Journal
MOLECULAR INFORMATICS
Volume 39, Issue 8, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.202000006
Keywords
DNA-binding protein; Evolutionary profile; Dipeptide composition; Physicochemical properties; Feature selection
Categories
Funding
- National Key Research and Development Program of China [2017YFC1601800]
- National Natural Science Foundation of China [61876072, 61902153, 61772273]
- UK EPSRC [EP/N007743/1]
- China Postdoctoral Science Foundation [2018T110441]
- Six Talent Peaks Project of Jiangsu Province [XYDXX-012]
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DNA-binding proteins play essential roles in many molecular functions and gene regulation. Therefore, it becomes highly desirable to develop effective computational techniques for detecting DNA-binding proteins. In this paper, we proposed a new method, iDBP-DEP, which performs DNA-binding prediction by using the discriminative feature derived from multi-view feature sources including evolutionary profile, dipeptide composition, and physicochemical properties with feature selection. We evaluated iDBP-DEP on two benchmark datasets, i. e., PDB1075 and PDB594 by rigorous Jackknife test. Compared with the state-of-the-art sequence-based DNA-binding predictors, the proposed iDBP-DEP achieved 1.8 % and 3.0 % improvements of accuracy (Acc) and Mathew's Correlation Coefficient (MCC), respectively, on PDB1075 dataset; 7.4 % and 14.8 % improvements of Acc and MCC, respectively, on PDB594. The independent validation test with PDB186 show that the proposed method achieved the best performances on Acc (80.1 %) and MCC (0.684), which further demonstrated the robustness of iDBP-DEP for the detection of DNA-binding proteins. Datasets and codes used in this study are freely available at https://githup.com/Zll-codeside/iDBP-DEP.
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