Prediction of RNA-binding proteins from primary sequence by a support vector machine approach.

• Main Authors: HAN, Lian Yi, CAI, Cong Zhong, LO, Siaw Ling, CHUNG, Maxey, CHEN, Yu Zong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2004
• Subjects: RNA-binding proteins; RNA-protein interactions; rRNA; mRNA; tRNA; snRNA; support vector machine; Bioinformatics; Computer Sciences; Life Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/4876; https://ink.library.smu.edu.sg/context/sis_research/article/5879/viewcontent/Prediction___PV.pdf
• Institution: Singapore Management University

Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions.