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Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies. Biomolecules 2022; 12:biom12070878. [PMID: 35883434 PMCID: PMC9312989 DOI: 10.3390/biom12070878] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/05/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.
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Wang H, Wang Z, Wei C, Wang J, Xu Y, Bai G, Yao Q, Zhang L, Chen Y. Anticancer potential of indirubins in medicinal chemistry: Biological activity, structural modification, and structure-activity relationship. Eur J Med Chem 2021; 223:113652. [PMID: 34161865 DOI: 10.1016/j.ejmech.2021.113652] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 06/13/2021] [Accepted: 06/13/2021] [Indexed: 10/21/2022]
Abstract
Indirubin is the crucial ingredient of Danggui Longhui Wan and Qing-Dai, traditional Chinese medicine herbal formulas used for the therapy of chronic myelocytic leukemia in China for hundreds of years. Although the monomeric indirubin has been used in China for the treatment human chronic myelocytic leukemia. However, due to low water solubility, poor pharmacokinetic properties and low therapeutic effects are the major obstacle, and had significantly limited its clinical application. Consequently, the attractive anticancer profile of indirubin has enthused numerous researchers to discover novel indirubin derivatives with improved pharmacodynamic activity as well as good pharmacokinetic property. In this paper, we comprehensively review the recent progress of anticancer potential of indirubins, structural modification and structure-activity relationship, which may provide useful direction for the further development of novel indirubins with improved pharmacological profiles for the treatment of various types of cancer.
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Affiliation(s)
- Hezhen Wang
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China
| | - Zhiyuan Wang
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China
| | - Chunyong Wei
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China
| | - Jing Wang
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China
| | - Yingshu Xu
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China
| | - Guohui Bai
- Key Laboratory of Oral Disease of Higher Schools in Guizhou Province, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China.
| | - Qizheng Yao
- School of Pharmacy, China Pharmaceutical University, 24 Tongjia Xiang, Nanjing, 210009, PR China.
| | - Lei Zhang
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China.
| | - Yongzheng Chen
- Key Laboratory of Biocatalysis & Chiral Drug Synthesis of Guizhou Province, Key Laboratory of Basic Pharmacology of Ministry of Education, School of Pharmacy, Zunyi Medical University, 6 West Xuefu Road, Zunyi, 563000, PR China.
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3
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Ma Z, Huang SY, Cheng F, Zou X. Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases. J Phys Chem B 2021; 125:2288-2298. [PMID: 33651624 DOI: 10.1021/acs.jpcb.1c00016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.
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Affiliation(s)
- Zhiwei Ma
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Sheng-You Huang
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Fei Cheng
- McCombs School of Business, University of Texas, Austin, Texas 78712, United States
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Department of Physics and Astronomy, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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4
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Zhou W, Yang H, Wang H. Inverse in silico-in vitro fishing of unexpected paroxetine kinase targets from tumor druggable kinome. J Mol Model 2020; 26:197. [PMID: 32623519 DOI: 10.1007/s00894-020-04444-y] [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: 12/11/2019] [Accepted: 06/15/2020] [Indexed: 12/11/2022]
Abstract
The serotonin selective reuptake inhibitor paroxetine has been clinically observed to reposition a significant suppressing potency on human tumors by unexpectedly targeting diverse kinase pathways involved in tumorigenesis. Here, we describe an inverse in silico-in vitro strategy to fish potential kinase targets using the paroxetine as bait. This is different (inverse) to the traditional drug discovery process that commonly screens small-molecule inhibitors for a specific kinase target. In the procedure, cell viability assays demonstrate that paroxetine has strong cytotoxicity on human tumor cell lines. Various protooncogene protein kinases are ontologically/manually enriched to define a druggable kinome, and a systematic interaction profile of paroxetine with the kinome is created, which indicates that paroxetine can potentially bind to some known targets or key regulators of human tumors. Kinase assays determine that paroxetine can effectively inhibit c-Src family kinases at nanomolar or micromolar levels. It is observed that the paroxetine ligand forms a tightly packed interface against the active site of these unexpected kinase targets to constitute several specific hydrogen bonds/π-π/cation-π stackings and a number of nonspecific hydrophobic/vdW contacts, while exposing a portion of molecular surface to solvent. More significantly, the ligand adopts two distinct binding modes (i.e., class I and class II) to interact with different kinases; the class-I mode has a higher stability and inhibitory activity than class-II mode. Steric clash seems to cause the ligand flipping from class I to class II. Graphical abstract.
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Affiliation(s)
- Weiyan Zhou
- Department of Gynaecology, The Affiliated Huai'an Hospital of Xuzhou Medical University and the Second People's Hospital of Huai'an, Huai'an, 223002, China
| | - Hongbo Yang
- Department of Gynaecology, Huai'an Maternal and Child Health Care Center, The Affiliated Hospital of Yangzhou University Medical College, Huai'an, 223000, China
| | - Haifeng Wang
- Department of Gynaecology, Huai'an Maternal and Child Health Care Center, The Affiliated Hospital of Yangzhou University Medical College, Huai'an, 223000, China.
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5
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Prakash AV, Park JW, Seong JW, Kang TJ. Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis. Biomol Ther (Seoul) 2020; 28:222-229. [PMID: 32133828 PMCID: PMC7216745 DOI: 10.4062/biomolther.2020.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 02/11/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
The process of drug discovery and drug development consumes billions of dollars to bring a new drug to the market. Drug development is time consuming and sometimes, the failure rates are high. Thus, the pharmaceutical industry is looking for a better option for new drug discovery. Drug repositioning is a good alternative technology that has demonstrated many advantages over de novo drug development, the most important one being shorter drug development timelines. In the last two decades, drug repositioning has made tremendous impact on drug development technologies. In this review, we focus on the recent advances in drug repositioning technologies and discuss the repositioned drugs used for inflammatory diseases such as sepsis, asthma, and atopic dermatitis.
