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TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM

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Abstract

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the “intermediate” decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys.

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Notes

  1. In this study, multi-view features mean the features extracted from different sources, such as amino acids composition, protein evolutionary profile, and so on.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61373062, 61175024, 61222306, and 61233011), the Natural Science Foundation of Jiangsu (No. BK20141403), the Jiangsu University Graduate Research and Innovation Project (No. KYZZ_0123), Jiangsu Postdoctoral Science Foundation (No. 1201027C), the Science and Technology Commission of Shanghai Municipality (No. 16JC1404300), “The Six Top Talents” of Jiangsu Province (No. 2013-XXRJ-022), and the Fundamental Research Funds for the Central Universities (No. 30916011327). D. J. Yu is the corresponding author for this paper.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China

    Jun Hu, Ke Han, Yang Li, Jing-Yu Yang & Dong-Jun Yu

  2. Department of Automation, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, China

    Hong-Bin Shen

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  1. Jun Hu
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Correspondence to Dong-Jun Yu.

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Handling Editor: S. C. E. Tosatto.

Appendix A: Parameters of TargetCrys

Appendix A: Parameters of TargetCrys

See Table 8.

Table 8 Parameters of SVM models identified with the grid search program of LIBSVM software on TRAIN3587 and TRAIN1500.
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Hu, J., Han, K., Li, Y. et al. TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM. Amino Acids 48, 2533–2547 (2016). https://doi.org/10.1007/s00726-016-2274-4

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