- 综合性科技类中文核心期刊
- 中国科技论文统计源期刊
- 中国科学引文数据库来源期刊
- 中国学术期刊文摘数据库（核心版）来源期刊
- 中国学术期刊综合评价数据库来源期刊
| Citation: |
GONG Xinqi, CAO Tingyi. Five Dimensional Feature Space for Protein Binding Site Residue Prediction[J]. Journal of Beijing University of Technology, 2017, 43(12): 1837-1845. DOI: 10.11936/bjutxb2017070008
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Correctly understanding and predicting binding site residues is necessary for studying protein interactions and their networks. A novel approach was reported to predict protein interface residues using five dimensional feature space based on residue physical and chemical features. In this method, a grid multiple dimensional feature space was built using standardized feature values. Then, all the surface residues were put into the grids according to their feature values. Finally, the grids were clustered. Interestingly, interface residues prefer some grids clustered together. Excellent prediction result were obtained on a public and data benchmark was verified. This approach not only opens up a new visual angle for binding site residue prediction, but also help to understand protein-protein interactions more deeply.
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