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Affiliation(s)
- Annamneedi Venkata Prakash
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Jun Woo Park
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Ju-Won Seong
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Tae Jin Kang
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
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Pacureanu L, Avram S, Crisan L. Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors. J Biomol Struct Dyn 2020; 39:2318-2337. [PMID: 32216607 DOI: 10.1080/07391102.2020.1747544] [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] [Indexed: 10/24/2022]
Abstract
Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Sorin Avram
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
| | - Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, Romanian Academy, Timisoara, Romania
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7
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Li Y, Wei X, Wang Q, Li W, Yang T. Inverse screening of Simvastatin kinase targets from glioblastoma druggable kinome. Comput Biol Chem 2020; 86:107243. [PMID: 32172201 DOI: 10.1016/j.compbiolchem.2020.107243] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/04/2020] [Accepted: 03/01/2020] [Indexed: 12/16/2022]
Abstract
The statin drug Simvastatin is a HMG-CoA reductase inhibitor that has been widely used to lower blood lipid. However, the drug is clinically observed to reposition a significant suppressing potency on glioblastoma (GBM) by unexpectedly targeting diverse kinase pathways involved in GBM tumorigensis. Here, an inverse screening strategy is described to discover potential kinase targets of Simvastatin. Various human protein kinases implicated in GBM are enriched to define a druggable kinome; the binding behavior of Simvastatin to the kinome is profiled systematically via an integrative computational approach, from which most kinases have only low or moderate binding potency to Simvastatin, while only few are identified as promising kinase hits. It is revealed that Simvastatin can potentially interact with certain known targets or key regulators of GBM such as ErbB, c-Src and FGFR signaling pathways, but exhibit low affinity to the well-established GBM target of PI3K/Akt/mTOR pathway. Further assays determine that Simvastatin can inhibit kinase hits EGFR, MET, SRC and HER2 at nanomolar level, which are comparable with those of cognate kinase inhibitors. Structural analyses reveal that the sophisticated T790 M gatekeeper mutation can considerably reduce Simvastatin sensitivity to EGFR by inducing the ligand change between different binding modes.
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Affiliation(s)
- Yi Li
- Department of Neurosurgery, Second Affiliated Hospital, Zunyi Medical University, Zunyi 563006, China
| | - Xu Wei
- Department of Neurosurgery, Second Affiliated Hospital, Zunyi Medical University, Zunyi 563006, China
| | - Qiuhong Wang
- Department of Neurosurgery, Second Affiliated Hospital, Zunyi Medical University, Zunyi 563006, China
| | - Wei Li
- Department of Neurosurgery, Second Affiliated Hospital, Zunyi Medical University, Zunyi 563006, China
| | - Tao Yang
- Department of Neurosurgery, Second Affiliated Hospital, Zunyi Medical University, Zunyi 563006, China.
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8
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Adasme MF, Parisi D, Sveshnikova A, Schroeder M. Structure-based drug repositioning: Potential and limits. Semin Cancer Biol 2020; 68:192-198. [PMID: 32032699 DOI: 10.1016/j.semcancer.2020.01.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/08/2020] [Accepted: 01/16/2020] [Indexed: 12/28/2022]
Abstract
Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.
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Affiliation(s)
- Melissa F Adasme
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Daniele Parisi
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany; ESAT-STADIUS, KU Leuven, B-3001 Heverlee, Belgium
| | - Anastasia Sveshnikova
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany.
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9
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Lo YC, Liu T, Morrissey KM, Kakiuchi-Kiyota S, Johnson AR, Broccatelli F, Zhong Y, Joshi A, Altman RB. Computational analysis of kinase inhibitor selectivity using structural knowledge. Bioinformatics 2019; 35:235-242. [PMID: 29985971 DOI: 10.1093/bioinformatics/bty582] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 07/05/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Chen Lo
- Department of Bioengineering, Stanford, CA, USA
| | - Tianyun Liu
- Department of Bioengineering, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
| | - Kari M Morrissey
- Department of Clinical Pharmacology, South San Francisco, CA, USA
| | | | - Adam R Johnson
- Biochemical and Cellular Pharmacology, South San Francisco, CA, USA
| | - Fabio Broccatelli
- Department of Drug Metabolism and Pharmacokinetic, Genentech Inc., South San Francisco, CA, USA
| | - Yu Zhong
- Department of Safety Assessment, South San Francisco, CA, USA
| | - Amita Joshi
- Department of Clinical Pharmacology, South San Francisco, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
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10
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In Silico Investigation of the Anti-Tumor Mechanisms of Epigallocatechin-3-Gallate. Molecules 2019; 24:molecules24071445. [PMID: 30979098 PMCID: PMC6480119 DOI: 10.3390/molecules24071445] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/06/2019] [Accepted: 04/09/2019] [Indexed: 12/15/2022] Open
Abstract
The EGCG, an important component of polyphenol in green tea, is well known due to its numerous health benefits. We employed the reverse docking method for the identification of the putative targets of EGCG in the anti-tumor target protein database and these targets were further uploaded to public databases in order to understand the underlying pharmacological mechanisms and search for novel EGCG-associated targets. Similarly, the pharmacological linkage between tumor-related proteins and EGCG was manually constructed in order to provide greater insight into the molecular mechanisms through a systematic integration with applicable bioinformatics. The results indicated that the anti-tumor mechanisms of EGCG may involve 12 signaling transduction pathways and 33 vital target proteins. Moreover, we also discovered four novel putative target proteins of EGCG, including IKBKB, KRAS, WEE1 and NTRK1, which are significantly related to tumorigenesis. In conclusion, this work may provide a useful perspective that will improve our understanding of the pharmacological mechanism of EGCG and identify novel potential therapeutic targets.
