POOL 服务器：用于蛋白质功能位点预测的机器学习应用程序。
POOL server: machine learning application for functional site prediction in proteins.
机构信息
Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.
出版信息
Bioinformatics. 2012 Aug 1;28(15):2078-9. doi: 10.1093/bioinformatics/bts321. Epub 2012 Jun 1.
SUMMARY
We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction.
AVAILABILITY
The web site is free and open to all users with no login requirements at http://www.pool.neu.edu.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
我们提出了一个自动化的网络服务器，用于部分订单最优似然（POOL），这是一种机器学习应用程序，它将计算的静电和几何信息结合起来，从 3D 结构中高性能地预测催化残基。输入特征包括 THEMATICS 静电数据和 ConCavity 的口袋信息。THEMATICS 测量偏离典型的、S 型滴定行为的程度，以识别功能重要的残基，而 ConCavity 通过分析蛋白质结构的表面几何形状来识别结合口袋。THEMATICS 和 ConCavity（仅结构）都不需要查询蛋白与其他蛋白具有任何序列或结构相似性。因此，POOL 适用于具有新颖折叠和工程化蛋白的蛋白。作为对具有序列同源物的情况的附加选择，用户可以包括 INTREPID 的进化信息，以提高位点预测的准确性。
可用性
该网站是免费的，对所有用户开放，无需登录要求，网址为 http://www.pool.neu.edu。
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补充信息
补充数据可在 Bioinformatics 在线获得。
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