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

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

doi:10.1038/s41598-017-10275-4

Cited by 32 publications

(25 citation statements)

References 72 publications

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“…Computational methods are commonly used for structure-based drug discovery (SBDD) and ligandbased drug discovery (LBDD) [14][15][16][17][18][19] . LBDD is a technique for searching and designing new drugs based on experimental information and structural information of known compounds 20,21 .…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Yoshino¹,

Yasuo²,

Sekijima

2020

Preprint

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The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against <a>SARS-CoV </a>Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

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Section: Introductionmentioning

confidence: 99%

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Yoshino¹,

Yasuo²,

Sekijima

2020

Preprint

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“…Considering this, open innovation is attracting attention, and various collaborations for predicting the properties of compounds in the early stage of drug development have been made [9]. In one application, several competitions (or contests, assessments) were held for protein engineering and hit compound acquisition [10] [11]. Informatics approaches are indispensable for drug discovery research in recent years.…”

Section: What Is Open and Innovative Collaborative Drug Research (Oicmentioning

confidence: 99%

“…Several successful examples of collaborative work between companies and universities or between companies [11] and individuals [12] have been reported. This shows the barrier between companies and academia has become lower [10]. In the CBI field, there is a need for cross-sectional discussion among young researchers (i.e., early-career researchers), and various proposals beyond the research fields based on their innovative ideas are required.…”

Section: What Is Open and Innovative Collaborative Drug Research (Oicmentioning

confidence: 99%

Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development

Esaki

Kumazawa

Takahashi

et al. 2020

CBIJ

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In recent years, with the emergence of new technologies employing information science, open innovation and collaborative drug discovery research, utilizing biological and chemical experimental data, have been actively conducted. The Young Researcher Association of Chem-Bio Informatics Society ("CBI Wakate") has constructed an online discussion space using Slack and provided a cloud-based collaborative platform in which researchers have freely discussed specific issues and aimed at raising the level of cross-sectoral communication regarding technology and knowledge. On this platform, we created three channels-dataset, model evaluation and scripts-where participants with different backgrounds co-developed a solution for solubility prediction. In the dataset channel, we exchanged our knowledge and

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“…4 CADD is being utilized to expedite and facilitate hit identi¯cation and hit-to-lead selection; optimize the absorption, distribution, metabolism, excretion and toxicity pro¯le; and avoid safety issues. [5][6][7][8] In drug discovery, the theoretical number of compounds in chemical space is estimated to be as much as 10 60 . 9 By contrast, compound libraries possessed by pharmaceutical companies enumerate only a few million substances, leading to the serious problem of an in-su±cient search range.…”

Section: Introductionmentioning

confidence: 99%

Compound property enhancement by virtual compound synthesis

Arai

Yoshikawa

Yasuo

et al. 2018

J. Bioinform. Comput. Biol.

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During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

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

Cited by 32 publications

References 72 publications

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Identification of Key Interactions Between SARS-CoV-2 Main Protease and Inhibitor Drug Candidates

Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development

Compound property enhancement by virtual compound synthesis

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