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11
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Kim SS, Aprahamian ML, Lindert S. Improving inverse docking target identification with Z-score selection. Chem Biol Drug Des 2019; 93:1105-1116. [PMID: 30604454 DOI: 10.1111/cbdd.13453] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/22/2018] [Accepted: 11/17/2018] [Indexed: 12/12/2022]
Abstract
The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
| | | | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio
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12
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Hameed R, Khan A, Khan S, Perveen S. Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development. Anticancer Agents Med Chem 2018; 19:592-598. [PMID: 30306880 DOI: 10.2174/1871520618666181009163014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 04/09/2018] [Accepted: 09/03/2018] [Indexed: 01/07/2023]
Abstract
BACKGROUND One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development. METHODS Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets. RESULTS Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer. CONCLUSION Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.
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Affiliation(s)
- Rabia Hameed
- Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
| | - Afsar Khan
- Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
| | - Sehroon Khan
- Key Laboratory of Economic Plants and Biotechnology, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 560201, Yunnan, China
| | - Shagufta Perveen
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
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13
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Meng L, Huang Z. In silico-in vitro discovery of untargeted kinase-inhibitor interactions from kinase-targeted therapies: A case study on the cancer MAPK signaling pathway. Comput Biol Chem 2018; 75:196-204. [PMID: 29803964 DOI: 10.1016/j.compbiolchem.2018.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 05/09/2018] [Accepted: 05/13/2018] [Indexed: 12/14/2022]
Abstract
Protein kinase inhibitors have been widely used as therapeutic agents to treat a variety of diseases, but many of them may cause off-target effects by unexpectedly targeting other noncognate kinases due to high conversion across the protein kinase family. The mitogen-activated protein kinase (MAPK) signaling pathway plays an essential role in tumorigenesis, which has been recognized as a high priority in the druggable target candidates of anticancer therapy. Here, we attempt to investigate the untargeted kinase-inhibitor interactions (UKIIs) of kinase-targeted therapies for the cancer MAPK signaling cascade via an integration of biomolecular modeling, cell viability assay and kinase inhibition analysis. A systematic kinase-inhibitor interaction profile is created for 28 FDA-approved kinase inhibitor drugs across 9 caner-related MAPK kinases. The created profile is analyzed at structural, energetic and dynamic levels and, consequently, totally 18 promising UKII pairs with high theoretical affinity are derived, from which the noncognate inhibitors Cabozantinib, Regorafenib and Crizotinib are selected to test their cytotoxic effects on human epithelial colorectal adenocarcinoma Caco-2 cell line and inhibition activity against the recombinant protein of human p38α kinase domain. The obtained results are compared with two cognate MAPK inhibitors JNK-IN-8 and BIRB796. As might be expected, the Regorafenib, Crizotinib and Cabozantinib exhibit high, moderate and low cytotoxicities, respectively. In addition, the Regorafenib is determined to have a potent p38α-inhibitory activity. This is basically in line with the test results of positive controls JNK-IN-8 and BIRB796 and can be well confirmed by computational modeling.
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Affiliation(s)
- Li Meng
- College of Pharmaceutical Sciences, Jiangsu Vocational College of Medicine, Yancheng 224008, China
| | - Zhijun Huang
- Department of General Surgery, Yancheng First People's Hospital, Yancheng 224005, China.
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14
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Huang H, Zhang G, Zhou Y, Lin C, Chen S, Lin Y, Mai S, Huang Z. Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds. Front Chem 2018; 6:138. [PMID: 29868550 PMCID: PMC5954125 DOI: 10.3389/fchem.2018.00138] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/09/2018] [Indexed: 12/13/2022] Open
Abstract
This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.
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Affiliation(s)
- Hongbin Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Guigui Zhang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Yuquan Zhou
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Chenru Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Suling Chen
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Yutong Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Shangkang Mai
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Zunnan Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
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15
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Xu X, Huang M, Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. BIOPHYSICS REPORTS 2018; 4:1-16. [PMID: 29577065 PMCID: PMC5860130 DOI: 10.1007/s41048-017-0045-8] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 09/08/2017] [Indexed: 01/09/2023] Open
Abstract
Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.
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Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
| | - Marshal Huang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
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16
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Sivanandan S, Pimple S. Molecular Docking Studies of <i>Alpinia galanga</i> Phytoconstituents for Psychostimulant Activity. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/abc.2018.84006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Abstract
Nowadays it is widely accepted that one compound can be able to hit several targets at once. This "magic shotgun" approach for drug development properly describes the mechanism of biomolecular recognition. The need to take into account the polypharmacology in structure-based drug design has led to the development of several computational tools. Here we present a computational protocol to identify promising compounds against several biological targets, a protocol known as inverse docking.
