1
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Sidgwick GP, Weston R, Mahmoud AM, Schiro A, Serracino-Inglott F, Tandel SM, Skeoch S, Bruce IN, Jones AM, Alexander MY, Wilkinson FL. Novel Glycomimetics Protect against Glycated Low-Density Lipoprotein-Induced Vascular Calcification In Vitro via Attenuation of the RAGE/ERK/CREB Pathway. Cells 2024; 13:312. [PMID: 38391925 PMCID: PMC10887290 DOI: 10.3390/cells13040312] [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: 12/29/2023] [Revised: 02/01/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Heparan sulphate (HS) can act as a co-receptor on the cell surface and alterations in this process underpin many pathological conditions. We have previously described the usefulness of mimics of HS (glycomimetics) in protection against β-glycerophosphate-induced vascular calcification and in the restoration of the functional capacity of diabetic endothelial colony-forming cells in vitro. This study aims to investigate whether our novel glycomimetic compounds can attenuate glycated low-density lipoprotein (g-LDL)-induced calcification by inhibiting RAGE signalling within the context of critical limb ischemia (CLI). We used an established osteogenic in vitro vascular smooth muscle cell (VSMC) model. Osteoprotegerin (OPG), sclerostin and glycation levels were all significantly increased in CLI serum compared to healthy controls, while the vascular calcification marker osteocalcin (OCN) was down-regulated in CLI patients vs. controls. Incubation with both CLI serum and g-LDL (10 µg/mL) significantly increased VSMC calcification vs. controls after 21 days, with CLI serum-induced calcification apparent after only 10 days. Glycomimetics (C2 and C3) significantly inhibited g-LDL and CLI serum-induced mineralisation, as shown by a reduction in alizarin red (AR) staining and alkaline phosphatase (ALP) activity. Furthermore, secretion of the osteogenic marker OCN was significantly reduced in VSMCs incubated with CLI serum in the presence of glycomimetics. Phosphorylation of cyclic AMP response element-binding protein (CREB) was significantly increased in g-LDL-treated cells vs. untreated controls, which was attenuated with glycomimetics. Blocking CREB activation with a pharmacological inhibitor 666-15 replicated the protective effects of glycomimetics, evidenced by elevated AR staining. In silico molecular docking simulations revealed the binding affinity of the glycomimetics C2 and C3 with the V domain of RAGE. In conclusion, these findings demonstrate that novel glycomimetics, C2 and C3 have potent anti-calcification properties in vitro, inhibiting both g-LDL and CLI serum-induced VSMC mineralisation via the inhibition of LDLR, RAGE, CREB and subsequent expression of the downstream osteogenic markers, ALP and OCN.
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Affiliation(s)
- Gary P. Sidgwick
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
| | - Ria Weston
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
| | - Ayman M. Mahmoud
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
| | - Andrew Schiro
- Cardiovascular Research Institute, University of Manchester, Manchester M13 9PL, UK;
- Vascular Unit, Manchester University Hospitals NHS Foundation Trust, Manchester M13 9WL, UK
| | - Ferdinand Serracino-Inglott
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
- Cardiovascular Research Institute, University of Manchester, Manchester M13 9PL, UK;
- Vascular Unit, Manchester University Hospitals NHS Foundation Trust, Manchester M13 9WL, UK
| | - Shikha M. Tandel
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
| | - Sarah Skeoch
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester M13 9PL, UK; (S.S.); (I.N.B.)
- National Institute for Health Research Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
- Royal National Hospital for Rheumatic Diseases, Bath BA1 1RL, UK
| | - Ian N. Bruce
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester M13 9PL, UK; (S.S.); (I.N.B.)
- National Institute for Health Research Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
| | - Alan M. Jones
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
- School of Pharmacy, University of Birmingham, Birmingham B15 2TT, UK
| | - M. Yvonne Alexander
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
| | - Fiona L. Wilkinson
- Department of Life Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK (R.W.); (A.M.M.); (F.S.-I.); (S.M.T.); (A.M.J.); (M.Y.A.)
