1
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Ibrahim PEGF, Zuccotto F, Zachariae U, Gilbert I, Bodkin M. Accurate prediction of dynamic protein-ligand binding using P-score ranking. J Comput Chem 2024; 45:1762-1778. [PMID: 38647338 DOI: 10.1002/jcc.27370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/25/2024]
Abstract
Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.
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Affiliation(s)
- Peter E G F Ibrahim
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Fabio Zuccotto
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ulrich Zachariae
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ian Gilbert
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Mike Bodkin
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
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2
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Mori S, Izumi H, Araki M, Liu J, Tanaka Y, Kagawa Y, Sagae Y, Ma B, Isaka Y, Sasakura Y, Kumagai S, Sakae Y, Tanaka K, Shibata Y, Udagawa H, Matsumoto S, Yoh K, Okuno Y, Goto K, Kobayashi SS. LTK mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring CLIP1-LTK fusion. Commun Biol 2024; 7:412. [PMID: 38575808 PMCID: PMC10995188 DOI: 10.1038/s42003-024-06116-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 03/27/2024] [Indexed: 04/06/2024] Open
Abstract
The CLIP1-LTK fusion was recently discovered as a novel oncogenic driver in non-small cell lung cancer (NSCLC). Lorlatinib, a third-generation ALK inhibitor, exhibited a dramatic clinical response in a NSCLC patient harboring CLIP1-LTK fusion. However, it is expected that acquired resistance will inevitably develop, particularly by LTK mutations, as observed in NSCLC induced by oncogenic tyrosine kinases treated with corresponding tyrosine kinase inhibitors (TKIs). In this study, we evaluate eight LTK mutations corresponding to ALK mutations that lead to on-target resistance to lorlatinib. All LTK mutations show resistance to lorlatinib with the L650F mutation being the highest. In vitro and in vivo analyses demonstrate that gilteritinib can overcome the L650F-mediated resistance to lorlatinib. In silico analysis suggests that introduction of the L650F mutation may attenuate lorlatinib-LTK binding. Our study provides preclinical evaluations of potential on-target resistance mutations to lorlatinib, and a novel strategy to overcome the resistance.
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Affiliation(s)
- Shunta Mori
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Hiroki Izumi
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Jie Liu
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577, Japan
| | - Yu Tanaka
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Yosuke Kagawa
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Yukari Sagae
- Graduate School of Medicine, Kyoto University, Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Biao Ma
- RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan
| | - Yuta Isaka
- RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan
| | - Yoko Sasakura
- RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan
| | - Shogo Kumagai
- Division of Cancer Immunology, Research Institute/Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577, Japan
| | - Yuta Sakae
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577, Japan
| | - Kosuke Tanaka
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577, Japan
| | - Yuji Shibata
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Hibiki Udagawa
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Shingo Matsumoto
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Kiyotaka Yoh
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Koichi Goto
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan
| | - Susumu S Kobayashi
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577, Japan.
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan.
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
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3
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Suzuki M, Uchibori K, Oh-Hara T, Nomura Y, Suzuki R, Takemoto A, Araki M, Matsumoto S, Sagae Y, Kukimoto-Niino M, Kawase Y, Shirouzu M, Okuno Y, Nishio M, Fujita N, Katayama R. A macrocyclic kinase inhibitor overcomes triple resistant mutations in EGFR-positive lung cancer. NPJ Precis Oncol 2024; 8:46. [PMID: 38396251 PMCID: PMC10891166 DOI: 10.1038/s41698-024-00542-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Brigatinib-based therapy was effective against osimertinib-resistant EGFR C797S mutants and is undergoing clinical studies. However, tumor relapse suggests additional resistance mutations might emerge. Here, we first demonstrated the binding mode of brigatinib to the EGFR-T790M/C797S mutant by crystal structure analysis and predicted brigatinib-resistant mutations through a cell-based assay including N-ethyl-N-nitrosourea (ENU) mutagenesis. We found that clinically reported L718 and G796 compound mutations appeared, consistent with their proximity to the binding site of brigatinib, and brigatinib-resistant quadruple mutants such as EGFR-activating mutation/T790M/C797S/L718M were resistant to all the clinically available EGFR-TKIs. BI-4020, a fourth-generation EGFR inhibitor with a macrocyclic structure, overcomes the quadruple and major EGFR-activating mutants but not the minor mutants, such as L747P or S768I. Molecular dynamics simulation revealed the binding mode and affinity between BI-4020 and EGFR mutants. This study identified potential therapeutic strategies using the new-generation macrocyclic EGFR inhibitor to overcome the emerging ultimate resistance mutants.
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Affiliation(s)
- Mai Suzuki
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
- Department of Medical Genome Science, Graduate School of Frontier Science, The University of Tokyo, Tokyo, 108-8639, Japan
| | - Ken Uchibori
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
- Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tomoko Oh-Hara
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
| | - Yumi Nomura
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
| | - Ryusei Suzuki
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
| | - Ai Takemoto
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-Ku, Kyoto, Japan
| | - Shigeyuki Matsumoto
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-Ku, Kyoto, Japan
| | - Yukari Sagae
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-Ku, Kyoto, Japan
| | - Mutsuko Kukimoto-Niino
- Laboratory for Protein Functional and Structural Biology, RIKEN Center for Biosystems Dynamics Research, Yokohama, Kanagawa, 230-0045, Japan
| | | | - Mikako Shirouzu
- Laboratory for Protein Functional and Structural Biology, RIKEN Center for Biosystems Dynamics Research, Yokohama, Kanagawa, 230-0045, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-Ku, Kyoto, Japan
| | - Makoto Nishio
- Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Naoya Fujita
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Ryohei Katayama
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, 135-8550, Japan.
