Suppr超能文献

使用原子力显微镜力谱法优化用于药物发现的蛋白质-蛋白质相互作用测量

Optimization of Protein-Protein Interaction Measurements for Drug Discovery Using AFM Force Spectroscopy.

作者信息

Yang Yongliang, Zeng Bixi, Sun Zhiyong, Esfahani Amir Monemian, Hou Jing, Jiao Nian-Dong, Liu Lianqing, Chen Liangliang, Basson Marc D, Dong Lixin, Yang Ruiguo, Xi Ning

机构信息

Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48823, USA.

Departments of Surgery and Biomedical Sciences, University of North Dakota, Grand Forks, ND, 58202, USA.

出版信息

IEEE Trans Nanotechnol. 2019;18:509-517. doi: 10.1109/tnano.2019.2915507. Epub 2019 May 14.

Abstract

Increasingly targeted in drug discovery, protein-protein interactions challenge current high throughput screening technologies in the pharmaceutical industry. Developing an effective and efficient method for screening small molecules or compounds is critical to accelerate the discovery of ligands for enzymes, receptors and other pharmaceutical targets. Here, we report developments of methods to increase the signal-to-noise ratio (SNR) for screening protein-protein interactions using atomic force microscopy (AFM) force spectroscopy. We have demonstrated the effectiveness of these developments on detecting the binding process between focal adhesion kinases (FAK) with protein kinase B (Akt1), which is a target for potential cancer drugs. These developments include optimized probe and substrate functionalization processes and redesigned probe-substrate contact regimes. Furthermore, a statistical-based data processing method was developed to enhance the contrast of the experimental data. Collectively, these results demonstrate the potential of the AFM force spectroscopy in automating drug screening with high throughput.

摘要

在药物研发中,蛋白质-蛋白质相互作用越来越成为研究目标,这对制药行业当前的高通量筛选技术提出了挑战。开发一种有效且高效的小分子或化合物筛选方法对于加速酶、受体及其他药物靶点配体的发现至关重要。在此,我们报告了利用原子力显微镜(AFM)力谱法提高蛋白质-蛋白质相互作用筛选信噪比(SNR)的方法进展。我们已经证明了这些进展在检测粘着斑激酶(FAK)与蛋白激酶B(Akt1)之间结合过程中的有效性,Akt1是潜在抗癌药物的靶点。这些进展包括优化探针和底物功能化过程以及重新设计探针-底物接触模式。此外,还开发了一种基于统计的数据处理方法以增强实验数据的对比度。总体而言,这些结果证明了AFM力谱法在高通量自动化药物筛选中的潜力。

相似文献

8
Structural and mechanistic insights into mechanoactivation of focal adhesion kinase.机械激活粘着斑激酶的结构和机制见解。
Proc Natl Acad Sci U S A. 2019 Apr 2;116(14):6766-6774. doi: 10.1073/pnas.1820567116. Epub 2019 Mar 15.
10
Single-cell force spectroscopy.单细胞力谱学
J Cell Sci. 2008 Jun 1;121(11):1785-91. doi: 10.1242/jcs.030999.

本文引用的文献

2
cat-ELCCA: catalyzing drug discovery through click chemistry.通过点击化学加速药物发现。
Chem Commun (Camb). 2018 Jun 19;54(50):6531-6539. doi: 10.1039/c8cc02332h.
6
Biophysics in drug discovery: impact, challenges and opportunities.药物发现中的生物物理学:影响、挑战与机遇。
Nat Rev Drug Discov. 2016 Oct;15(10):679-98. doi: 10.1038/nrd.2016.123. Epub 2016 Aug 12.
7
Protein self-assembly via supramolecular strategies.蛋白质通过超分子策略进行自组装。
Chem Soc Rev. 2016 May 21;45(10):2756-67. doi: 10.1039/c6cs00004e. Epub 2016 Apr 15.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验