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Affiliation(s)
- Patricia Saenz-Méndez
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden. .,Computational Chemistry and Biology Group, Facultad de Química, UdelaR, Montevideo, Uruguay.
| | - Leif A Eriksson
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
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18
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Malik V, Dhanjal JK, Kumari A, Radhakrishnan N, Singh K, Sundar D. Function and structure-based screening of compounds, peptides and proteins to identify drug candidates. Methods 2017; 131:10-21. [DOI: 10.1016/j.ymeth.2017.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 08/21/2017] [Accepted: 08/21/2017] [Indexed: 01/01/2023] Open
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19
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Tsakiri EN, Gaboriaud-Kolar N, Iliaki KK, Tchoumtchoua J, Papanagnou ED, Chatzigeorgiou S, Tallas KD, Mikros E, Halabalaki M, Skaltsounis AL, Trougakos IP. The Indirubin Derivative 6-Bromoindirubin-3'-Oxime Activates Proteostatic Modules, Reprograms Cellular Bioenergetic Pathways, and Exerts Antiaging Effects. Antioxid Redox Signal 2017; 27:1027-1047. [PMID: 28253732 PMCID: PMC5651956 DOI: 10.1089/ars.2016.6910] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
AIMS Organismal aging can be delayed by mutations that either activate stress responses or reduce the nutrient-sensing pathway signaling; thus, by using Drosophila melanogaster as an in vivo experimental screening platform, we searched for compounds that modulate these pathways. RESULTS We noted that oral administration of the glycogen synthase kinase 3 (Gsk-3) inhibitor 6-bromoindirubin-3'-oxime (6BIO) in Drosophila flies extended healthy life span. 6BIO is not metabolized in fly tissues, modulated bioenergetic pathways, decreased lipid and glucose tissue load, activated antioxidant and proteostatic modules, and enhanced resistance to stressors. Mechanistically, we found that the effects on the stress-responsive pathways were largely dependent on the activity of the transcription factor nuclear factor erythroid 2-related factor (Nrf-2). Genetic inhibition of Gsk-3 largely phenocopied the 6BIO-mediated effects, while high levels of Gsk-3 expression and/or kinase activity suppressed proteostatic modules and reduced flies' longevity; these effects were partially rescued by 6BIO. Also, 6BIO was found to partially reduce the 3-phosphoinositide-dependent protein kinase-1 (Pdpk1) activity, a major effector of the insulin/insulin-like growth factor-1 cell signaling pathways. INNOVATION 6BIO exerts the unique property of increasing stress tolerance and in parallel partially suppressing the nutrient-sensing pathway signaling. CONCLUSION Our findings suggest that the 6BIO scaffold can be used for the development of novel antiaging compounds. Antioxid. Redox Signal. 27, 1027-1047.
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Affiliation(s)
- Eleni N Tsakiri
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
| | - Nicolas Gaboriaud-Kolar
- 2 Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , Athens, Greece
| | - Kalliopi K Iliaki
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
| | - Job Tchoumtchoua
- 2 Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , Athens, Greece
| | - Eleni-Dimitra Papanagnou
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
| | - Sofia Chatzigeorgiou
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
| | - Konstantinos D Tallas
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
| | - Emmanuel Mikros
- 3 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , Athens, Greece
| | - Maria Halabalaki
- 2 Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , Athens, Greece
| | - Alexios-Leandros Skaltsounis
- 2 Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens , Athens, Greece
| | - Ioannis P Trougakos
- 1 Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens , Athens, Greece
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20
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Rare Diseases: Drug Discovery and Informatics Resource. Interdiscip Sci 2017; 10:195-204. [PMID: 29094320 DOI: 10.1007/s12539-017-0270-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 12/13/2022]
Abstract
A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.
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21
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Sklirou AD, Gaboriaud-Kolar N, Papassideri I, Skaltsounis AL, Trougakos IP. 6-bromo-indirubin-3'-oxime (6BIO), a Glycogen synthase kinase-3β inhibitor, activates cytoprotective cellular modules and suppresses cellular senescence-mediated biomolecular damage in human fibroblasts. Sci Rep 2017; 7:11713. [PMID: 28916781 PMCID: PMC5601901 DOI: 10.1038/s41598-017-11662-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 08/25/2017] [Indexed: 02/07/2023] Open
Abstract
As genetic interventions or extended caloric restriction cannot be applied in humans, many studies have been devoted to the identification of natural products that can prolong healthspan. 6-bromoindirubin-3′-oxime (6BIO), a hemi-synthetic derivative of indirubins found in edible mollusks and plants, is a potent inhibitor of Glycogen synthase kinase 3β (Gsk-3β). This pleiotropic kinase has been implicated in various age-related diseases including tumorigenesis, neurodegeneration and diabetes. Accordingly, 6BIO has shown anti-tumor and anti-neurodegenerative activities; nevertheless, the potential role of 6BIO in normal human cells senescence remains largely unknown. We report herein that treatment of human diploid skin fibroblasts with 6BIO reduced the oxidative load, conferred protection against oxidative stress-mediated DNA damage, and it also promoted the activation of antioxidant and proteostatic modules; these effects were largely phenocopied by genetic inhibition of Gsk-3. Furthermore, prolonged treatment of cells with 6BIO, although it decreased the rate of cell cycling, it significantly suppressed cellular senescence-related accumulation of biomolecular damage. Taken together, our presented findings suggest that 6BIO is a novel activator of antioxidant responses and of the proteostasis network in normal human cells; moreover, and given the low levels of biomolecules damage in 6BIO treated senescing cells, this compound likely exerts anti-tumor properties.
