Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.
Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy Biocomputing Group, University of Bologna, CIRI-Health Science and Technology/Department of Biology, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy.
Bioinformatics. 2014 Oct 15;30(20):2973-4. doi: 10.1093/bioinformatics/btu411. Epub 2014 Jun 27.
Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.
Supplementary data are available at Bioinformatics online.
靶向肽是蛋白质中的 N 端分拣信号，通过与不同的蛋白质机器相互作用，促进它们转移到线粒体中。我们最近开发了一种基于机器学习的方法 TPpred，在预测蛋白质序列中靶向肽的存在及其切割位点方面，TPpred 得分在可用方法中名列前茅。在这里，我们介绍了 TPpred2，它提高了 TPpred 在识别靶向肽切割位点任务中的性能。TPpred2 现在有一个网络界面和一个独立版本，供用户自由下载并用于处理大量序列。可用性和实现：TPpred2 可在 http://tppred2.biocomp.unibo.it 作为网络服务器和独立版本使用。
补充数据可在“Bioinformatics”在线获取。
