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Ma XL, Chen JZ, Lu X, Zhe YT, Jiang ZB. HPLC coupled with quadrupole time of flight tandem mass spectrometry for analysis of glycosylated components from the fresh flowers of two congeneric species: Robinia hispida L. and Robinia pseudoacacia L. J Sep Sci 2021; 44:1537-1551. [PMID: 33386775 DOI: 10.1002/jssc.202001068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 11/07/2022]
Abstract
Developing methods for the systematic and rapid identification of the chemical compositions of fresh plant tissues has long attracted the attention of phytochemists and pharmacologists. In the present study, based on highly efficient sample pretreatment and high-throughput analysis of high-performance liquid chromatography coupled with quadrupole time of flight tandem mass spectrometry data using molecular networks, a method was developed for systematically analyzing the chemical constituents of the fresh flowers of Robinia hispida L. and Robina pseudoacacia L., two congeneric ornamental species that lack prior consideration. A total of 44 glycosylated structures were characterized. And on the basis of establishing of the fragmentation pathways of 11 known flavonoid glycosides, together with the molecular networking analysis, 18 other ions of flavonoid glycosides in five classes were clustered. Moreover, 15 soyasaponins/triterpenoid glycosides were tentatively identified by comparison of their tandem mass spectrometry characteristic ions with those reported in the literature or the online Global Natural Product Social Molecular Networking database. The water extracts were separated by flash chromatography, which resulted in the discovery of one new compound, named rohispidascopolin, along with five known entities. The pharmacological targets were predicted by SwissTargetPrediction.
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Affiliation(s)
- Xiao-Li Ma
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan, P. R. China.,Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission, Yinchuan, P. R. China
| | - Jing-Zhi Chen
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan, P. R. China
| | - Xing Lu
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan, P. R. China
| | - Ya-Ting Zhe
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan, P. R. China
| | - Zhi-Bo Jiang
- Department of Pharmaceutical Engineering, School of Chemistry and Chemical Engineering, North Minzu University, Yinchuan, P. R. China.,Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission, Yinchuan, P. R. China
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Playe B, Stoven V. Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity. J Cheminform 2020; 12:11. [PMID: 33431042 PMCID: PMC7011501 DOI: 10.1186/s13321-020-0413-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/27/2020] [Indexed: 01/09/2023] Open
Abstract
Chemogenomics, also called proteochemometrics, covers a range of computational methods that can be used to predict protein–ligand interactions at large scales in the protein and chemical spaces. They differ from more classical ligand-based methods (also called QSAR) that predict ligands for a given protein receptor. In the context of drug discovery process, chemogenomics allows to tackle the question of predicting off-target proteins for drug candidates, one of the main causes of undesirable side-effects and failure within drugs development processes. The present study compares shallow and deep machine-learning approaches for chemogenomics, and explores data augmentation techniques for deep learning algorithms in chemogenomics. Shallow machine-learning algorithms rely on expert-based chemical and protein descriptors, while recent developments in deep learning algorithms enable to learn abstract numerical representations of molecular graphs and protein sequences, in order to optimise the performance of the prediction task. We first propose a formulation of chemogenomics with deep learning, called the chemogenomic neural network (CN), as a feed-forward neural network taking as input the combination of molecule and protein representations learnt by molecular graph and protein sequence encoders. We show that, on large datasets, the deep learning CN model outperforms state-of-the-art shallow methods, and competes with deep methods with expert-based descriptors. However, on small datasets, shallow methods present better prediction performance than deep learning methods. Then, we evaluate data augmentation techniques, namely multi-view and transfer learning, to improve the prediction performance of the chemogenomic neural network. We conclude that a promising research direction is to integrate heterogeneous sources of data such as auxiliary tasks for which large datasets are available, or independently, multiple molecule and protein attribute views.
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Affiliation(s)
- Benoit Playe
- Center for Computational Biology, Mines ParisTech, PSL Research University, 60 Bd Saint-Michel, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75248, Paris, France
| | - Veronique Stoven
- Center for Computational Biology, Mines ParisTech, PSL Research University, 60 Bd Saint-Michel, 75006, Paris, France. .,Institut Curie, 75248, Paris, France. .,INSERM U900, 75248, Paris, France.
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Safikhani Z, Smirnov P, Thu KL, Silvester J, El-Hachem N, Quevedo R, Lupien M, Mak TW, Cescon D, Haibe-Kains B. Gene isoforms as expression-based biomarkers predictive of drug response in vitro. Nat Commun 2017; 8:1126. [PMID: 29066719 PMCID: PMC5655668 DOI: 10.1038/s41467-017-01153-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 08/23/2017] [Indexed: 01/09/2023] Open
Abstract
Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.
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Affiliation(s)
- Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Kelsie L Thu
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Jennifer Silvester
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Nehme El-Hachem
- Institut de Recherches Cliniques de Montréal, 110 Pine Avenue West, Montreal, QC, Canada, H2W 1R7
| | - Rene Quevedo
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Mathieu Lupien
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
| | - Tak W Mak
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
| | - David Cescon
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7
- Campbell Family Institute for Breast Cancer Research, 620 University Avenue, Toronto, ON, Canada, M5G2C1
- Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, 27 King's College Circle, Toronto, ON, Canada, M5S 1A1
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, Canada, M5G1L7.
- Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, ON, Canada, M5S 3G4.
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, Canada, M5G 0A3.
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