- Department of Medical Genome Science, Graduate School of Frontier Science, The University of Tokyo, Tokyo, 108-8639, Japan.
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4
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Xu H. The slow but steady rise of binding free energy calculations in drug discovery. J Comput Aided Mol Des 2023; 37:67-74. [PMID: 36469232 DOI: 10.1007/s10822-022-00494-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.
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Affiliation(s)
- Huafeng Xu
- Roivant Discovery, 151 West 42nd Street, New York, NY, 10036, USA.
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5
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Takaba K, Watanabe C, Tokuhisa A, Akinaga Y, Ma B, Kanada R, Araki M, Okuno Y, Kawashima Y, Moriwaki H, Kawashita N, Honma T, Fukuzawa K, Tanaka S. Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy. J Comput Chem 2022; 43:1362-1371. [PMID: 35678372 DOI: 10.1002/jcc.26940] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/27/2022] [Accepted: 04/02/2022] [Indexed: 01/16/2023]
Abstract
Fragment molecular orbital (FMO) method is a powerful computational tool for structure-based drug design, in which protein-ligand interactions can be described by the inter-fragment interaction energy (IFIE) and its pair interaction energy decomposition analysis (PIEDA). Here, we introduced a dynamically averaged (DA) FMO-based approach in which molecular dynamics simulations were used to generate multiple protein-ligand complex structures for FMO calculations. To assess this approach, we examined the correlation between the experimental binding free energies and DA-IFIEs of six CDK2 inhibitors whose net charges are zero. The correlation between the experimental binding free energies and snapshot IFIEs for X-ray crystal structures was R2 = 0.75. Using the DA-IFIEs, the correlation significantly improved to 0.99. When an additional CDK2 inhibitor with net charge of -1 was added, the DA FMO-based scheme with the dispersion energies still achieved R2 = 0.99, whereas R2 decreased to 0.32 employing all the energy terms of PIEDA.
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Affiliation(s)
- Kenichiro Takaba
- Pharmaceutical Research Center, Advanced Drug Discovery, Asahi Kasei Pharma Corporation, Shizuoka, Japan
| | - Chiduru Watanabe
- RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama, Kanagawa, Japan
| | - Atsushi Tokuhisa
- RIKEN Center for Computational Science, Chuo-ku, Kobe, Hyogo, Japan
| | - Yoshinobu Akinaga
- RIKEN Center for Computational Science, Chuo-ku, Kobe, Hyogo, Japan.,Project Development Department, VINAS Co., Ltd., Osaka, Japan
| | - Biao Ma
- RIKEN Center for Computational Science, Chuo-ku, Kobe, Hyogo, Japan
| | - Ryo Kanada
- RIKEN Center for Computational Science, Chuo-ku, Kobe, Hyogo, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- RIKEN Center for Computational Science, Chuo-ku, Kobe, Hyogo, Japan.,Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yusuke Kawashima
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, Tokyo, Japan
| | - Hirotomo Moriwaki
- RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama, Kanagawa, Japan
| | | | - Teruki Honma
- RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama, Kanagawa, Japan
| | - Kaori Fukuzawa
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, Tokyo, Japan.,Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.,Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Osaka, Japan
| | - Shigenori Tanaka
- Graduate School of System Informatics, Kobe University, Kobe, Japan
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6
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Sensitivity to dabrafenib and trametinib treatments in patients with non-small-cell cancer harboring BRAF compound mutations: A pooled analysis of BRAF p.V600E-positive advanced non-small-cell lung cancer. Cancer Genet 2022; 266-267:1-6. [DOI: 10.1016/j.cancergen.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 03/03/2022] [Accepted: 05/01/2022] [Indexed: 11/20/2022]
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7
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Kori S, Shibahashi Y, Ekimoto T, Nishiyama A, Yoshimi S, Yamaguchi K, Nagatoishi S, Ohta M, Tsumoto K, Nakanishi M, Defossez PA, Ikeguchi M, Arita K. Structure-based screening combined with computational and biochemical analyses identified the inhibitor targeting the binding of DNA Ligase 1 to UHRF1. Bioorg Med Chem 2021; 52:116500. [PMID: 34801826 DOI: 10.1016/j.bmc.2021.116500] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/02/2021] [Accepted: 11/02/2021] [Indexed: 01/04/2023]
Abstract
The accumulation of epigenetic alterations is one of the major causes of tumorigenesis. Aberrant DNA methylation patterns cause genome instability and silencing of tumor suppressor genes in various types of tumors. Therefore, drugs that target DNA methylation-regulating factors have great potential for cancer therapy. Ubiquitin-like containing PHD and RING finger domain 1 (UHRF1) is an essential factor for DNA methylation maintenance. UHRF1 is overexpressed in various cancer cells and down-regulation of UHRF1 in these cells reactivates the expression of tumor suppressor genes, thus UHRF1 is a promising target for cancer therapy. We have previously shown that interaction between the tandem Tudor domain (TTD) of UHRF1 and DNA ligase 1 (LIG1) di/trimethylated on Lys126 plays a key role in the recruitment of UHRF1 to replication sites and replication-coupled DNA methylation maintenance. An arginine binding cavity (Arg-binding cavity) of the TTD is essential for LIG1 interaction, thus the development of inhibitors that target the Arg-binding cavity could potentially repress UHRF1 function in cancer cells. To develop such an inhibitor, we performed in silico screening using not only static but also dynamic metrics based on all-atom molecular dynamics simulations, resulting in efficient identification of 5-amino-2,4-dimethylpyridine (5A-DMP) as a novel TTD-binding compound. Crystal structure of the TTD in complex with 5A-DMP revealed that the compound stably bound to the Arg-binding cavity of the TTD. Furthermore, 5A-DMP inhibits the full-length UHRF1:LIG1 interaction in Xenopus egg extracts. Our study uncovers a UHRF1 inhibitor which can be the basis of future experiments for cancer therapy.