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Affiliation(s)
- Aimilia D Sklirou
- Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens, Athens, 15784, Greece
| | - Nicolas Gaboriaud-Kolar
- Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Issidora Papassideri
- Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens, Athens, 15784, Greece
| | - Alexios-Leandros Skaltsounis
- Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Ioannis P Trougakos
- Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens, Athens, 15784, Greece.
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22
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Saenz-Méndez P, Eriksson M, Eriksson LA. Ligand Selectivity between the ADP-Ribosylating Toxins: An Inverse-Docking Study for Multitarget Drug Discovery. ACS OMEGA 2017; 2:1710-1719. [PMID: 30023642 PMCID: PMC6044789 DOI: 10.1021/acsomega.7b00010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 03/17/2017] [Indexed: 06/02/2023]
Abstract
Bacterial adenosine 5'-diphosphate-ribosylating toxins are encoded by several human pathogens, such as Pseudomonas aeruginosa (exotoxin A (ETA)), Corynebacterium diphtheriae (diphtheria toxin (DT)), and Vibrio cholerae (cholix toxin (CT)). The toxins modify eukaryotic elongation factor 2, an essential human enzyme in protein synthesis, thereby causing cell death. Targeting external virulence factors, such as the above toxins, is a promising alternative for developing new antibiotics, while at the same time avoiding drug resistance. This study aims to establish a reliable computational methodology to find a "silver bullet" able to target all three toxins. Herein, we have undertaken a detailed analysis of the active sites of ETA, DT, and CT, followed by the determination of the most appropriate selection of the size of the docking sphere. Thereafter, we tested two different approaches for normalizing the docking scores and used these to verify the best target (toxin) for each ligand. The results indicate that the methodology is suitable for identifying selective as well as multitoxin inhibitors, further validating the robustness of inverse docking for target-fishing experiments.
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Affiliation(s)
- Patricia Saenz-Méndez
- Department
of Chemistry and Molecular Biology, University
of Gothenburg, 405 30 Göteborg, Sweden
- Computational
Chemistry and Biology Group, Facultad de Química, Universidad de la República, 11800 Montevideo, Uruguay
| | - Martin Eriksson
- Department
of Chemistry and Molecular Biology, University
of Gothenburg, 405 30 Göteborg, Sweden
| | - Leif A. Eriksson
- Department
of Chemistry and Molecular Biology, University
of Gothenburg, 405 30 Göteborg, Sweden
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23
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Park K, Cho AE. Using reverse docking to identify potential targets for ginsenosides. J Ginseng Res 2016; 41:534-539. [PMID: 29021701 PMCID: PMC5628352 DOI: 10.1016/j.jgr.2016.10.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 08/30/2016] [Accepted: 10/25/2016] [Indexed: 11/25/2022] Open
Abstract
Background Ginsenosides are the main ingredients of ginseng, which, in traditional Eastern medicine, has been claimed to have therapeutic values for many diseases. In order to verify the effects of ginseng that have been empirically observed, we utilized the reverse docking method to screen for target proteins that are linked to specific diseases. Methods We constructed a target protein database including 1,078 proteins associated with various kinds of diseases, based on the Potential Drug Target Database, with an added list of kinase proteins. We screened 26 kinds of ginsenosides of this target protein database using docking. Results We found four potential target proteins for ginsenosides, based on docking scores. Implications of these “hit” targets are discussed. From this screening, we also found four targets linked to possible side effects and toxicities, based on docking scores. Conclusion Our method and results can be helpful for finding new targets and developing new drugs from natural products.
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Affiliation(s)
- Kichul Park
- Department of Bioinformatics, Korea University, Sejong, Republic of Korea
| | - Art E Cho
- Department of Bioinformatics, Korea University, Sejong, Republic of Korea
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24
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Liu Z, Fang H, Slikker W, Tong W. Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases. Trends Pharmacol Sci 2016; 37:843-857. [DOI: 10.1016/j.tips.2016.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 06/27/2016] [Accepted: 06/30/2016] [Indexed: 12/23/2022]
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25
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Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 2016; 11:707-15. [PMID: 27186904 DOI: 10.1080/17460441.2016.1190706] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. AREAS COVERED In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. EXPERT OPINION There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.
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Affiliation(s)
- Aeri Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Kyoungyeul Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Dongsup Kim
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
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26
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Wu X, Chen X, Dan J, Cao Y, Gao S, Guo Z, Zerbe P, Chai Y, Diao Y, Zhang L. Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification. Sci Rep 2016; 6:25491. [PMID: 27150638 PMCID: PMC4858665 DOI: 10.1038/srep25491] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 04/18/2016] [Indexed: 12/30/2022] Open
Abstract
Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines.
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Affiliation(s)
- Xunxun Wu
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China.,School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Xiaofei Chen
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Jia Dan
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Yan Cao
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Shouhong Gao
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Zhiying Guo
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China.,School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Philipp Zerbe
- Department of Plant Biology, University of California, Davis, CA 95616, USA
| | - Yifeng Chai
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
| | - Yong Diao
- School of Biomedical Science, Institute of Molecular Medicine, Huaqiao University, Quanzhou 362021, PR China
| | - Lei Zhang
- School of Pharmacy, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200433, PR China
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27
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Jang D, Lee S, Lee J, Kim K, Lee D. Inferring new drug indications using the complementarity between clinical disease signatures and drug effects. J Biomed Inform 2015; 59:248-57. [PMID: 26707452 DOI: 10.1016/j.jbi.2015.12.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Revised: 10/31/2015] [Accepted: 12/09/2015] [Indexed: 11/17/2022]
Abstract
BACKGROUND Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems. METHODS Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug's effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease-drug pair by the calculation of complementarity (anti-correlation) and association between clinical states ("up" or "down") of disease signatures and clinical effects ("up", "down" or "association") of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study. RESULTS The statistical significance of our prediction results is supported through two benchmark datasets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease-drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P<0.009 in Comparative Toxicogenomics Database and Clinical Trials) and presented evidences for those with already published literature. CONCLUSION The results show that electronic clinical information is a feasible data resource and utilizing the complementarity (anti-correlated relationships) between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research. It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid.