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2
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Panwar U, Murali A, Khan MA, Selvaraj C, Singh SK. Virtual Screening Process: A Guide in Modern Drug Designing. Methods Mol Biol 2024; 2714:21-31. [PMID: 37676591 DOI: 10.1007/978-1-0716-3441-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.
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Affiliation(s)
- Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Aarthy Murali
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Mohammad Aqueel Khan
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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3
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Yoshino R, Yasuo N, Hagiwara Y, Ishida T, Inaoka DK, Amano Y, Tateishi Y, Ohno K, Namatame I, Niimi T, Orita M, Kita K, Akiyama Y, Sekijima M. Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography. ACS OMEGA 2023; 8:25850-25860. [PMID: 37521650 PMCID: PMC10373461 DOI: 10.1021/acsomega.3c01314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023]
Abstract
In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.
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Affiliation(s)
- Ryunosuke Yoshino
- Transborder
Medical Research Center, University of Tsukuba, Tsukuba 305-8577, Japan
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Nobuaki Yasuo
- Tokyo
Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, Meguro, Tokyo 152-8550, Japan
| | - Yohsuke Hagiwara
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Takashi Ishida
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Daniel Ken Inaoka
- School of
Tropical Medicine and Global Health, Nagasaki
University, Sakamoto, Nagasaki 852-8523, Japan
- Department
of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yasushi Amano
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Yukihiro Tateishi
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Kazuki Ohno
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Ichiji Namatame
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Tatsuya Niimi
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Masaya Orita
- Medicinal
Chemistry Research Labs, Drug Discovery Research, Astellas Pharma Inc, Miyukigaoka, Tsukuba 305-8585, Japan
| | - Kiyoshi Kita
- School of
Tropical Medicine and Global Health, Nagasaki
University, Sakamoto, Nagasaki 852-8523, Japan
- Department
of Biomedical Chemistry, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Yutaka Akiyama
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Masakazu Sekijima
- Education
Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama 226-8501, Japan
- School
of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
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4
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches. Front Mol Biosci 2023; 10:1110567. [PMID: 36814641 PMCID: PMC9939524 DOI: 10.3389/fmolb.2023.1110567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry Education, Sunchon National University, Suncheon, South Korea,Department of Advanced Components and Materials Engineering, Sunchon National University, Suncheon, South Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Takatsugu Hirokawa
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States,Department of Computer Science, Purdue University, West Lafayette, IN, United States,Center for Cancer Research, Purdue University, West Lafayette, IN, United States,*Correspondence: Daisuke Kihara,
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5
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Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes. Int J Mol Sci 2022; 23:ijms23094749. [PMID: 35563139 PMCID: PMC9103889 DOI: 10.3390/ijms23094749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/18/2022] [Accepted: 04/23/2022] [Indexed: 02/01/2023] Open
Abstract
To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.
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6
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Yamamoto K, Yasuo N, Sekijima M. Screening for Inhibitors of Main Protease in SARS-CoV-2: In Silico and In Vitro Approach Avoiding Peptidyl Secondary Amides. J Chem Inf Model 2022; 62:350-358. [PMID: 35015543 PMCID: PMC8767656 DOI: 10.1021/acs.jcim.1c01087] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Indexed: 01/01/2023]
Abstract
In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space, excluding peptidyl secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates of >5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 10-4 M. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.
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Affiliation(s)
- Kazuki
Z. Yamamoto
- Department
of Computer Science, Tokyo Institute of
Technology, Yokohama, 226-8501, Japan
| | - Nobuaki Yasuo
- Academy
for Convergence of Materials and Informatics, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
| | - Masakazu Sekijima
- Department
of Computer Science, Tokyo Institute of
Technology, Yokohama, 226-8501, Japan
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7
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Erikawa D, Yasuo N, Sekijima M. MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning. J Cheminform 2021; 13:94. [PMID: 34838134 PMCID: PMC8626955 DOI: 10.1186/s13321-021-00572-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 11/15/2021] [Indexed: 12/05/2022] Open
Abstract
The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid.