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Affiliation(s)
- Satomi Kori
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yuki Shibahashi
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Toru Ekimoto
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Atsuya Nishiyama
- Division of Cancer Cell Biology, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Sae Yoshimi
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kosuke Yamaguchi
- Univ. Paris, Epigenetics and Cell Fate, UMR 7216 CNRS, 75013 Paris, France
| | - Satoru Nagatoishi
- Institute of Medical Sciences, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Masateru Ohta
- HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama 230-0045, Japan
| | - Kouhei Tsumoto
- Institute of Medical Sciences, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan; Department of Bioengineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Makoto Nakanishi
- Division of Cancer Cell Biology, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | | | - Mitsunori Ikeguchi
- Computational Life Science Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan; HPC- and AI-driven Drug Development Platform Division, Center for Computational Science, RIKEN, Yokohama 230-0045, Japan
| | - Kyohei Arita
- Structural Biology Laboratory, Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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8
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Ma B, Terayama K, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y. Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. J Chem Inf Model 2021; 61:3304-3313. [PMID: 34242036 DOI: 10.1021/acs.jcim.1c00679] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.
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Affiliation(s)
- Biao Ma
- Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Kei Terayama
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yohohama, Kanagawa 230-0045, Japan
| | - Shigeyuki Matsumoto
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Yuta Isaka
- Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yoko Sasakura
- Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Yasushi Okuno
- Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
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9
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Tanaka S, Tokutomi S, Hatada R, Okuwaki K, Akisawa K, Fukuzawa K, Komeiji Y, Okiyama Y, Mochizuki Y. Dynamic Cooperativity of Ligand-Residue Interactions Evaluated with the Fragment Molecular Orbital Method. J Phys Chem B 2021; 125:6501-6512. [PMID: 34124906 DOI: 10.1021/acs.jpcb.1c03043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
By the splendid advance in computation power realized with the Fugaku supercomputer, it has become possible to perform ab initio fragment molecular orbital (FMO) calculations for thousands of dynamic structures of protein-ligand complexes in a parallel way. We thus carried out electron-correlated FMO calculations for a complex of the 3C-like (3CL) main protease (Mpro) of the new coronavirus (SARS-CoV-2) and its inhibitor N3 incorporating the structural fluctuations sampled by classical molecular dynamics (MD) simulation in hydrated conditions. Along with a statistical evaluation of the interfragment interaction energies (IFIEs) between the N3 ligand and the surrounding amino-acid residues for 1000 dynamic structure samples, in this study we applied a novel approach based on principal component analysis (PCA) and singular value decomposition (SVD) to the analysis of IFIE data in order to extract the dynamically cooperative interactions between the ligand and the residues. We found that the relative importance of each residue is modified via the structural fluctuations and that the ligand is bound in the pharmacophore in a dynamic manner through collective interactions formed by multiple residues, thus providing new insight into structure-based drug discovery.
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Affiliation(s)
- Shigenori Tanaka
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Shusuke Tokutomi
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Ryo Hatada
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Koji Okuwaki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kazuki Akisawa
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kaori Fukuzawa
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan.,Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, 6-6-11 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Yuto Komeiji
- Biomedical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan
| | - Yoshio Okiyama
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 201-9501, Japan
| | - Yuji Mochizuki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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10
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Yoshizawa T, Uchibori K, Araki M, Matsumoto S, Ma B, Kanada R, Seto Y, Oh-Hara T, Koike S, Ariyasu R, Kitazono S, Ninomiya H, Takeuchi K, Yanagitani N, Takagi S, Kishi K, Fujita N, Okuno Y, Nishio M, Katayama R. Microsecond-timescale MD simulation of EGFR minor mutation predicts the structural flexibility of EGFR kinase core that reflects EGFR inhibitor sensitivity. NPJ Precis Oncol 2021; 5:32. [PMID: 33863983 PMCID: PMC8052404 DOI: 10.1038/s41698-021-00170-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/10/2021] [Indexed: 12/30/2022] Open
Abstract
Approximately 15–30% of patients with lung cancer harbor mutations in the EGFR gene. Major EGFR mutations (>90% of EGFR-mutated lung cancer) are highly sensitive to EGFR tyrosine kinase inhibitors (TKIs). Many uncommon EGFR mutations have been identified, but little is known regarding their characteristics, activation, and sensitivity to various EGFR-TKIs, including allosteric inhibitors. We encountered a case harboring an EGFR-L747P mutation, originally misdiagnosed with EGFR-del19 mutation using a routine diagnostic EGFR mutation test, which was resistant to EGFR-TKI gefitinib. Using this minor mutation and common EGFR-activating mutations, we performed the binding free energy calculations and microsecond-timescale molecular dynamic (MD) simulations, revealing that the L747P mutation considerably stabilizes the active conformation through a salt-bridge formation between K745 and E762. We further revealed why several EGFR inhibitors, including the allosteric inhibitor, were ineffective. Our computational structural analysis strategy would be beneficial for future drug development targeting the EGFR minor mutations.
