Computational Prediction of Protein Function Based on Weighted Mapping of Domains and GO Terms

• Main Authors: Zhixia Teng, Maozu Guo, Qiguo Dai, Chunyu Wang, Jin Li, Xiaoyan Liu
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2014/641469

In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains.