork using back-propagation to minimize error between
Figure3. Atom types

calculated and desired output.

65 protein complexes from Protein Docking Benchmark 3.0[1] set were used as prediction data.
Also, ZDOCK 3.0[3] provided 54,000 decoys for each complex. Decoys used the unbound structuresof the proteins. For each decoy, they computed contacts and used trained neural network tocalculate score.
Figure 4 shows the overview of the knowledge-based potentials, which is one of the scoring
functions.

Figure4. Overview of the knowledge-based potentials

Our goal is to improve the program to increase the success rate for prediction.The detail work ofwhat I was working on can be divided into 4 points below.
(1) Find good distance classes.
I tried 20 groups of different distance classes, each of themtook almost one day to run. And finally I found several
good distance classes, which have increased the successful
cases of prediction. Figure 5 shows one of the best distanceclasses. The number of successful cases is 12 when the12number of predictions is 1. Our goal is to reach 14.
(2) Analyze atom classes for the amino acids.
I translated atoms within PDB files of bound complexes

Figure5. One of the best distance classes.

into corresponding atom type which is show in Figure2.
(3) Analyze distance classes:。

Extend the range of distance class to 9 Α.  Increase successful cases

Combine distance classes for hydrogens.

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

22  23
 22  22 22 
8  1    8  1  Take less time to calculate
22 
 2

(4) New method to compare performance of two predictions.
It’s hard to tell which method is the best one when looking at the results in the existing paper. Todeal with the problem, I tried both Cumulative Hypergeometric Distribution (CHD) and cumulative
distribution (CD). CHD took much more time to run than CD, at the same time, CD performances
better than CHD in many cases. So, I used CD NN 2.5 prob  1  1 
total rank

to calculate the probabilities of finding at least one of the [NN2.5 ] with [rank ] tries out of [total ]for each method. Then average each of the probabilities, as the final values to compare the methods.
The lower the value is, the better the method is.
Then I applied this new method tocompare the program I have improved(neural network-Peiying) with theprevious program (neural network-Chae)
and another pretty good program(ZRANK). The result shows that neuralnetwork-Peiying is better than other twomethods (the lower, the better).

Other Activities
During my stay inFree University ofBerlin, I joined themin eating super big
pizza (top-left),experienced thegraduation party ofPh.D. (top-right),
visited the Berlin
Wall (down-left),attended theCarnival of Culturesin Berlin (down-right)
and also travelled toother cities.

Figure6. Compare three methods (sorted)

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

Acknowledgements
Finally, I want to express my thanks to my supervisor Prof. Akutsu for allowing me participating
this program, program director Prof. Mamitsuka and Prof.Kanehisa for giving me this chance, also
thanks to associate Prof. Ernst-Walter Knapp for giving me so many advices about my research andall the Knapp’s group, especially to my collaborator Florian Krull.

From left to right: Jan Zacharias, meand Anna Lena, Woelke.

Almost all the members in Knapplaboratory. From left to right: TimMeyer, Prof. Knapp, me, DawidRasinski, Ilkay Sakalli, Anna Lena,
Gerrit Korff, Jorge Numata and Jan
Zacharias

References[1] Hwang H, Pierce B, Mintseris J, Janin J, Weng Z. Proten-protein docking benchmark version 3.0.
Proteins, 73: 705-709, 2008.
[2] Huang SY and Zou X. An iterative knowledge-based scoring function for protein-protein recognition.
Proteins, 72: 557-579, 2008
[3] Pierce B, Weng Z. Structure Prediction of Protein Complexes. Proteins, 67: 1078-1086, 2007