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Affiliation(s)
- Takahiro Yoshizawa
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan.,Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan.,Division of Respiratory Medicine, Toho University School of Medicine, 6-11-1, Omorinishi, Ota-ku, Tokyo, Japan.,Department of Clinical Oncology, Toho University School of Medicine, 6-11-1, Omorinishi, Ota-ku, Tokyo, Japan
| | - Ken Uchibori
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan.,Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, Japan
| | - Shigeyuki Matsumoto
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Kanagawa, Japan
| | - Biao Ma
- Research and Development Group for In Silico Drug Discovery, Center for Cluster Development and Coordination (CCD), Foundation for Biomedical Research and Innovation at Kobe (FBRI), Hyogo, Japan
| | - Ryo Kanada
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Kanagawa, Japan
| | - Yosuke Seto
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Tomoko Oh-Hara
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Sumie Koike
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Ryo Ariyasu
- Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Satoru Kitazono
- Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Hironori Ninomiya
- Division of Pathology, Cancer Institute, , Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Kengo Takeuchi
- Division of Pathology, Cancer Institute, , Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Noriko Yanagitani
- Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Satoshi Takagi
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Kazuma Kishi
- Division of Respiratory Medicine, Toho University School of Medicine, 6-11-1, Omorinishi, Ota-ku, Tokyo, Japan.,Department of Clinical Oncology, Toho University School of Medicine, 6-11-1, Omorinishi, Ota-ku, Tokyo, Japan
| | - Naoya Fujita
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, Japan
| | - Makoto Nishio
- Department of Thoracic Medical Oncology, the Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan.
| | - Ryohei Katayama
- Div. of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto-ku, Tokyo, Japan.
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11
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Gilteritinib overcomes lorlatinib resistance in ALK-rearranged cancer. Nat Commun 2021; 12:1261. [PMID: 33627640 PMCID: PMC7904790 DOI: 10.1038/s41467-021-21396-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 01/26/2021] [Indexed: 12/25/2022] Open
Abstract
ALK gene rearrangement was observed in 3%-5% of non-small cell lung cancer patients, and multiple ALK-tyrosine kinase inhibitors (TKIs) have been sequentially used. Multiple ALK-TKI resistance mutations have been identified from the patients, and several compound mutations, such as I1171N + F1174I or I1171N + L1198H are resistant to all the approved ALK-TKIs. In this study, we found that gilteritinib has an inhibitory effect on ALK-TKI-resistant single mutants and I1171N compound mutants in vitro and in vivo. Surprisingly, EML4-ALK I1171N + F1174I compound mutant-expressing tumors were not completely shrunk but regrew within a short period of time after alectinib or lorlatinib treatment. However, the relapsed tumor was markedly shrunk after switching to the gilteritinib in vivo model. In addition, gilteritinib was effective against NTRK-rearranged cancers including entrectinib-resistant NTRK1 G667C-mutant and ROS1 fusion-positive cancer.
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12
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Furuta H, Araki M, Masago K, Sagae Y, Fujita S, Seto K, Shimizu J, Horio Y, Sasaki E, Hosoda W, Katayama R, Okuno Y, Hida T. Novel Resistance Mechanisms Including L1196Q, P1094H, and R1248_D1249 Insertion in Three Patients With NSCLC After ALK Tyrosine Kinase Inhibitor Treatment. J Thorac Oncol 2020; 16:477-482. [PMID: 33166721 DOI: 10.1016/j.jtho.2020.09.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 09/23/2020] [Accepted: 09/25/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVES The purposes of this study are to clarify the details of the ALK tyrosine kinase inhibitor (TKI) resistance mechanism in rebiopsy cases and to predict novel resistance gene alterations using molecular dynamics simulation. METHODS A total of 21 patients with ALK-positive NSCLC who underwent a rebiopsy after ALK TKI failure were included in this analysis. ALK fluorescence in situ hybridization and reverse transcription polymerase chain reaction were performed with paired initial and rebiopsy tumor specimens. RESULTS Nine patients had no known ALK resistance mechanisms. Four had ALK amplification. L1196M, I1171N, and G1269A, mutations that are known to indicate resistance to ALK TKIs, were detected in one patient each. Small cell carcinoma and sarcomatoid transition were found in one case each. L1196Q, P1094H, and exon 24 76-base pair insertion were detected after the second-generation ALK TKIs. CONCLUSIONS The combination of a genetic analysis and a computational simulation model may make a prediction of resistance mechanisms for overcoming ALK TKI resistance, and the construction of a genomic and simulation fused database is important for the development of personalized medicine in this field.
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Affiliation(s)
- Hiromi Furuta
- Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Mitsugu Araki
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Katsuhiro Masago
- Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan.
| | - Yukari Sagae
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shiro Fujita
- Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Katsutoshi Seto
- Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Junichi Shimizu
- Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Yoshitsugu Horio
- Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Eiichi Sasaki
- Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Waki Hosoda
- Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
| | - Ryohei Katayama
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toyoaki Hida
- Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Aichi, Japan
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13
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Matsumoto S, Araki M, Isaka Y, Ono F, Hirohashi K, Ohashi S, Muto M, Okuno Y. E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2. Biophys J 2020; 119:628-637. [PMID: 32681823 PMCID: PMC7399495 DOI: 10.1016/j.bpj.2020.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/25/2020] [Accepted: 07/02/2020] [Indexed: 11/26/2022] Open
Abstract
Mitochondrial aldehyde dehydrogenase 2 (ALDH2), which is a homotetramer assembled by two equivalent dimers, is an important enzyme that metabolizes ethanol-derived acetaldehyde to acetate in a coenzyme-dependent manner. The highly reactive acetaldehyde exhibits a toxic effect, indicating that the proper functioning of ALDH2 is essential to counteract aldehyde-associated diseases. It is known that the catalytic activity of ALDH2 is drastically impaired by a frequently observed mutation, E487K, in a dominant fashion. However, the molecular basis of the inactivation mechanism is elusive because of the complex nature of the dynamic behavior. Here, we performed microsecond-timescale molecular dynamics simulations of the proteins complexed with coenzymes. The E487K mutation elevated the conformational heterogeneity of the dimer interfaces, which are relatively distal from the substituted residue. Dynamic network analyses showed that Glu487 and the dimer interface were dynamically communicated, and the dynamic community further spanned throughout all of the subunits in the wild-type; however, this network was completely rearranged by the E487K mutation. The perturbation of the dynamic properties led to alterations of the global conformational motions and destabilization of the coenzyme binding required for receiving a proton from the catalytic nucleophile. The insights into the dynamic behavior of the dominant negative mutant in this work will provide clues to restore its function.
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Affiliation(s)
- Shigeyuki Matsumoto
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Kanagawa, Japan
| | - Mitsugu Araki
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuta Isaka
- Research and Development Group for In Silico Drug Discovery, Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe 6-3-5, Minatojima-Minamimachi Kobe, Hyogo, Japan
| | - Fumie Ono
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kenshiro Hirohashi
- Department of Therapeutic Oncology, Graduate School of Medicine, Kyoto University, Shogoin, Kyoto, Japan
| | - Shinya Ohashi
- Department of Therapeutic Oncology, Graduate School of Medicine, Kyoto University, Shogoin, Kyoto, Japan
| | - Manabu Muto
- Department of Therapeutic Oncology, Graduate School of Medicine, Kyoto University, Shogoin, Kyoto, Japan
| | - Yasushi Okuno
- Medical Sciences Innovation Hub Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Kanagawa, Japan; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Research and Development Group for In Silico Drug Discovery, Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe 6-3-5, Minatojima-Minamimachi Kobe, Hyogo, Japan.
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14
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Oshima H, Re S, Sugita Y. Prediction of Protein–Ligand Binding Pose and Affinity Using the gREST+FEP Method. J Chem Inf Model 2020; 60:5382-5394. [DOI: 10.1021/acs.jcim.0c00338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Suyong Re
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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15
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Wang A, Zhang Y, Chu H, Liao C, Zhang Z, Li G. Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking. J Chem Inf Model 2020; 60:2939-2950. [PMID: 32383873 DOI: 10.1021/acs.jcim.9b01168] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.
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Affiliation(s)
- Anhui Wang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China.,Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Yuebin Zhang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Huiying Chu
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Chenyi Liao
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Zhichao Zhang
- State Key Laboratory of Fine Chemicals, School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Guohui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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16
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Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method. Sci Rep 2020; 10:2161. [PMID: 32034220 PMCID: PMC7005789 DOI: 10.1038/s41598-020-58877-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 01/20/2020] [Indexed: 12/27/2022] Open
Abstract
While molecular-targeted drugs have demonstrated strong therapeutic efficacy against diverse diseases such as cancer and infection, the appearance of drug resistance associated with genetic variations in individual patients or pathogens has severely limited their clinical efficacy. Therefore, precision medicine approaches based on the personal genomic background provide promising strategies to enhance the effectiveness of molecular-targeted therapies. However, identifying drug resistance mutations in individuals by combining DNA sequencing and in vitro analyses is generally time consuming and costly. In contrast, in silico computation of protein-drug binding free energies allows for the rapid prediction of drug sensitivity changes associated with specific genetic mutations. Although conventional alchemical free energy computation methods have been used to quantify mutation-induced drug sensitivity changes in some protein targets, these methods are often adversely affected by free energy convergence. In this paper, we demonstrate significant improvements in prediction performance and free energy convergence by employing an alchemical mutation protocol, MutationFEP, which directly estimates binding free energy differences associated with protein mutations in three types of a protein and drug system. The superior performance of MutationFEP appears to be attributable to its more-moderate perturbation scheme. Therefore, this study provides a deeper level of insight into computer-assisted precision medicine.