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Affiliation(s)
- Daiki Erikawa
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, Japan
| | - Nobuaki Yasuo
- Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan
| | - Masakazu Sekijima
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, Japan. .,Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
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Sarkar MK, Kar A, Jayaraman A, Kar Mahapatra S, Vadivel V. Vitexin isolated from Prosopis cineraria leaves induce apoptosis in K-562 leukemia cells via inhibition of the BCR-ABL-Ras-Raf pathway. J Pharm Pharmacol 2021; 74:103-111. [PMID: 34109977 DOI: 10.1093/jpp/rgab085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 05/14/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Leukemia is one of the severe cancer types all around the globe. Even though some chemotherapeutic drugs are available for treating leukemia, they have various side effects. As an alternative approach, herbal drugs are focused on current research to overcome leukemia. The present work was conducted to investigate the antileukemic mechanism of active phytochemical vitexin, which was isolated from ethno-medicine (Prosopis cineraria leaf) used by traditional healers of West Bengal, India. METHODS Antiproliferative mechanisms of selected phyto-compound against K-562 cells were evaluated using cellular uptake, morphological changes, DNA fragmentation, mitochondrial membrane potential and signaling pathways analysis. KEY FINDINGS Vitexin exhibited cytotoxicity by reducing mitochondrial membrane potential (32.40%) and causing DNA fragmentation (84.15%). The western blotting study indicated inhibition of cell survival proteins (BCR, ABL, H-RAS, N-RAS, K-RAS and RAF) and expression of apoptotic proteins (p38, BAX and caspase-9) in leukemia cells upon treatment with vitexin. CONCLUSIONS Based on the results, presently investigated phyto-compound vitexin could be considered for developing safe and natural drugs to treat leukemia after conducting suitable preclinical and clinical trials.
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Affiliation(s)
- Monaj Kumar Sarkar
- Chemical Biology Lab (ASK-II-409), School of Chemical and Biotechnology (SCBT), SASTRA Deemed University, Thanjavur, India
| | - Amrita Kar
- Medicinal Chemistry and Immunology Lab (ASK-II-406), School of Chemical and Biotechnology (SCBT), SASTRA Deemed University, Thanjavur, India
| | - Adithyan Jayaraman
- Medicinal Chemistry and Immunology Lab (ASK-II-406), School of Chemical and Biotechnology (SCBT), SASTRA Deemed University, Thanjavur, India
| | - Santanu Kar Mahapatra
- Medicinal Chemistry and Immunology Lab (ASK-II-406), School of Chemical and Biotechnology (SCBT), SASTRA Deemed University, Thanjavur, India
| | - Vellingiri Vadivel
- Chemical Biology Lab (ASK-II-409), School of Chemical and Biotechnology (SCBT), SASTRA Deemed University, Thanjavur, India
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Yasuo N, Ishida T, Sekijima M. Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project. Parasitol Int 2021; 83:102366. [PMID: 33915269 DOI: 10.1016/j.parint.2021.102366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/23/2021] [Accepted: 04/07/2021] [Indexed: 01/09/2023]
Abstract
Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.
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Affiliation(s)
- Nobuaki Yasuo
- Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, S6-23, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
| | - Takashi Ishida
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, W8-85, 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan.
| | - Masakazu Sekijima
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, Yokohama, 226-8501, Japan.
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10
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Yoshino R, Yasuo N, Sekijima M. Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci Rep 2020; 10:12493. [PMID: 32719454 PMCID: PMC7385649 DOI: 10.1038/s41598-020-69337-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 07/01/2020] [Indexed: 01/08/2023] Open
Abstract
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 SARS-CoV 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 1 μs 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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Affiliation(s)
- Ryunosuke Yoshino
- Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Nobuaki Yasuo
- Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | - Masakazu Sekijima
- Tokyo Tech Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.
