Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

BMC Bioinformatics

Research Article

Lina Zhang¹  Runtao Yang¹  Rui Gao¹  Chengjin Zhang²  Qing Song³ 

[1] School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, 250061, Jinan, China;School of Control Science and Engineering, Shandong University, Jingshi Road No.17923, 250061, Jinan, China;School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Wenhuaxi Road No.180, 264209, Weihai, China;School of Electrical Engineering, University of Jinan, Nanxinzhuangxi Road No.336, 250022, Jinan, China;

关键词: Aptamer-protein interacting pairs;    Ensemble method;    Hybrid features;    Imbalanced data problem;   

DOI  :  10.1186/s12859-016-1087-5

 received in 2016-01-07, accepted in 2016-05-17,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundAptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.ResultsIn this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden’s Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden’s Index of 0.451.ConclusionsThese results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

【 授权许可】

CC BY   
© Zhang et al. 2016

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