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17
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Computational basis for the design of PLK-2 inhibitors. Struct Chem 2020. [DOI: 10.1007/s11224-019-01394-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Negami T, Araki M, Okuno Y, Terada T. Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs. Sci Rep 2019; 9:16586. [PMID: 31719645 PMCID: PMC6851376 DOI: 10.1038/s41598-019-53120-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 10/28/2019] [Indexed: 11/11/2022] Open
Abstract
The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel that plays an essential role in the repolarization of action potentials in cardiac muscle. However, various drugs can block the ion current by binding to the hERG channel, resulting in potentially lethal cardiac arrhythmia. Accordingly, in silico studies are necessary to clarify the mechanisms of how these drugs bind to the hERG channel. Here, we used the experimental structure of the hERG channel, determined by cryo-electron microscopy, to perform docking simulations to predict the complex structures that occur between the hERG channel and structurally diverse drugs. The absolute binding free energies for the models were calculated using the MP-CAFEE method; calculated values were well correlated with experimental ones. By applying the regression equation obtained here, the affinity of a drug for the hERG channel can be accurately predicted from the calculated value of the absolute binding free energy.
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Affiliation(s)
- Tatsuki Negami
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tohru Terada
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan. .,Interfaculty Initiative in Information Studies, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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19
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Ikemura S, Yasuda H, Matsumoto S, Kamada M, Hamamoto J, Masuzawa K, Kobayashi K, Manabe T, Arai D, Nakachi I, Kawada I, Ishioka K, Nakamura M, Namkoong H, Naoki K, Ono F, Araki M, Kanada R, Ma B, Hayashi Y, Mimaki S, Yoh K, Kobayashi SS, Kohno T, Okuno Y, Goto K, Tsuchihara K, Soejima K. Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations. Proc Natl Acad Sci U S A 2019; 116:10025-10030. [PMID: 31043566 PMCID: PMC6525482 DOI: 10.1073/pnas.1819430116] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3,779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R2 = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.
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Affiliation(s)
- Shinnosuke Ikemura
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
- Keio Cancer Center, Keio University School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
- Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan
| | - Hiroyuki Yasuda
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan;
| | - Shingo Matsumoto
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577 Chiba, Japan
| | - Mayumi Kamada
- Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan
| | - Junko Hamamoto
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Keita Masuzawa
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Keigo Kobayashi
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Tadashi Manabe
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Daisuke Arai
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Ichiro Nakachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Ichiro Kawada
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Kota Ishioka
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
- Tokyo Saiseikai Central Hospital, Minato-ku, 108-0073 Tokyo, Japan
| | - Morio Nakamura
- Tokyo Saiseikai Central Hospital, Minato-ku, 108-0073 Tokyo, Japan
| | - Ho Namkoong
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Katsuhiko Naoki
- Keio Cancer Center, Keio University School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
| | - Fumie Ono
- Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan
| | - Ryo Kanada
- Compass to Healthy Life Research Complex Program, RIKEN, Kobe, 650-0047 Hyogo, Japan
| | - Biao Ma
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundation for Biomedical Research and Innovation, Kobe, 650-0047 Hyogo, Japan
| | - Yuichiro Hayashi
- Department of Pathology, Keio University School of Medicine, 160-8582 Tokyo, Japan
| | - Sachiyo Mimaki
- Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan
| | - Kiyotaka Yoh
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577 Chiba, Japan
| | - Susumu S Kobayashi
- Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan
- Division of Hematology/Oncology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02215
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, 104-0045 Tokyo, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Shogoin Sakyo-ku, 606-8507 Kyoto, Japan
| | - Koichi Goto
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577 Chiba, Japan
| | - Katsuya Tsuchihara
- Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, 277-8577 Chiba, Japan;
| | - Kenzo Soejima
- Division of Pulmonary Medicine, Department of Medicine, Keio University, School of Medicine, Shinjuku-ku, 160-8582 Tokyo, Japan
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20
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Okada K, Araki M, Sakashita T, Ma B, Kanada R, Yanagitani N, Horiike A, Koike S, Oh-Hara T, Watanabe K, Tamai K, Maemondo M, Nishio M, Ishikawa T, Okuno Y, Fujita N, Katayama R. Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance. EBioMedicine 2019; 41:105-119. [PMID: 30662002 PMCID: PMC6441848 DOI: 10.1016/j.ebiom.2019.01.019] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 01/08/2019] [Accepted: 01/08/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Alectinib has shown a greater efficacy to ALK-rearranged non-small-cell lung cancers in first-line setting; however, most patients relapse due to acquired resistance, such as secondary mutations in ALK including I1171N and G1202R. Although ceritinib or lorlatinib was shown to be effective to these resistant mutants, further resistance often emerges due to ALK-compound mutations in relapse patients following the use of ceritinib or lorlatinib. However, the drug for overcoming resistance has not been established yet. METHODS We established lorlatinib-resistant cells harboring ALK-I1171N or -G1202R compound mutations by performing ENU mutagenesis screening or using an in vivo mouse model. We performed drug screening to overcome the lorlatinib-resistant ALK-compound mutations. To evaluate these resistances in silico, we developed a modified computational molecular dynamic simulation (MP-CAFEE). FINDINGS We identified 14 lorlatinib-resistant ALK-compound mutants, including several mutants that were recently discovered in lorlatinib-resistant patients. Some of these compound mutants were found to be sensitive to early generation ALK-TKIs and several BCR-ABL inhibitors. Using our original computational simulation, we succeeded in demonstrating a clear linear correlation between binding free energy and in vitro experimental IC50 value of several ALK-TKIs to single- or compound-mutated EML4-ALK expressing Ba/F3 cells and in recapitulating the tendency of the binding affinity reduction by double mutations found in this study. Computational simulation revealed that ALK-L1256F single mutant conferred resistance to lorlatinib but increased the sensitivity to alectinib. INTERPRETATION We discovered lorlatinib-resistant multiple ALK-compound mutations and an L1256F single mutation as well as the potential therapeutic strategies for these ALK mutations. Our original computational simulation to calculate the binding affinity may be applicable for predicting resistant mutations and for overcoming drug resistance in silico. FUND: This work was mainly supported by MEXT/JSPS KAKENHI Grants and AMED Grants.