- School of Computing, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.
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11
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Esaki T, Kumazawa K, Takahashi K, Watanabe R, Masuda T, Watanabe H, Shimizu Y, Okada A, Takimoto S, Shimada T, Ikeda K. Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development. CHEM-BIO INFORMATICS JOURNAL 2020. [DOI: 10.1273/cbij.20.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Tsuyoshi Esaki
- The Center for Data Science Education and Research, Shiga University
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited
| | - Kazutoshi Takahashi
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co. Inc
| | - Reiko Watanabe
- Laboratory of Bioinformatics, AI Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition
| | | | - Hirofumi Watanabe
- Education Center on Computational Science and Engineering, Kobe University
| | - Yugo Shimizu
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy
| | - Akitoshi Okada
- Central Pharmaceutical Research Institute Takatsuki Research Center, Japan Tobacco Inc
| | - Seisuke Takimoto
- Central Pharmaceutical Research Institute Takatsuki Research Center, Japan Tobacco Inc
| | - Tomohiro Shimada
- Administration Department, Planning & amp; Control Division, Teijin Pharma Limited
| | - Kazuyoshi Ikeda
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy
- Center for Life Science Technologies, RIKEN
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12
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Tutumlu G, Dogan B, Avsar T, Orhan MD, Calis S, Durdagi S. Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2. Front Chem 2020; 8:167. [PMID: 32328476 PMCID: PMC7160371 DOI: 10.3389/fchem.2020.00167] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 02/25/2020] [Indexed: 12/13/2022] Open
Abstract
Antiapoptotic members of B-cell leukemia/lymphoma-2 (BCL-2) family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets. As such, targeting of BCL-2 family proteins raises hopes for new therapeutic discoveries. Thus, we used multistep screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from a small molecule database (Specs SC), which includes more than 210,000 compounds. This database is first filtered based on binary “cancer-QSAR” model constructed with 886 training and 167 test set compounds and common 26 toxicity quantitative structure-activity relationships (QSAR) models. Predicted non-toxic compounds are considered for target-driven studies. Here, we applied two different approaches to filter and select hit compounds for further in vitro biological assays and human cell line experiments. In the first approach, a molecular docking and filtering approach is used to rank compounds based on their docking scores and only a few top-ranked molecules are selected for further long (100-ns) molecular dynamics (MD) simulations and in vitro tests. While docking algorithms are promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of in silico studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1 ns) MD simulations and their binding energies are calculated via molecular mechanics generalized Born surface area (MM/GBSA) method. Then, the compounds are ranked based on their average MM/GBSA energy values to select hit molecules for further long MD simulations and in vitro studies. Additionally, we have applied text-mining approaches to identify molecules that contain “indol” phrase as many of the approved drugs contain indole and indol derivatives. Around 2700 compounds are filtered based on “cancer-QSAR” model and are then docked into BCL-2. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based on their MM/GBSA values to select hit molecules for further long MD simulations and in vitro studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines as well as Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials are assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.
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Affiliation(s)
- Gurbet Tutumlu
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
| | - Berna Dogan
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
| | - Timucin Avsar
- Department of Medical Biology, Bahcesehir University, School of Medicine, Istanbul, Turkey.,Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
| | - Muge Didem Orhan
- Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
| | - Seyma Calis
- Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Molecular Biology, Genetics and Biotechnology Graduate Program, Istanbul Technical University, Istanbul, Turkey
| | - Serdar Durdagi
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey.,Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
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13
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Kanakaveti V, Shanmugam A, Ramakrishnan C, Anoosha P, Sakthivel R, Rayala SK, Gromiha MM. Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 121:25-47. [PMID: 32312424 DOI: 10.1016/bs.apcsb.2019.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.
