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High-Throughput Screening for Enzyme Inhibitors Using Frontal Affinity Chromatography with Liquid Chromatography and Mass Spectrometry

机译:使用额叶亲和色谱-液相色谱和质谱法高通量筛选酶抑制剂

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摘要

This work presents new frontal affinity chromatography (FAC) methodologies for high-throughput screening of compound libraries, designed to increase screening rates and improve sensitivity and ruggedness in performance. A FAC column constructed around the enzyme N-acetyl-glucosaminyltransferase V (GnT-V) was implemented in the identification of potential enzyme inhibitors from two libraries of trisaccharides. Effluent from the FAC column was fractionated, sequentially processed via LC/MS, and referenced to a similar analysis through a control FAC column lacking the enzyme. The resulting multidimensional data sets were compared across corresponding sample and control fractions to identify binders, in a semiautomated approach. A strong binder in the protonated form at m/z 795 was identified from the first library of 81 compounds, exhibiting an estimated K_(d) value of 0.3 (mu)M. Other binders yielded K_(d) values ranging from 0.35 to 3.35 (mu)M. To demonstrate the improvement in performance of this FAC-LC/MS approach over the conventional online FAC/MS approach, 15 compounds from this library were blended with a second library of 1000 synthetic trisaccharides and screened against GnT-V. All ligands in the 15-compound set were identified in this larger screen, and no ligands of greater affinity than compound 1 were found. Our results show that FAC-LC/MS is a reliable method for screening large compound libraries directly and useful for large-scale ligand discovery initiatives.
机译:这项工作为化合物库的高通量筛选提供了新的额叶亲和色谱(FAC)方法,旨在提高筛选速率并提高性能的灵敏度和耐用性。围绕N-乙酰-氨基葡萄糖氨基转移酶V(GnT-V)酶构建的FAC色谱柱用于从两个三糖文库中鉴定潜在的酶抑制剂。分离来自FAC柱的流出物,通过LC / MS进行顺序处理,并通过缺乏酶的对照FAC柱进行类似分析。使用半自动化方法,将所得的多维数据集在相应的样品和对照馏分之间进行比较,以鉴定粘合剂。从第一个81种化合物库中鉴定出质子化形式m / z 795的强结合剂,其估计K_(d)值为0.3μM。其他粘合剂产生的K_(d)值为0.35至3.35μM。为了证明该FAC-LC / MS方法相对于常规在线FAC / MS方法的性能有所提高,将来自该文库的15种化合物与第二个包含1000种合成三糖的文库混合,并针对GnT-V进行了筛选。在此较大的筛选中鉴定出15种化合物中的所有配体,未发现比化合物1具有更高亲和力的配体。我们的结果表明,FAC-LC / MS是直接筛选大型化合物库的可靠方法,可用于大规模的配体发现计划。

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