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Affiliation(s)
- Koutaroh Okada
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Mitsugu Araki
- RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Takuya Sakashita
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Biao Ma
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundation for Biomedical Research and Innovation (FBRI), 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Ryo Kanada
- RIKEN Compass to Healthy Life Research Complex Program, 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Noriko Yanagitani
- Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Atsushi Horiike
- Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Sumie Koike
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tomoko Oh-Hara
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kana Watanabe
- Department of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan
| | - Keiichi Tamai
- Division of Cancer Stem Cell, Miyagi Cancer Center Research Institute, Miyagi, Japan
| | - Makoto Maemondo
- Department of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan
| | - Makoto Nishio
- Department of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Takeshi Ishikawa
- Department of Molecular Microbiology and Immunology, Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
| | - Yasushi Okuno
- RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Naoya Fujita
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Ryohei Katayama
- Division of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan.
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21
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Araki M, Iwata H, Ma B, Fujita A, Terayama K, Sagae Y, Ono F, Tsuda K, Kamiya N, Okuno Y. Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach. J Comput Chem 2018; 39:2679-2689. [DOI: 10.1002/jcc.25715] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 09/19/2018] [Accepted: 09/25/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Mitsugu Araki
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- RIKEN Advanced Institute for Computational Sciences; 7-1-26 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Hiroaki Iwata
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Biao Ma
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Atsuto Fujita
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Kei Terayama
- Department of Computational Biology and Medical Sciences; Graduate School of Frontier Sciences, The University of Tokyo; Chiba 277-8561 Japan
| | - Yukari Sagae
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
| | - Fumie Ono
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Sciences; Graduate School of Frontier Sciences, The University of Tokyo; Chiba 277-8561 Japan
| | - Narutoshi Kamiya
- Graduate School of Simulation Studies; University of Hyogo; 7-1-28 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Yasushi Okuno
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- RIKEN Advanced Institute for Computational Sciences; 7-1-26 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
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22
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Takemura K, Sato C, Kitao A. ColDock: Concentrated Ligand Docking with All-Atom Molecular Dynamics Simulation. J Phys Chem B 2018; 122:7191-7200. [PMID: 29993242 DOI: 10.1021/acs.jpcb.8b02756] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We propose a simple but efficient and accurate method to generate protein-ligand complex structures, called Concentrated ligand Docking (ColDock). This method consists of multiple independent molecular dynamics simulations in which ligands are initially distributed randomly around a protein at relatively high concentration (∼100 mM). This condition significantly increases the probability of the ligand exploring the protein surface, which induces spontaneous ligand binding to the correct binding sites within a 100 ns MD. After clustering of the protein-bound ligand poses, representatives of the populationally dominant clusters are considered as predicted ligand poses. We applied ColDock to four cases starting from holo protein structures and showed that ColDock can generate "correct" ligand poses very similar to the crystal complex structures. Correct ligand poses are also well reproduced in three out of four cases started from apo structures, with the exception being a case with an initially closed binding pocket. The results indicate that ColDock can be used as a protein-ligand docking as long as the ligand binding pocket is initially open. Plausible protein-ligand complex structures can be easily generated by conducting the ColDock procedure using standard MD simulation software.
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Affiliation(s)
| | | | - Akio Kitao
- School of Life Science and Technology , Tokyo Institute of Technology , 2 Chome-12-1 , Ookayama, Meguro, Tokyo 152-8550 , Japan
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23
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Kawai R, Araki M, Yoshimura M, Kamiya N, Ono M, Saji H, Okuno Y. Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods. ACS Chem Neurosci 2018; 9:957-966. [PMID: 29381047 DOI: 10.1021/acschemneuro.7b00389] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Development of new diagnostic imaging probes for Alzheimer's disease, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) probes, has been strongly desired. In this study, we investigated the most accessible amyloid β (Aβ) binding site of [123I]IMPY, a Thioflavin-T-derived SPECT probe, using experimental and computational methods. First, we performed a competitive inhibition assay with Orange-G, which recognizes the KLVFFA region in Aβ fibrils, suggesting that IMPY and Orange-G bind to different sites in Aβ fibrils. Next, we precisely predicted the IMPY binding site on a multiple-protofilament Aβ fibril model using computational approaches, consisting of molecular dynamics and docking simulations. We generated possible IMPY-binding structures using docking simulations to identify candidates for probe-binding sites. The binding free energy of IMPY with the Aβ fibril was calculated by a free energy simulation method, MP-CAFEE. These computational results suggest that IMPY preferentially binds to an interfacial pocket located between two protofilaments and is stabilized mainly through hydrophobic interactions. Finally, our computational approach was validated by comparing it with the experimental results. The present study demonstrates the possibility of computational approaches to screen new PET/SPECT probes for Aβ imaging.
