Abstract
Short linear motifs (SLiMs) play a central role in several biological functions, such as cell regulation, scaffolding, cell signaling, post-translational modification, and cleavage. Identifying SLiMs is an important step for understanding their functions and mechanism. Due to their short length and particular properties, discovery of SLiMs in proteins is a challenge both experimentally and computationally. So far, many existing computational methods adopted many predicted sequence or structures features as input for prediction, there is no report about using position-specific scoring matrix (PSSM) profiles of proteins directly for SLiMs prediction. In this study, we describe a simple method, named as PSSMpred, which only use the evolutionary information generated in form of PSSM profiles of protein sequences for SLiMs prediction. When comparing with other methods tested on the same datasets, PSSMpred achieves the best performances: (1) achieving 0.03–0.1 higher AUC than other methods when tested on HumanTest151; (2) achieving 0.03–0.05 and 0.03–0.06 higher AUC than other methods when tested on ANCHOR-short and ANCHOR-long respectively.
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Acknowledgment
We gratefully thank Dr. Catherine Mooney for providing us with the datasets of SLiMPred, and Professor Xiaohong Liu of Shandong University of Technology for providing a lot of convenience for our study. This work was supported by a grant from Natural Science Foundation of Shandong Province (NO. ZR2014FQ028). We also thank the anonymous reviewers for his/her helpful comments, which improved the manuscript.
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Fang, C., Noguchi, T., Yamana, H., Sun, F. (2016). Identifying Protein Short Linear Motifs by Position-Specific Scoring Matrix. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_22
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DOI: https://doi.org/10.1007/978-3-319-41009-8_22
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