Title: In silico identification and functional annotation of yeast E3 ubiquitin ligase Rsp5 substrates

Authors: Xiaofeng Song; Lizhen Hu; Ping Han; Xuejiang Guo; Jiahao Sha

Addresses: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China ' State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, Nanjing Medical University, Nanjing 210029, China ' State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, Nanjing Medical University, Nanjing 210029, China

Abstract: Rsp5, E3 ligases conserved from yeast to mammals, plays a key role in diverse processes in yeast. However, many of Rsp5 substrates are still unclear. Therefore we proposed an in silico method to recognise new substrates of Rsp5. To investigate the molecular determinants that affect the interaction between Rsp5 and its substrate, we have systematically analysed many features that perhaps correlated with the Rsp5 substrate recognition. It is found that PPxY motif, transmembrane region, disorder region and N-linked glycosylation modification are the most important features for substrate recognition. We have constructed an SVM-based classifier to recognise Rsp5 substrates, obtaining 81.5% sensitivity and 74.1% specificity averagely on ten independent testing dataset. We also applied the model on the whole yeast proteome, and identified ∼66 new Rsp5 substrates. Functional annotation reveals that half of these novel substrates function in the Rsp5 involved cell processes as Rsp5-interacting proteins.

Keywords: E3 ubiquitin ligase; Rsp5 substrate; yeast proteome; bioinformatics; in silico identification; functional annotation; PPxY motif; transmembrane region; disorder region; N-linked glycosylation modification; substrate recognition; SVM; support vector machines.

DOI: 10.1504/IJDMB.2015.072754

International Journal of Data Mining and Bioinformatics, 2015 Vol.13 No.4, pp.321 - 337

Received: 16 Jul 2013
Accepted: 13 Nov 2014
Published online: 28 Oct 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article