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Affiliation(s)
- Ryoko Kawai
- Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Mitsugu Araki
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
- RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, JAPAN
| | - Masashi Yoshimura
- Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Narutoshi Kamiya
- Graduate School of Simulation Studies, University of Hyogo, 7-1-28 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Masahiro Ono
- Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Hideo Saji
- Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
- RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, JAPAN
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24
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Bhati AP, Wan S, Hu Y, Sherborne B, Coveney PV. Uncertainty Quantification in Alchemical Free Energy Methods. J Chem Theory Comput 2018; 14:2867-2880. [PMID: 29678106 PMCID: PMC6095638 DOI: 10.1021/acs.jctc.7b01143] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Alchemical
free energy methods have gained much importance recently
from several reports of improved ligand–protein binding affinity
predictions based on their implementation using molecular dynamics
simulations. A large number of variants of such methods implementing
different accelerated sampling techniques and free energy estimators
are available, each claimed to be better than the others in its own
way. However, the key features of reproducibility and quantification
of associated uncertainties in such methods have barely been discussed.
Here, we apply a systematic protocol for uncertainty quantification
to a number of popular alchemical free energy methods, covering both
absolute and relative free energy predictions. We show that a reliable
measure of error estimation is provided by ensemble simulation—an
ensemble of independent MD simulations—which applies irrespective
of the free energy method. The need to use ensemble methods is fundamental
and holds regardless of the duration of time of the molecular dynamics
simulations performed.
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Affiliation(s)
- Agastya P Bhati
- Centre for Computational Science, Department of Chemistry , University College London , 20 Gordon Street , London WC1H 0AJ , United Kingdom
| | - Shunzhou Wan
- Centre for Computational Science, Department of Chemistry , University College London , 20 Gordon Street , London WC1H 0AJ , United Kingdom
| | - Yuan Hu
- Modeling and Informatics , Merck & Co., Inc. , 2000 Galloping Hill Road , Kenilworth , New Jersey 07033 , United States
| | - Brad Sherborne
- Modeling and Informatics , Merck & Co., Inc. , 2000 Galloping Hill Road , Kenilworth , New Jersey 07033 , United States
| | - Peter V Coveney
- Centre for Computational Science, Department of Chemistry , University College London , 20 Gordon Street , London WC1H 0AJ , United Kingdom
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25
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Abstract
Receptor tyrosine kinase signalling pathways have been successfully targeted to inhibit proliferation and angiogenesis for cancer therapy. However, kinase deregulation has been firmly demonstrated to play an essential role in virtually all major disease areas. Kinase inhibitor drug discovery programmes have recently broadened their focus to include an expanded range of kinase targets and therapeutic areas. In this Review, we provide an overview of the novel targets, biological processes and disease areas that kinase-targeting small molecules are being developed against, highlight the associated challenges and assess the strategies and technologies that are enabling efficient generation of highly optimized kinase inhibitors.
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26
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Barlow KA, Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation. J Phys Chem B 2018; 122:5389-5399. [PMID: 29401388 DOI: 10.1021/acs.jpcb.7b11367] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.
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Affiliation(s)
- Kyle A Barlow
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America
| | - Shane Ó Conchúir
- California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America
| | - Samuel Thompson
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - Pooja Suresh
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - James E Lucas
- Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America
| | - Markus Heinonen
- Department of Computer Science , Aalto University , Espoo , Finland.,Helsinki Institute for Information Technology (HIIT) , Helsinki , Finland
| | - Tanja Kortemme
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America.,California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America.,Chan Zuckerberg Biohub , San Francisco , California 94158 , United States
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27
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A secondary RET mutation in the activation loop conferring resistance to vandetanib. Nat Commun 2018; 9:625. [PMID: 29434222 PMCID: PMC5809600 DOI: 10.1038/s41467-018-02994-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 01/08/2018] [Indexed: 01/09/2023] Open
Abstract
Resistance to vandetanib, a type I RET kinase inhibitor, developed in a patient with metastatic lung adenocarcinoma harboring a CCDC6-RET fusion that initially exhibited a response to treatment. The resistant tumor acquired a secondary mutation resulting in a serine-to-phenylalanine substitution at codon 904 in the activation loop of the RET kinase domain. The S904F mutation confers resistance to vandetanib by increasing the ATP affinity and autophosphorylation activity of RET kinase. A reduced interaction with the drug is also observed in vitro for the S904F mutant by thermal shift assay. A crystal structure of the S904F mutant reveals a small hydrophobic core around F904 likely to enhance basal kinase activity by stabilizing an active conformer. Our findings indicate that missense mutations in the activation loop of the kinase domain are able to increase kinase activity and confer drug resistance through allosteric effects. Mechanisms of acquired resistance to RET tyrosine kinase inhibitors in lung cancers are largely unknown. Here, the authors report in a lung adenocarcinoma patient harboring a CCDC6-RET mutation in the RET kinase (S904F) that results in resistance to the kinase inhibitor vandetanib by increasing the ATP affinity and autophosphorylation activity of RET kinase.
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28
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Bekker GJ, Kamiya N, Araki M, Fukuda I, Okuno Y, Nakamura H. Accurate Prediction of Complex Structure and Affinity for a Flexible Protein Receptor and Its Inhibitor. J Chem Theory Comput 2017; 13:2389-2399. [DOI: 10.1021/acs.jctc.6b01127] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gert-Jan Bekker
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Narutoshi Kamiya
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Simulation Studies, University of Hyogo, 7-1-28 Minatojima
Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Mitsugu Araki
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Medicine, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Ikuo Fukuda
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yasushi Okuno
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima Minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Graduate
School of Medicine, Kyoto University, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Haruki Nakamura
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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