用于预测蛋白质-配体结合位点的生物信息学工具的自动生成。
Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.
作者信息
Komiyama Yusuke, Banno Masaki, Ueki Kokoro, Saad Gul, Shimizu Kentaro
机构信息
Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan and.
Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan.
出版信息
Bioinformatics. 2016 Mar 15;32(6):901-7. doi: 10.1093/bioinformatics/btv593. Epub 2015 Nov 5.
MOTIVATION
Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.
RESULTS
We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.
AVAILABILITY AND IMPLEMENTATION
The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
在创新药物设计环境中促进配体研究需要按需对蛋白质 - 配体结合进行建模的预测工具。然而，开发适用于单个配体的预测工具需要花费大量时间和精力。一个能够快速有效地开发用户友好型蛋白质 - 配体结合预测工具的自动化生产流程将很有用。
结果
我们开发了一个自动生成蛋白质 - 配体结合预测的系统。将该系统集成到基于语义网技术的网络工具管道中，用户能够指定一个配体并在0.5 - 1天内获得预测工具。我们展示了三种机器学习算法对八个配体的高预测准确性。
可用性与实现
源代码和网络应用程序可从http://utprot.net免费下载。它们用Python实现并在Linux上支持。
联系方式
shimizu@bi.a.u - tokyo.ac.jp
补充信息
补充数据可在《生物信息学》在线获取。
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