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Affiliation(s)
- Dongjin Jang
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea; Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
| | - Sejoon Lee
- Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
| | - Jaehyun Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea; Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
| | - Kiseong Kim
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea; Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea; Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
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28
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Drug Signature-based Finding of Additional Clinical Use of LC28-0126 for Neutrophilic Bronchial Asthma. Sci Rep 2015; 5:17784. [PMID: 26626943 PMCID: PMC4667219 DOI: 10.1038/srep17784] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 11/03/2015] [Indexed: 12/20/2022] Open
Abstract
In recent decades, global pharmaceutical companies have suffered from an R&D innovation gap between the increased cost of a new drug’s development and the decreased number of approvals. Drug repositioning offers another opportunity to fill the gap because the approved drugs have a known safety profile for human use, allowing for a reduction of the overall cost of drug development by eliminating rigorous safety assessment. In this study, we compared the transcriptional profile of LC28-0126, an investigational drug for acute myocardial infarction (MI) at clinical trial, obtained from healthy male subjects with molecular activity profiles in the Connectivity Map. We identified dyphilline, an FDA-approved drug for bronchial asthma, as a top ranked connection with LC28-0126. Subsequently, we demonstrated that LC28-0126 effectively ameliorates the pathophysiology of neutrophilic bronchial asthma in OVALPS-OVA mice accompanied with a reduction of inflammatory cell counts in the bronchoalveolar lavage fluid (BALF), inhibition of the release of proinflammatory cytokines, relief of airway hyperactivity, and improvement of histopathological changes in the lung. Taken together, we suggest that LC28-0126 could be a potential therapeutic for bronchial asthma. In addition, this study demonstrated the potential general utility of computational drug repositioning using clinical profiles of the investigational drug.
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Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery. Biotechnol Adv 2015; 33:941-7. [PMID: 26025037 DOI: 10.1016/j.biotechadv.2015.05.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Revised: 05/19/2015] [Accepted: 05/23/2015] [Indexed: 12/21/2022]
Abstract
Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.
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A miRNA-driven inference model to construct potential drug-disease associations for drug repositioning. BIOMED RESEARCH INTERNATIONAL 2015; 2015:406463. [PMID: 25789319 PMCID: PMC4350970 DOI: 10.1155/2015/406463] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 01/13/2015] [Accepted: 01/29/2015] [Indexed: 11/18/2022]
Abstract
Increasing evidence discovered that the inappropriate expression of microRNAs (miRNAs) will lead to many kinds of complex diseases and drugs can regulate the expression level of miRNAs. Therefore human diseases may be treated by targeting some specific miRNAs with drugs, which provides a new perspective for drug repositioning. However, few studies have attempted to computationally predict associations between drugs and diseases via miRNAs for drug repositioning. In this paper, we developed an inference model to achieve this aim by combining experimentally supported drug-miRNA associations and miRNA-disease associations with the assumption that drugs will form associations with diseases when they share some significant miRNA partners. Experimental results showed excellent performance of our model. Case studies demonstrated that some of the strongly predicted drug-disease associations can be confirmed by the publicly accessible database CTD (www.ctdbase.org), which indicated the usefulness of our inference model. Moreover, candidate miRNAs as molecular hypotheses underpinning the associations were listed to guide future experiments. The predicted results were released for further studies. We expect that this study will provide help in our understanding of drug-disease association prediction and in the roles of miRNAs in drug repositioning.
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Issa NT, Peters OJ, Byers SW, Dakshanamurthy S. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions. Comb Chem High Throughput Screen 2015; 18:784-94. [PMID: 26234515 PMCID: PMC5848469 DOI: 10.2174/1386207318666150803130138] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 06/17/2015] [Accepted: 07/28/2015] [Indexed: 11/22/2022]
Abstract
We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing.
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Abstract
Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.
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Westermaier Y, Barril X, Scapozza L. Virtual screening: an in silico tool for interlacing the chemical universe with the proteome. Methods 2014; 71:44-57. [PMID: 25193260 DOI: 10.1016/j.ymeth.2014.08.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Revised: 07/16/2014] [Accepted: 08/02/2014] [Indexed: 12/28/2022] Open
Abstract
In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.
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Affiliation(s)
- Yvonne Westermaier
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211 Geneva 4, Switzerland; Computational Biology & Drug Design Group, Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain.
| | - Xavier Barril
- Computational Biology & Drug Design Group, Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
| | - Leonardo Scapozza
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211 Geneva 4, Switzerland.
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Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 827:227-57. [PMID: 25387968 PMCID: PMC7120483 DOI: 10.1007/978-94-017-9245-5_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.
