Computational Analysis of Kinase Selectivity using Structural Knowledge

Here, we present a knowledge-based approach to profile kinase selectivity based on the similarity between drug binding microenvironments. To allow large-scale kinase site similarity profiling, we have created a kinome structure database consisting of 5000 inhibitor-binding pockets from 187 unique human kinase crystal structures. <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=1275#pack_1983">KinomeFEATURE database </a> : KinomeFEATURE database V1 </li> </ul>

本研究提出了一种基于药物结合微环境相似性的知识库方法，以描述激酶的选择性。为便于大规模激酶位点相似性分析，我们构建了一个包含187种独特人类激酶晶体结构中5000个抑制剂结合口袋的激酶组结构数据库。本项目包含以下软件/数据包：

<ul>
<li><a href="https://simtk.org/frs?group_id=1275#pack_1983">激酶组特征数据库（KinomeFEATURE database）</a>：激酶组特征数据库V1</li>
</ul>