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Affiliation(s)
- Vishnupriya Kanakaveti
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Anusuya Shanmugam
- Department of Pharmaceutical Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, Tamil Nadu, India
| | - C Ramakrishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - P Anoosha
- Department of Internal Medicine, Division of Medical Oncology and Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - R Sakthivel
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - S K Rayala
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Advanced Computational Drug Discovery Unit (ACDD), Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Japan
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14
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Chiba S, Ohue M, Gryniukova A, Borysko P, Zozulya S, Yasuo N, Yoshino R, Ikeda K, Shin WH, Kihara D, Iwadate M, Umeyama H, Ichikawa T, Teramoto R, Hsin KY, Gupta V, Kitano H, Sakamoto M, Higuchi A, Miura N, Yura K, Mochizuki M, Ramakrishnan C, Thangakani AM, Velmurugan D, Gromiha MM, Nakane I, Uchida N, Hakariya H, Tan M, Nakamura HK, Suzuki SD, Ito T, Kawatani M, Kudoh K, Takashina S, Yamamoto KZ, Moriwaki Y, Oda K, Kobayashi D, Okuno T, Minami S, Chikenji G, Prathipati P, Nagao C, Mohsen A, Ito M, Mizuguchi K, Honma T, Ishida T, Hirokawa T, Akiyama Y, Sekijima M. A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Sci Rep 2019; 9:19585. [PMID: 31863054 PMCID: PMC6925144 DOI: 10.1038/s41598-019-55069-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 11/21/2019] [Indexed: 12/17/2022] Open
Abstract
Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.
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Affiliation(s)
- Shuntaro Chiba
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,RIKEN Medical Sciences Innovation Hub Program, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | | | - Petro Borysko
- Bienta/Enamine Ltd., 78 Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Sergey Zozulya
- Bienta/Enamine Ltd., 78 Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Nobuaki Yasuo
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Research Fellow of the Japan Society for the Promotion of Science DC1, Tokyo, Japan
| | - Ryunosuke Yoshino
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan
| | - Kazuyoshi Ikeda
- Faculty of Pharmacy, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan
| | - Woong-Hee Shin
- Department of Biological Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, Indiana, 47907, USA.,Department of Computer Science, Purdue University, Indiana, 47907, USA
| | - Mitsuo Iwadate
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Hideaki Umeyama
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Takaaki Ichikawa
- Department of Biological Sciences, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan
| | - Reiji Teramoto
- Discovery technology research department, Research division, Chugai Pharmaceutical Co.,Ltd., 200, Kajiwara, Kamakura, Kanagawa, 247-8530, Japan
| | - Kun-Yi Hsin
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa, 904-0495, Japan
| | - Vipul Gupta
- The Systems Biology Research Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato-ku, Tokyo, 108-0071, Japan
| | - Hiroaki Kitano
- Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami, Okinawa, 904-0495, Japan.,The Systems Biology Research Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato-ku, Tokyo, 108-0071, Japan.,Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan
| | - Mika Sakamoto
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan
| | - Akiko Higuchi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Nobuaki Miura
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan
| | - Kei Yura
- Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan.,Center for Simulation Science and Informational Biology, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 112-8610, Japan.,School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan
| | - Masahiro Mochizuki
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,IMSBIO Co., Ltd., Level 6 OWL TOWER, 4-21-1 Higashi-Ikebukuro, Toshima-ku, Tokyo, 170-0013, Japan
| | - Chandrasekaran Ramakrishnan
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - A Mary Thangakani
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - D Velmurugan
- CAS in Crystallography and Biophysics and Bioinformatics Facility, University of Madras, Chennai, 600025, Tamilnadu, India
| | - M Michael Gromiha
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India
| | - Itsuo Nakane
- Okazaki City Hall, 2-9 Juo-cho Okazaki, Aichi, 444-8601, Japan
| | - Nanako Uchida
- IQVIA Services Japan K.K., 4-10-18 Takanawa Minato-ku, Tokyo, 108-0074, Japan
| | - Hayase Hakariya
- Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan.,Training Program of Leaders for Integrated Medical System (LIMS), Kyoto University, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Modong Tan
- Department of Chemistry & Biotechnology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan
| | - Hironori K Nakamura
