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基于蛋白质-化学相互作用,通过虚拟筛选和实验验证对靶蛋白进行全面预测。

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

作者信息

Kobayashi Hiroki, Harada Hiroko, Nakamura Masaomi, Futamura Yushi, Ito Akihiro, Yoshida Minoru, Iemura Shun-Ichiro, Shin-Ya Kazuo, Doi Takayuki, Takahashi Takashi, Natsume Tohru, Imoto Masaya, Sakakibara Yasubumi

机构信息

Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan.

Chemical Genetics Laboratory, RIKEN Advanced Science Institute, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.

出版信息

BMC Chem Biol. 2012 Apr 5;12:2. doi: 10.1186/1472-6769-12-2.

Abstract

BACKGROUND

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis.

RESULTS

We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins.

CONCLUSIONS

This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

摘要

背景

生物活性化合物靶蛋白的鉴定对于阐明其作用模式至关重要;然而,一般来说靶蛋白的鉴定一直很困难,主要是因为使用亲和色谱结合考马斯亮蓝染色和串联质谱分析进行检测的灵敏度较低。

结果

我们应用了结合计算机筛选和实验验证来预测靶蛋白的方案,用于因西丁(incednine),它通过未知机制抑制Bcl-xL的抗凋亡功能。通过统计预测方法,在计算机上预测了182个可能与因西丁结合的靶蛋白候选物,并通过因西丁与七种蛋白质的体外结合对预测结果进行了验证,这七种蛋白质的表达可在我们的细胞系统中得到证实。结果成功实现了40%的计算机预测准确率,并且我们新发现了3种因西丁结合蛋白。

结论

本研究表明,我们提出的结合计算机筛选和实验验证来预测靶蛋白的方案是有用的,并为小分子靶蛋白鉴定策略提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1e0/3471015/6c45d1556b3a/1472-6769-12-2-1.jpg

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