Title: PPIPP: an online protein-protein interaction network prediction and analysis platform

Authors: Fen Wang; Baoxing Song; Dengyun Li; Xing Zhao; Yaotian Miao; Pengfei Jiang; Deli Zhang

Addresses: College of Life Science, Shanxi Agricultural University, Taigu 030800, Jinzhong City, Shanxi Province, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China ' College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi 712100, China

Abstract: Although several methods have been developed for protein-protein interaction (PPI) prediction, each method has a specialised emphasis, and it is often necessary to use multiple methods to avoid a high false-negative rate. We here describe a method that is based on binding profiles and only requires protein sequence as an input. We also developed an online platform, the PPI Prediction Platform (PPIPP), to predict PPI networks (PPINs). PPIPP, which is freely accessible at http://ppipp.songbx.me, provides two main functions: PPI prediction, which uses the binding profile method, domain-motif interactions from structural topology, and PPIN-based detection of functionally similar proteins within species. PPIPP offers a web-based interface to facilitate PPIN predictions and a high-performance server to overcome the problems of user access and large-scale computation. The wheat proteome was used to evaluate the performance of this platform.

Keywords: protein-protein interaction; binding profiles; online PPI; domain-motif interactions; structural topology; PPI network prediction; PPI network analysis; bioinformatics; protein sequences; functionally similar proteins; wheat proteome.

DOI: 10.1504/IJDMB.2016.075819

International Journal of Data Mining and Bioinformatics, 2016 Vol.14 No.4, pp.305 - 314

Received: 14 Jan 2015
Accepted: 06 Aug 2015
Published online: 06 Apr 2016 *

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