Skip to main content

Advertisement

Table 2 Rescoring Fpocket and ConCavity predictions with PRANK: cross-validation results on CHEN11 dataset and the results of the final prediction model (trained on CHEN11-Fpocket) for all datasets

From: Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

Dataset Top-n [%] Rescored [%] All [%] Δ %possible* P R MCC
Fpocket predictions
CHEN11 (CV)** 47.9 58.8 71 +10.6 47.1 0.60 0.32 0.41
CHEN11*** 47.9 67.9 71 +20 86.4 0.87 1.0 0.98
ASTEX 58 63.6 81.1 +5.6 24.2 0.56 0.41 0.46
DT198 37.5 56.2 80.2 +18.8 43.9 0.31 0.38 0.33
MP210 56.6 67.7 78.8 +11.1 50 0.58 0.42 0.47
B48 74.1 81.5 92.6 +7.4 40 0.58 0.45 0.49
U48 53.7 77.8 88.9 +24.1 68.4 0.55 0.36 0.42
ConCavity predictions
CHEN11 (CV)** 47.9 50.7 52.3 +2.8 63.3 0.44 0.76 0.40
CHEN11*** 47.9 52.3 52.3 +4.4 100 0.80 0.82 0.75
ASTEX 55.2 62.9 65.7 +7.7 73.3 0.60 0.55 0.46
DT198 45.8 61.5 65.6 +15.6 78.9 0.33 0.55 0.34
MP210 57.4 66.1 68.2 +8.7 80.6 0.63 0.53 0.49
B48 66.7 77.8 81.5 +11.1 75 0.61 0.53 0.47
U48 64.8 74.1 77.8 +9.3 71.4 0.58 0.46 0.43
  1. Abbreviations: P precision, R recall, MCC Matthews correlation coefficient.
  2. *percentage of improvement that was theoretically possible to obtain by reordering pockets [ Δ / (All – Top-n)].
  3. **cross-validation results.
  4. ***results where the test set was de facto the same as the training set for the Random Forest classifier (included here only for completeness).