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D'Abramo M, Besker N, Chillemi G, Grottesi A. Modeling conformational transitions in kinases by molecular dynamics simulations: achievements, difficulties, and open challenges. Front Genet 2014; 5:128. [PMID: 24860596 PMCID: PMC4026744 DOI: 10.3389/fgene.2014.00128] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2014] [Accepted: 04/22/2014] [Indexed: 12/26/2022] Open
Abstract
Protein kinases work because their flexibility allows to continuously switch from inactive to active form. Despite the large number of structures experimentally determined in such states, the mechanism of their conformational transitions as well as the transition pathways are not easily to capture. In this regard, computational methods can help to shed light on such an issue. However, due to the intrinsic sampling limitations, much efforts have been done to model in a realistic way the conformational changes occurring in protein kinases. In this review we will address the principal biological achievements and structural aspects in studying kinases conformational transitions and will focus on the main challenges related to computational approaches such as molecular modeling and MD simulations.
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Affiliation(s)
- Marco D'Abramo
- CINECA Rome, Italy ; Dipartimento di Chimica, Sapienza University of Rome Rome, Italy
| | - Neva Besker
- Dipartimento di Scienze e Tecnologie Chimiche, Università di Roma "Tor Vergata," Rome, Italy
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37
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Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
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Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
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Abstract
Docking is the computational method of choice to quickly predict how a low molecular-weight ligand binds to its macromolecular target. Despite persistent problems in predicting binding free energies, docking has undergone significant advances in numerous topics (throughput, target flexibility). The ever increasing availability of high-resolution X-ray structures and the development of more reliable comparative models for proteins of pharmacological interest paved the way to apply protein–ligand docking to multiple targets to predict main and off-targets for bioactive compounds and even to repurpose existing drugs. Applying docking to multiple targets brings an additional level of complexity in scoring numerous and heterogeneous docking poses. Despite undeniable successes, proteomewide docking should, however, be considered with caution with regard to recall and precision of the predictions.
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Braig S, Kressirer CA, Liebl J, Bischoff F, Zahler S, Meijer L, Vollmar AM. Indirubin Derivative 6BIO Suppresses Metastasis. Cancer Res 2013; 73:6004-12. [DOI: 10.1158/0008-5472.can-12-4358] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2013; 2013:806072. [PMID: 23818932 PMCID: PMC3684027 DOI: 10.1155/2013/806072] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2013] [Revised: 05/06/2013] [Accepted: 05/07/2013] [Indexed: 12/22/2022]
Abstract
The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.
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41
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Erić S, Ke S, Barata T, Solmajer T, Antić Stanković J, Juranić Z, Savić V, Zloh M. Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity. Bioorg Med Chem 2012; 20:5220-8. [DOI: 10.1016/j.bmc.2012.06.051] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Revised: 06/24/2012] [Accepted: 06/29/2012] [Indexed: 12/17/2022]
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Santiago DN, Pevzner Y, Durand AA, Tran M, Scheerer RR, Daniel K, Sung SS, Woodcock HL, Guida WC, Brooks WH. Virtual target screening: validation using kinase inhibitors. J Chem Inf Model 2012; 52:2192-203. [PMID: 22747098 PMCID: PMC3488111 DOI: 10.1021/ci300073m] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.
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Affiliation(s)
- Daniel N. Santiago
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
| | - Yuri Pevzner
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
| | - Ashley A. Durand
- HTS & Chemistry Core, H. Lee Moffitt Cancer Institute & Research Institute, 12902 Magnolia Drive, Drug Discovery-SRB3, Tampa, Florida 33612
| | - MinhPhuong Tran
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
| | - Rachel R. Scheerer
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
| | - Kenyon Daniel
- HTS & Chemistry Core, H. Lee Moffitt Cancer Institute & Research Institute, 12902 Magnolia Drive, Drug Discovery-SRB3, Tampa, Florida 33612
| | - Shen-Shu Sung
- Department of Pharmacology, Milton S. Hershey Medical Cancer Institute, Pennsylvania State University, 500 University Drive, MC H072, Hershey, Pennsylvania 17033
| | - H. Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
- Center for Molecular Diversity in Drug Design, Discovery and Delivery, University of South Florida, 4202 East Fowler Avenue, CHE 205, Tampa, Florida 33620
| | - Wayne C. Guida
- HTS & Chemistry Core, H. Lee Moffitt Cancer Institute & Research Institute, 12902 Magnolia Drive, Drug Discovery-SRB3, Tampa, Florida 33612
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
- Center for Molecular Diversity in Drug Design, Discovery and Delivery, University of South Florida, 4202 East Fowler Avenue, CHE 205, Tampa, Florida 33620
| | - Wesley H. Brooks
- HTS & Chemistry Core, H. Lee Moffitt Cancer Institute & Research Institute, 12902 Magnolia Drive, Drug Discovery-SRB3, Tampa, Florida 33612
- Department of Chemistry, University of South Florida, Tampa, Florida 33620
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Lauro G, Masullo M, Piacente S, Riccio R, Bifulco G. Inverse Virtual Screening allows the discovery of the biological activity of natural compounds. Bioorg Med Chem 2012; 20:3596-602. [DOI: 10.1016/j.bmc.2012.03.072] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 03/26/2012] [Accepted: 03/30/2012] [Indexed: 12/17/2022]
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Wang JC, Chu PY, Chen CM, Lin JH. idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res 2012; 40:W393-9. [PMID: 22649057 PMCID: PMC3394295 DOI: 10.1093/nar/gks496] [Citation(s) in RCA: 126] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw.
