Lievens Sam, Van der Heyden José, Masschaele Delphine, De Ceuninck Leentje, Petta Ioanna, Gupta Surya, De Puysseleyr Veronic, Vauthier Virginie, Lemmens Irma, De Clercq Dries J H, Defever Dieter, Vanderroost Nele, De Smet Anne-Sophie, Eyckerman Sven, Van Calenbergh Serge, Martens Lennart, De Bosscher Karolien, Libert Claude, Hill David E, Vidal Marc, Tavernier Jan
From the ‡Medical Biotechnology Center, VIB, Ghent, Belgium.
§Department of Biochemistry, Ghent University, Ghent, Belgium.
Mol Cell Proteomics. 2016 Dec;15(12):3624-3639. doi: 10.1074/mcp.M116.061994. Epub 2016 Nov 1.
Because proteins are the main mediators of most cellular processes they are also prime therapeutic targets. Identifying physical links among proteins and between drugs and their protein targets is essential in order to understand the mechanisms through which both proteins themselves and the molecules they are targeted with act. Thus, there is a strong need for sensitive methods that enable mapping out these biomolecular interactions. Here we present a robust and sensitive approach to screen proteome-scale collections of proteins for binding to proteins or small molecules using the well validated MAPPIT (Mammalian Protein-Protein Interaction Trap) and MASPIT (Mammalian Small Molecule-Protein Interaction Trap) assays. Using high-density reverse transfected cell microarrays, a close to proteome-wide collection of human ORF clones can be screened for interactors at high throughput. The versatility of the platform is demonstrated through several examples. With MAPPIT, we screened a 15k ORF library for binding partners of RNF41, an E3 ubiquitin protein ligase implicated in receptor sorting, identifying known and novel interacting proteins. The potential related to the fact that MAPPIT operates in living human cells is illustrated in a screen where the protein collection is scanned for interactions with the glucocorticoid receptor (GR) in its unliganded versus dexamethasone-induced activated state. Several proteins were identified the interaction of which is modulated upon ligand binding to the GR, including a number of previously reported GR interactors. Finally, the screening technology also enables detecting small molecule target proteins, which in many drug discovery programs represents an important hurdle. We show the efficiency of MASPIT-based target profiling through screening with tamoxifen, a first-line breast cancer drug, and reversine, an investigational drug with interesting dedifferentiation and antitumor activity. In both cases, cell microarray screens yielded known and new potential drug targets highlighting the utility of the technology beyond fundamental biology.
由于蛋白质是大多数细胞过程的主要介导者，它们也是主要的治疗靶点。确定蛋白质之间以及药物与其蛋白质靶点之间的物理联系，对于理解蛋白质本身及其靶向分子的作用机制至关重要。因此，迫切需要能够绘制这些生物分子相互作用的灵敏方法。在此，我们展示了一种稳健且灵敏的方法，使用经过充分验证的MAPPIT（哺乳动物蛋白质-蛋白质相互作用陷阱）和MASPIT（哺乳动物小分子-蛋白质相互作用陷阱）检测法，筛选蛋白质组规模的蛋白质集合与蛋白质或小分子的结合情况。利用高密度反向转染细胞微阵列，可以高通量筛选接近蛋白质组范围的人类开放阅读框（ORF）克隆的相互作用分子。通过几个例子证明了该平台的多功能性。利用MAPPIT，我们筛选了一个包含15000个ORF的文库，寻找RNF41的结合伙伴，RNF41是一种参与受体分选的E3泛素蛋白连接酶，鉴定出了已知和新的相互作用蛋白质。MAPPIT在活的人类细胞中起作用这一事实的潜力，在一个筛选中得到了体现，该筛选扫描蛋白质集合与未结合配体状态和地塞米松诱导激活状态下的糖皮质激素受体（GR）的相互作用。鉴定出了几种蛋白质，其相互作用在配体与GR结合后受到调节，包括一些先前报道的GR相互作用分子。最后，该筛选技术还能够检测小分子靶蛋白，这在许多药物发现项目中是一个重要障碍。我们通过用他莫昔芬（一种一线乳腺癌药物）和雷弗西丁（一种具有有趣的去分化和抗肿瘤活性的研究性药物）进行筛选，展示了基于MASPIT的靶点分析的效率。在这两种情况下，细胞微阵列筛选都产生了已知和新的潜在药物靶点，突出了该技术在基础生物学之外的实用性。