- Biomodeling Research Co., Ltd., 1-704-2 Uedanishi, Tenpaku-ku, Nagoya, 468-0058, Japan
| | - Shogo D Suzuki
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Tomoki Ito
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Masahiro Kawatani
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Kentaroh Kudoh
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Sakurako Takashina
- Faculty of Medicine, Akita University, 1-1-1 Hondo, Akita, 010-8543, Japan
| | - Kazuki Z Yamamoto
- Isotope Science Center, The University of Tokyo, 2-11- 16, Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Yoshitaka Moriwaki
- Department of Biotechnology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Keita Oda
- Google Japan Inc., 6-10-1 Roppongi, Minato-ku, Tokyo, 106-6126, Japan.,Otemachi Bldg. 3F, 1-6-1, Preferred Networks, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Daisuke Kobayashi
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Tatsuya Okuno
- Tosei General Hospital, 160 Nishioiwake-cho, Seto, Aichi, 489-8642, Japan
| | - Shintaro Minami
- Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Furocho, Chikusa, Nagoya, 464-8601, Japan
| | - George Chikenji
- Department of Computational Science and Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8603, Japan
| | - Philip Prathipati
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Chioko Nagao
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Attayeb Mohsen
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Mari Ito
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes for Biomedical Innovation, Health and Nutrition, Osaka, 567-0085, Japan
| | - Teruki Honma
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,RIKEN Center for Biosystems Dynamic Research, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Takashi Ishida
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan
| | - Takatsugu Hirokawa
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan
| | - Yutaka Akiyama
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan.,Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan
| | - Masakazu Sekijima
- Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan. .,Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, J3-23-4259 Nagatsutacho, Midori-ku, Yokohama, 226-8501, Japan. .,Initiative for Parallel Bioinformatics, Level 14 Hibiya Central Building, 1-2-9 Nishi-Shimbashi Minato-Ku, Tokyo, 105-0003, Japan.
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15
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Yoshino R, Yasuo N, Sekijima M. Molecular Dynamics Simulation reveals the mechanism by which the Influenza Cap-dependent Endonuclease acquires resistance against Baloxavir marboxil. Sci Rep 2019; 9:17464. [PMID: 31767949 PMCID: PMC6877583 DOI: 10.1038/s41598-019-53945-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 10/24/2019] [Indexed: 12/11/2022] Open
Abstract
Baloxavir marboxil (BXM), an antiviral drug for influenza virus, inhibits RNA replication by binding to RNA replication cap-dependent endonuclease (CEN) of influenza A and B viruses. Although this drug was only approved by the FDA in October 2018, drug resistant viruses have already been detected from clinical trials owing to an I38 mutation of CEN. To investigate the reduction of drug sensitivity by the I38 mutant variants, we performed a molecular dynamics (MD) simulation on the CEN-BXM complex structure to analyze variations in the mode of interaction. Our simulation results suggest that the side chain methyl group of I38 in CEN engages in a CH-pi interaction with the aromatic ring of BXM. This interaction is abolished in various I38 mutant variants. Moreover, MD simulation on various mutation models and binding free energy prediction by MM/GBSA method suggest that the I38 mutation precludes any interaction with the aromatic ring of BXA and thereby reduces BXA sensitivity.
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Affiliation(s)
- Ryunosuke Yoshino
- Transborder Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Nobuaki Yasuo
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259-J3-23 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan
| | - Masakazu Sekijima
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259-J3-23 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
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16
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Turki T, Taguchi YH. Machine learning algorithms for predicting drugs–tissues relationships. EXPERT SYSTEMS WITH APPLICATIONS 2019. [DOI: 10.1016/j.eswa.2019.02.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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17
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Yasuo N, Sekijima M. Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning. J Chem Inf Model 2019; 59:1050-1061. [PMID: 30808172 DOI: 10.1021/acs.jcim.8b00673] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which protein-ligand interaction energies are extracted to represent docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-the-art virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at https://github.com/sekijima-lab/SIEVE-Score .