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Affiliation(s)
- Jui-Chih Wang
- Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
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45
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Wang W, Zhou X, He W, Fan Y, Chen Y, Chen X. The interprotein scoring noises in glide docking scores. Proteins 2011; 80:169-83. [DOI: 10.1002/prot.23173] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Revised: 08/17/2011] [Accepted: 08/29/2011] [Indexed: 11/08/2022]
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46
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Lauro G, Romano A, Riccio R, Bifulco G. Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds. JOURNAL OF NATURAL PRODUCTS 2011; 74:1401-7. [PMID: 21542600 DOI: 10.1021/np100935s] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
An inverse virtual screening in silico approach has been applied to natural bioactive molecules to screen their efficacy against proteins involved in cancer processes, with the aim of directing future experimental assays. Docking studies were performed on a panel of 126 protein targets extracted from the Protein Data Bank, to analyze their possible interactions with a small library of 43 bioactive compounds. Analysis of the molecular docking results was performed through the use of tables containing energy data organized in a matrix. The application of this approach may facilitate the prediction of the activity of unknown ligands for known targets involved in the development of cancer and could be applied to other models based on different libraries of ligands and different panels of targets.
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Affiliation(s)
- Gianluigi Lauro
- Dipartimento di Scienze Farmaceutiche e Biomediche, Università di Salerno, Via Ponte Don Melillo, 84084 Fisciano (SA), Italy
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47
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Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011; 12:303-11. [PMID: 21690101 DOI: 10.1093/bib/bbr013] [Citation(s) in RCA: 313] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.
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48
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Martin L, Page G, Terro F. Tau phosphorylation and neuronal apoptosis induced by the blockade of PP2A preferentially involve GSK3β. Neurochem Int 2011; 59:235-50. [PMID: 21672577 DOI: 10.1016/j.neuint.2011.05.010] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2011] [Revised: 05/24/2011] [Accepted: 05/26/2011] [Indexed: 02/08/2023]
Abstract
Overactivation of GSK3β (glycogen synthase kinase-3β) and downregulation of PP2A (protein phosphatase-2A) have been proposed to be involved in the abnormal tau phosphorylation and aggregation in Alzheimer's disease (AD). GSK3β and PP2A signaling pathways were reported to be interconnected. Targeting tau kinases was suggested to represent a therapeutic strategy for AD. Here, tau phosphorylation and neuronal apoptosis were induced in cortical cultured neurons by the inhibition of PP2A by okadaic acid (OKA). In this in vitro model of 'tau pathology' and neurodegeneration, we tested whether GSK3β and other tau kinases including DYRK1A and CDK5 were implicated. Our results show that the inhibitors of GSK3β, lithium and 6-BIO (6-bromoindirubin-3'-oxime), prevented OKA-induced tau phosphorylation and neuronal apoptosis. The implication of GSK3β in these OKA-induced effects was confirmed by its silencing by hairpin siRNA. By contrast, inhibition of DYRK1A (dual-specificity tyrosine-phosphorylation regulated kinase-1A) and CDK5 (cyclin-dependent kinase-5) reversed OKA-induced tau phosphorylation at certain sites but failed to prevent neuronal apoptosis. These results indicate that OKA-induced effects, especially neuronal apoptosis, are preferentially mediated by GSK3β. Furthermore, since chronic exposure to lithium and 6-BIO might be deleterious for neurons, we tested the effect of a new 6-BIO derivative, 6-BIBEO (6-bromoindirubin-3'-(2-bromoethyl)-oxime), which is much less cytotoxic and more selectively inhibits GSK3β compared to lithium and 6-BIO. We show that 6-BIBEO efficiently reversed OKA-induced tau phosphorylation and neuronal apoptosis. It will be interesting to test neuroprotection by 6-BIBEO in an in vivo model of tau pathology and neurodegeneration.
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Affiliation(s)
- Ludovic Martin
- Groupe de Neurobiologie Cellulaire-EA3842, Homéostasie cellulaire et pathologies, Faculté de Médecine, 2 rue du Dr Raymond Marcland, 87025 Limoges Cedex, France
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Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011; 74:2554-74. [PMID: 21621023 DOI: 10.1016/j.jprot.2011.05.011] [Citation(s) in RCA: 186] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 04/10/2011] [Accepted: 05/06/2011] [Indexed: 01/31/2023]
Abstract
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
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Affiliation(s)
- Alexios Koutsoukas
- Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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Kinnings SL, Jackson RM. ReverseScreen3D: a structure-based ligand matching method to identify protein targets. J Chem Inf Model 2011; 51:624-34. [PMID: 21361385 DOI: 10.1021/ci1003174] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Ligand promiscuity, which is now recognized as an extremely common phenomenon, is a major underlying cause of drug toxicity. We have developed a new reverse virtual screening (VS) method called ReverseScreen3D, which can be used to predict the potential protein targets of a query compound of interest. The method uses a 2D fingerprint-based method to select a ligand template from each unique binding site of each protein within a target database. The target database contains only the structurally determined bioactive conformations of known ligands. The 2D comparison is followed by a 3D structural comparison to the selected query ligand using a geometric matching method, in order to prioritize each target binding site in the database. We have evaluated the performance of the ReverseScreen2D and 3D methods using a diverse set of small molecule protein inhibitors known to have multiple targets, and have shown that they are able to provide a highly significant enrichment of true targets in the database. Furthermore, we have shown that the 3D structural comparison improves early enrichment when compared with the 2D method alone, and that the 3D method performs well even in the absence of 2D similarity to the template ligands. By carrying out further experimental screening on the prioritized list of targets, it may be possible to determine the potential targets of a new compound or determine the off-targets of an existing drug. The ReverseScreen3D method has been incorporated into a Web server, which is freely available at http://www.modelling.leeds.ac.uk/ReverseScreen3D .
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Affiliation(s)
- Sarah L Kinnings
- Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
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