An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

Shuntaro Chiba; Takashi Ishida; Kazuyoshi Ikeda; Masahiro Mochizuki; Reiji Teramoto; Y-H Taguchi; Mitsuo Iwadate; Hideaki Umeyama; Chandrasekaran Ramakrishnan; A Mary Thangakani; D Velmurugan; M Michael Gromiha; Tatsuya Okuno; Koya Kato; Shintaro Minami; George Chikenji; Shogo D Suzuki; Keisuke Yanagisawa; Woong-Hee Shin; Daisuke Kihara; Kazuki Z Yamamoto; Yoshitaka Moriwaki; Nobuaki Yasuo; Ryunosuke Yoshino; Sergey Zozulya; Petro Borysko; Roman Stavniichuk; Teruki Honma; Takatsugu Hirokawa; Yutaka Akiyama; Masakazu Sekijima

doi:10.1038/s41598-017-10275-4

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

Sci Rep. 2017 Sep 20;7(1):12038. doi: 10.1038/s41598-017-10275-4.

Authors

Shuntaro Chiba^{1

2}, Takashi Ishida^{1

2

3}, Kazuyoshi Ikeda⁴, Masahiro Mochizuki⁵, Reiji Teramoto⁶, Y-H Taguchi⁷, Mitsuo Iwadate⁸, Hideaki Umeyama⁸, Chandrasekaran Ramakrishnan⁹, A Mary Thangakani¹⁰, D Velmurugan¹⁰, M Michael Gromiha⁹, Tatsuya Okuno¹¹, Koya Kato¹², Shintaro Minami¹³, George Chikenji¹², Shogo D Suzuki³, Keisuke Yanagisawa³, Woong-Hee Shin¹⁴, Daisuke Kihara^{14

15}, Kazuki Z Yamamoto¹⁶, Yoshitaka Moriwaki¹⁷, Nobuaki Yasuo³, Ryunosuke Yoshino^{17

18}, Sergey Zozulya^{19

20}, Petro Borysko^{19

20}, Roman Stavniichuk¹⁹, Teruki Honma^{1

3

21}, Takatsugu Hirokawa^{22

23

24}, Yutaka Akiyama^{1

2

3

22

24}, Masakazu Sekijima^{25

26

27

28

29}

Affiliations

¹ Advanced Computational Drug Discovery Unit, Institute of Innovative Research, Tokyo Institute of Technology, J3-23 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
² Education Academy of Computational Life Sciences, Tokyo Institute of Technology, J3-141 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
³ Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
⁴ Level Five Co. Ltd., Shiodome Shibarikyu Bldg., 1-2-3 Kaigan, Minato-ku, Tokyo, 105-0022, Japan.
⁵ IMSBIO Co., Ltd., Level 6 OWL TOWER, 4-21-1 Higashi-Ikebukuro, Toshima-ku, Tokyo, 170-0013, Japan.
⁶ Forerunner Pharma Research, Co., Ltd., Yokohama Bio Industry Center, 1-6 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan.
⁷ Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan.
⁸ Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan.
⁹ Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India.
¹⁰ CAS in Crystallography and Biophysics and Bioinformatics Facility, University of Madras, Chennai, 600025, Tamilnadu, India.
¹¹ Division of Neurogenetics, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa-ku, Nagoya, 466-8550, Japan.
¹² Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa, Nagoya, 464-8603, Japan.
¹³ Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Furocho, Chikusa, Nagoya, 464-8601, Japan.
¹⁴ Department of Biological Sciences, Purdue University, Indiana, 47907, USA.
¹⁵ Department of Computer Science, Purdue University, Indiana, 47907, USA.
¹⁶ Isotope Science Center, The University of Tokyo, 2-11- 16, Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan.
¹⁷ Department of Biotechnology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
¹⁸ Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.
¹⁹ Bienta/Enamine Ltd., 78 Chervonotkatska Street, Kyiv, 02660, Ukraine.
²⁰ National Taras Shevchenko University of Kyiv, 64/13 Volodymyrska Street, Kyiv, 01601, Ukraine.
²¹ Center for Life Science Technologies, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.
²² Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
²³ Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan.
²⁴ Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan.
²⁵ Advanced Computational Drug Discovery Unit, Institute of Innovative Research, Tokyo Institute of Technology, J3-23 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan. sekijima@c.titech.ac.jp.
²⁶ Education Academy of Computational Life Sciences, Tokyo Institute of Technology, J3-141 4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan. sekijima@c.titech.ac.jp.
²⁷ Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. sekijima@c.titech.ac.jp.
²⁸ Global Scientific Information and Computing Center, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. sekijima@c.titech.ac.jp.
²⁹ Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan. sekijima@c.titech.ac.jp.

Abstract

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Drug Discovery / methods*
Enzyme Inhibitors / chemistry
Enzyme Inhibitors / metabolism
Enzyme Inhibitors / pharmacology*
High-Throughput Screening Assays / methods*
Humans
Machine Learning
Molecular Structure
Protein Binding
Protein Kinase Inhibitors / chemistry
Protein Kinase Inhibitors / metabolism
Protein Kinase Inhibitors / pharmacology*
Proto-Oncogene Proteins c-yes / antagonists & inhibitors*
Proto-Oncogene Proteins c-yes / metabolism
Reproducibility of Results
Structure-Activity Relationship

Substances

Enzyme Inhibitors
Protein Kinase Inhibitors
Proto-Oncogene Proteins c-yes
YES1 protein, human