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Table 3 The prediction performance of phi and psi angles of using different local window sizes with four deep learning methods

From: Deep learning methods for protein torsion angle prediction

Torsion Angle Window sizea Featuresb DRNN DReRBM DNN DRBM
phi 1 56 21.09 21.81 22.13 21.95
3 168 20.52 20.77 21.49 21.07
5 280 20.39 20.92 21.24 20.89
7 392 20.39 21.03 21.22 20.84
9 504 20.40 20.95 21.28 21.62
11 616 20.49 20.88 21.04 21.57
13 728 20.56 20.98 21.27 21.79
15 840 20.63 21.12 21.19 21.69
17 952 20.69 21.04 21.38 21.66
psi 1 56 31.68 32.93 33.55 33.02
3 168 29.29 29.86 30.14 29.74
5 280 28.96 29.94 29.25 28.92
7 392 28.85 30.11 29.25 28.85
9 504 28.86 29.94 29.38 29.61
11 616 29.06 29.95 29.06 29.75
13 728 29.27 30.13 29.38 30.19
15 840 29.44 30.48 29.24 30.25
17 952 29.72 30.36 29.54 30.33
  1. aNumber of window size range from 1 to 17
  2. bNumber of features as input for the deep learning model. For each residue, we used 7 kinds of features, represented by 56 numbers
  3. The bold fond denotes the best result for each method