1
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Zhang Y, Chowdhury S, Rodrigues JV, Shakhnovich E. Development of antibacterial compounds that constrain evolutionary pathways to resistance. eLife 2021; 10:64518. [PMID: 34279221 PMCID: PMC8331180 DOI: 10.7554/elife.64518] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 07/13/2021] [Indexed: 01/27/2023] Open
Abstract
Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.
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Affiliation(s)
- Yanmin Zhang
- School of Science, China Pharmaceutical University, Nanjing, China.,Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
| | - Sourav Chowdhury
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
| | - João V Rodrigues
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
| | - Eugene Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, United States
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2
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Yang Y, Zhang Y, Hua Y, Chen X, Fan Y, Wang Y, Liang L, Deng C, Lu T, Chen Y, Liu H. In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality. J Chem Inf Model 2020; 60:92-107. [PMID: 31886658 DOI: 10.1021/acs.jcim.9b00841] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.
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Affiliation(s)
- Yan Yang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Xingye Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yuanrong Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Yuchen Wang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China.,State Key Laboratory of Natural Medicines , China Pharmaceutical University , 24 Tongjiaxiang , Nanjing 210009 , China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science , China Pharmaceutical University , 639 Longmian Avenue , Nanjing 211198 , China
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3
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 413] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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4
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Zhang Y, Wang Y, Zhou W, Fan Y, Zhao J, Zhu L, Lu S, Lu T, Chen Y, Liu H. A combined drug discovery strategy based on machine learning and molecular docking. Chem Biol Drug Des 2019; 93:685-699. [PMID: 30688405 DOI: 10.1111/cbdd.13494] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 01/04/2019] [Accepted: 01/19/2019] [Indexed: 12/14/2022]
Abstract
Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross-validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure-based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.
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Affiliation(s)
- Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuchen Wang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weineng Zhou
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanrong Fan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Junnan Zhao
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Lu Zhu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Shuai Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
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5
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Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs. Molecules 2018; 23:molecules23092303. [PMID: 30201875 PMCID: PMC6225236 DOI: 10.3390/molecules23092303] [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] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 09/06/2018] [Accepted: 09/07/2018] [Indexed: 12/12/2022] Open
Abstract
Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.
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6
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Kang D, Pang X, Lian W, Xu L, Wang J, Jia H, Zhang B, Liu AL, Du GH. Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches. RSC Adv 2018; 8:5286-5297. [PMID: 35542432 PMCID: PMC9078101 DOI: 10.1039/c7ra12259d] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 12/27/2017] [Indexed: 02/06/2023] Open
Abstract
The high morbidity and mortality of cancer make it one of the leading causes of global death, thus it is an urgent need to develop effective drugs for cancer therapy.
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Affiliation(s)
- De Kang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Xiaocong Pang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Wenwen Lian
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Lvjie Xu
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Jinhua Wang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Hao Jia
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Baoyue Zhang
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Ai-Lin Liu
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
| | - Guan-Hua Du
- Institute of Materia Medica
- Chinese Academy of Medical Sciences
- Peking Union Medical College
- Beijing 100050
- China
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7
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Zhang Y, Chen Y, Zhang D, Wang L, Lu T, Jiao Y. Discovery of Novel Potent VEGFR-2 Inhibitors Exerting Significant Antiproliferative Activity against Cancer Cell Lines. J Med Chem 2017; 61:140-157. [DOI: 10.1021/acs.jmedchem.7b01091] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Yanmin Zhang
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Danfeng Zhang
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Lu Wang
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
- State
Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yu Jiao
- School
of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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8
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Mello JDFRE, Gomes RA, Vital-Fujii DG, Ferreira GM, Trossini GHG. Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases. Chem Biol Drug Des 2017; 90:1067-1078. [PMID: 28547936 DOI: 10.1111/cbdd.13030] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 04/12/2017] [Accepted: 05/08/2017] [Indexed: 12/24/2022]
Abstract
Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.
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Affiliation(s)
- Juliana da Fonseca Rezende E Mello
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Renan Augusto Gomes
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Drielli Gomes Vital-Fujii
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Glaucio Monteiro Ferreira
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil.,Programa de Pós-graduação em Toxicologia e Análises Toxicológicas, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | - Gustavo Henrique Goulart Trossini
- Litec, Laboratório de Integração Entre Técnicas Computacionais e Experimentais no Planejamento de Fármacos, Departamento de Farmácia, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil.,Programa de Pós-graduação em Toxicologia e Análises Toxicológicas, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
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9
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Zhang Y, Wang L, Zhang Q, Zhu G, Zhang Z, Zhou X, Chen Y, Lu T, Tang W. Potent Pan-Raf and Receptor Tyrosine Kinase Inhibitors Based on a Cyclopropyl Formamide Fragment Overcome Resistance. J Chem Inf Model 2017; 57:1439-1452. [DOI: 10.1021/acs.jcim.6b00795] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Yanmin Zhang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Lu Wang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Qing Zhang
- School
of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Gaoyuan Zhu
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Zhimin Zhang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Xiang Zhou
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Yadong Chen
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
| | - Tao Lu
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
- State
Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, Jiangsu 210009, China
| | - Weifang Tang
- School
of Basic Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, Jiangsu 211198, China
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10
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Design, synthesis and evaluation of derivatives based on pyrimidine scaffold as potent Pan-Raf inhibitors to overcome resistance. Eur J Med Chem 2017; 130:86-106. [DOI: 10.1016/j.ejmech.2017.02.041] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 02/14/2017] [Accepted: 02/16/2017] [Indexed: 12/19/2022]
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11
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Wang L, Zhu G, Zhang Q, Duan C, Zhang Y, Zhang Z, Zhou Y, Lu T, Tang W. Rational design, synthesis, and biological evaluation of Pan-Raf inhibitors to overcome resistance. Org Biomol Chem 2017; 15:3455-3465. [DOI: 10.1039/c7ob00518k] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We describe the design and characterization of a series of pyrimidine scaffolds as Pan-Raf inhibitors, which may overcome the resistance associated with current BRafV600E inhibitors.
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Affiliation(s)
- Lu Wang
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Gaoyuan Zhu
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Qing Zhang
- School of Basic Medicine and Clinical Pharmacy
- China Pharmaceutical University
- Nanjing
- China
| | - Chunqi Duan
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Yanmin Zhang
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Zhimin Zhang
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Yujun Zhou
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
| | - Tao Lu
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
- State Key Laboratory of Natural Medicines
| | - Weifang Tang
- School of Basic Science
- China Pharmaceutical University
- Nanjing
- China
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12
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Zhang Y, Zhang D, Tian H, Jiao Y, Shi Z, Ran T, Liu H, Lu S, Xu A, Qiao X, Pan J, Yin L, Zhou W, Lu T, Chen Y. Identification of Covalent Binding Sites Targeting Cysteines Based on Computational Approaches. Mol Pharm 2016; 13:3106-18. [PMID: 27483186 DOI: 10.1021/acs.molpharmaceut.6b00302] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.
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Affiliation(s)
- Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Danfeng Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Haozhong Tian
- State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Yu Jiao
- State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Zhihao Shi
- State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Ting Ran
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Shuai Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Anyang Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Xin Qiao
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Jing Pan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Lingfeng Yin
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Weineng Zhou
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China.,State Key Laboratory of Natural Medicines, China Pharmaceutical University , 24 Tongjiaxiang, Nanjing 210009, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , 639 Longmian Avenue, Nanjing 211198, China
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