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串联质谱编码文库在超高通量命中发现方面的进展。

Advances in Ultrahigh Throughput Hit Discovery with Tandem Mass Spectrometry Encoded Libraries.

机构信息

Leiden University, 2333 CC Leiden, The Netherlands.

出版信息

J Am Chem Soc. 2023 Aug 30;145(34):19129-19139. doi: 10.1021/jacs.3c04899. Epub 2023 Aug 9.

Abstract

Discovering new bioactive molecules is crucial for drug development. Finding a hit compound for a new drug target usually requires screening of millions of molecules. Affinity selection based technologies have revolutionized early hit discovery by enabling the rapid screening of libraries with millions or billions of compounds in short timeframes. In this Perspective, we describe recent technology breakthroughs that enable the screening of ultralarge synthetic peptidomimetic libraries with a barcode-free tandem mass spectrometry decoding strategy. A combination of combinatorial synthesis, affinity selection, automated peptide sequencing algorithms, and advances in mass spectrometry instrumentation now enables hit discovery from synthetic libraries with over 100 million members. We provide a perspective on this powerful technology and showcase success stories featuring the discovery of high affinity binders for a number of drug targets including proteins, nucleic acids, and specific cell types. Further, we show the usage of the technology to discover synthetic peptidomimetics with specific functions and reactivity. We predict that affinity selection coupled with tandem mass spectrometry and automated decoding will rapidly evolve further and become a broadly used drug discovery technology.

摘要

发现新的生物活性分子对于药物开发至关重要。为新的药物靶点找到一个命中化合物通常需要筛选数百万种分子。基于亲和力选择的技术通过在短时间内对包含数百万或数十亿种化合物的文库进行快速筛选,彻底改变了早期命中发现。在本观点中,我们描述了最近的技术突破,这些突破使无条码串联质谱解码策略能够筛选超大规模的合成拟肽库。组合合成、亲和选择、自动化肽测序算法以及质谱仪器的进步,现在使人们能够从包含超过 1 亿个成员的合成文库中发现命中化合物。我们对这项强大的技术提供了一个视角,并展示了一些成功案例,这些案例涉及到许多药物靶点(包括蛋白质、核酸和特定细胞类型)的高亲和力结合物的发现。此外,我们展示了该技术用于发现具有特定功能和反应性的合成拟肽。我们预测,亲和选择与串联质谱和自动化解码的结合将迅速进一步发展,并成为一种广泛使用的药物发现技术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecf3/10472510/726881177d2c/ja3c04899_0001.jpg

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