Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches
Fig 2
Comparison of deep learning models using different types of features and comparison of structural features of binding and non-binding residues.
(A) AUC measures for different types of features. Significance tests were performed as described in the Methods section. (B) MCC measures for different types of features. (C) Scatter plots of AUC for native structures and an example with prediction results generated by different types of features. (D) Comparison of partial structural features between DNA-binding and non-binding residues. The complete comparison is presented in S2 Fig. ORC: Ollivier Ricci curvature, FRC: Forman Ricci curvature, MFD: multifractal dimension, MIR: minimum inaccessible radius, ASV: accessible shell volume, and USR: ultrafast shape recognition. Significant differences were evaluated using Wilcoxon rank sum test. **** p < 0.0001, *** 0.0001 ≤ p < 0.001, ** 0.001 ≤ p < 0.01, * 0.01 ≤ p < 0.05 and ns: p ≥ 0.05.