Monitoring drug-target interactions through target engagement-mediated amplification on arrays and in situ

被引:1
|
作者
Al-Amin, Rasel A. [1 ]
Johansson, Lars [2 ]
Abdurakhmanov, Eldar [3 ]
Landegren, Nils [4 ,5 ]
Lof, Liza [1 ]
Arngarden, Linda [5 ]
Blokzijl, Andries [1 ]
Svensson, Richard [6 ]
Hammond, Maria [1 ]
Lonn, Peter [1 ]
Haybaeck, Johannes [7 ,8 ]
Kamali-Moghaddam, Masood [1 ]
Jensen, Annika Jenmalm [2 ]
Danielson, U. Helena [3 ]
Artursson, Per [6 ]
Lundback, Thomas [2 ]
Landegren, Ulf [1 ]
机构
[1] Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden
[2] Karolinska Inst, Dept Med Biochem & Biophys, Sci Life Lab, Chem Biol Consortium Sweden CBCS, Solna, Sweden
[3] Uppsala Univ, Dept Chem BMC, Sci Life Lab, Uppsala, Sweden
[4] Karolinska Inst, Ctr Mol Med, Dept Med Solna, Sci Life Lab, Solna, Sweden
[5] Uppsala Univ, Dept Med Sci, Uppsala, Sweden
[6] Uppsala Univ, Uppsala Univ Drug Optimizat & Pharmaceut Profilin, Dept Pharm, Sci Life Lab, Uppsala, Sweden
[7] Med Univ Innsbruck, Inst Pathol Neuropathol & Mol Pathol, Innsbruck, Austria
[8] Med Univ Graz, Diagnost & Res Inst Pathol, Graz, Austria
基金
欧洲研究理事会; 瑞典研究理事会;
关键词
KINASE INHIBITORS; CELLS; IDENTIFICATION; GEFITINIB; CHEMISTRY; ATTRITION; BINDING; DESIGN; VITRO; ASSAY;
D O I
10.1093/nar/gkac842
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Drugs are designed to bind their target proteins in physiologically relevant tissues and organs to modulate biological functions and elicit desirable clinical outcomes. Information about target engagement at cellular and subcellular resolution is therefore critical for guiding compound optimization in drug discovery, and for probing resistance mechanisms to targeted therapies in clinical samples. We describe a target engagement-mediated amplification (TEMA) technology, where oligonucleotide-conjugated drugs are used to visualize and measure target engagement in situ, amplified via rolling-circle replication of circularized oligonucleotide probes. We illustrate the TEMA technique using dasatinib and gefitinib, two kinase inhibitors with distinct selectivity profiles. In vitro binding by the dasatinib probe to arrays of displayed proteins accurately reproduced known selectivity profiles, while their differential binding to fixed adherent cells agreed with expectations from expression profiles of the cells. We also introduce a proximity ligation variant of TEMA to selectively investigate binding to specific target proteins of interest. This form of the assay serves to improve resolution of binding to on- and off-target proteins. In conclusion, TEMA has the potential to aid in drug development and clinical routine by conferring valuable insights in drug-target interactions at spatial resolution in protein arrays, cells and in tissues.
引用
收藏
页码:E129 / E129
页数:15
相关论文
共 50 条
  • [41] Identifying drug-target interactions based on graph convolutional network and deep neural network
    Zhao, Tianyi
    Hu, Yang
    Valsdottir, Linda R.
    Zang, Tianyi
    Peng, Jiajie
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (02) : 2141 - 2150
  • [42] CLIP-170S is a microtubule plus TIP variant that confers resistance to taxanes by impairing drug-target engagement
    Thakkar, Prashant, V
    Kita, Katsuhiro
    Del Castillo, Urko
    Galletti, Giuseppe
    Madhukar, Neel
    Navarro, Elena Vila
    Barasoain, Isabel
    Goodson, Holly, V
    Sackett, Dan
    Diaz, Jose Fernando
    Lu, Yao
    RoyChoudhury, Arindam
    Molina, Henrik
    Elemento, Olivier
    Shah, Manish A.
    Giannakakou, Paraskevi
    DEVELOPMENTAL CELL, 2021, 56 (23) : 3264 - +
  • [43] DeepACTION: A deep learning-based method for predicting novel drug-target interactions
    Mahmud, S. M. Hasan
    Chen, Wenyu
    Jahan, Hosney
    Dai, Bo
    Din, Salah Ud
    Dzisoo, Anthony Mackitz
    ANALYTICAL BIOCHEMISTRY, 2020, 610
  • [44] Prediction of drug-target interactions based on multi-layer network representation learning
    Shang, Yifan
    Gao, Lin
    Zou, Quan
    Yu, Liang
    NEUROCOMPUTING, 2021, 434 : 80 - 89
  • [45] Fluorescent Imaging for In Situ Measurement of Drug Target Engagement and Cell Signaling Pathways
    McMahon, Nathan P.
    Solanki, Allison
    Jones, Jocelyn
    Kwon, Sunjong
    Chang, Young-Hwan
    Chin, Koei
    Nederlof, Michel A.
    Gray, Joe W.
    Gibbs, Summer L.
    VISUALIZING AND QUANTIFYING DRUG DISTRIBUTION IN TISSUE IV, 2020, 11219
  • [46] A Synthetic Biology Project - Developing a single-molecule device for screening drug-target interactions
    Firman, Keith
    Evans, Luke
    Youell, James
    FEBS LETTERS, 2012, 586 (15) : 2157 - 2163
  • [47] SELF-BLM: Prediction of drug-target interactions via self-training SVM
    Keum, Jongsoo
    Nam, Hojung
    PLOS ONE, 2017, 12 (02):
  • [48] Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition
    Wang, Cheng
    Wang, Wenyan
    Lu, Kun
    Zhang, Jun
    Chen, Peng
    Wang, Bing
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2020, 21 (16) : 1 - 14
  • [49] Open-source chemogenomic data-driven algorithms for predicting drug-target interactions
    Hao, Ming
    Bryant, Stephen H.
    Wang, Yanli
    BRIEFINGS IN BIOINFORMATICS, 2019, 20 (04) : 1465 - 1474
  • [50] GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data
    Liu, Guannan
    Singha, Manali
    Pu, Limeng
    Neupane, Prasanga
    Feinstein, Joseph
    Wu, Hsiao-Chun
    Ramanujam, J.
    Brylinski, Michal
    JOURNAL OF CHEMINFORMATICS, 2021, 13 (01)