Suppr超能文献

eF-seek:通过搜索相似的静电势和分子表面形状来预测蛋白质的功能位点。

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.

作者信息

Kinoshita Kengo, Murakami Yoichi, Nakamura Haruki

机构信息

Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minatoku, Tokyo, 108-8639, Japan.

出版信息

Nucleic Acids Res. 2007 Jul;35(Web Server issue):W398-402. doi: 10.1093/nar/gkm351. Epub 2007 Jun 12.

Abstract

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

摘要

我们开发了一种方法,通过在蛋白质数据库(PDB)中搜索相似的结合位点,来预测新蛋白质结构中的配体结合位点。根据分子表面的形状及其静电势来衡量相似性。一个新的网络服务器eF-seek提供了我们搜索方法的接口。它只需要一个PDB格式的坐标文件,并生成一个预测结果作为虚拟复合物结构,以PDB格式文件中的假定配体作为输出。此外,还会显示预测的相互作用界面,以便通过网络浏览器在我们自己的小程序查看器上检查虚拟复合物结构(网址:http://eF-site.hgc.jp/eF-seek)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a880/1933152/3f9b31e6283d/gkm351f1.jpg

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