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Affiliation(s)
- Nobuaki Yasuo
- Department of Computer Science , Tokyo Institute of Technology , 4259-J3-23, Nagatsuta-cho , Midori-ku, Yokohama , Japan
| | - Masakazu Sekijima
- Department of Computer Science , Tokyo Institute of Technology , 4259-J3-23, Nagatsuta-cho , Midori-ku, Yokohama , Japan.,Advanced Computational Drug Discovery Unit , Tokyo Institute of Technology , 4259-J3-23, Nagatsuta-cho , Midori-ku, Yokohama , Japan
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18
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Homology Model and Docking-Based Virtual Screening for Ligands of Human Dyskerin as New Inhibitors of Telomerase for Cancer Treatment. Int J Mol Sci 2018; 19:ijms19103216. [PMID: 30340325 PMCID: PMC6214037 DOI: 10.3390/ijms19103216] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/06/2018] [Accepted: 10/08/2018] [Indexed: 01/31/2023] Open
Abstract
Immortality is one of the main features of cancer cells. Tumor cells have an unlimited replicative potential, principally due to the holoenzyme telomerase. Telomerase is composed mainly by dyskerin (DKC1), a catalytic retrotranscriptase (hTERT) and an RNA template (hTR). The aim of this work is to develop new inhibitors of telomerase, selecting the interaction between hTR⁻DKC1 as a target. We designed two models of the human protein DKC1: homology and ab initio. These models were evaluated by different procedures, revealing that the homology model parameters were the most accurate. We selected two hydrophobic pockets contained in the PUA (pseudouridine synthase and archaeosine transglycosylase) domain, using structural and stability analysis. We carried out a docking-based virtual screen on these pockets, using the reported mutation K314 as the center of the docking. The hDKC1 model was tested against a library of 450,000 drug-like molecules. We selected the first 10 molecules that showed the highest affinity values to test their inhibitory activity on the cell line MDA MB 231 (Monroe Dunaway Anderson Metastasis Breast cancer 231), obtaining three compounds that showed inhibitory effect. These results allowed us to validate our design and set the basis to continue with the study of telomerase inhibitors for cancer treatment.
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19
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QEX: target-specific druglikeness filter enhances ligand-based virtual screening. Mol Divers 2018; 23:11-18. [PMID: 29971617 PMCID: PMC6394530 DOI: 10.1007/s11030-018-9842-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Accepted: 06/12/2018] [Indexed: 11/22/2022]
Abstract
Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.
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20
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Arai N, Yoshikawa S, Yasuo N, Yoshino R, Sekijima M. Compound property enhancement by virtual compound synthesis. J Bioinform Comput Biol 2018; 16:1840016. [DOI: 10.1142/s0219720018400164] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
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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Affiliation(s)
- Naoki Arai
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
| | - Shunsuke Yoshikawa
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
| | - Nobuaki Yasuo
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
- Research Fellow of Japan Society for the Promotion of Science DC1, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
| | - Ryunosuke Yoshino
- Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
| | - Masakazu Sekijima
- Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, 4259-J3-23, Nagatsuta-cho, Midori-ku, 226-8502, Yokohama, Japan
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Wakui N, Yoshino R, Yasuo N, Ohue M, Sekijima M. Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach. J Mol Graph Model 2017; 79:166-174. [PMID: 29197725 DOI: 10.1016/j.jmgm.2017.11.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 11/21/2017] [Accepted: 11/21/2017] [Indexed: 10/18/2022]
Abstract
B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.
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Affiliation(s)
- Naoki Wakui
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
| | - Ryunosuke Yoshino
- Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan; Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan.
| | - Nobuaki Yasuo
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan; Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan.
| | - Masakazu Sekijima
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan; Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan; Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan.
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