Skip to main content

Table 4 What is the effect on FunFOLD performance if different starting models are used?

From: FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins

Group ID N Mean score for group Mean score for FunFOLD Increase in mean score Mean Z-score for group Mean Z-score for FunFOLD P-value
(raw score)
P-value (Z-scores) 1 - p-value
(raw score)
1 - p-value
(Z-score)
   MCC BDT MCC BDT MCC BDT MCC BDT MCC BDT MCC BDT MCC BDT MCC BDT MCC BDT
FN407 25 0.768 0.689 0.789 0.697 0.021 0.008 1.097 1.095 1.169 1.128 0.493 0.528 0.478 0.542 0.507 0.472 0.522 0.458
FN293 19 0.742 0.684 0.774 0.714 0.032 0.030 1.017 1.027 1.134 1.158 0.370 0.269 0.301 0.301 0.630 0.731 0.699 0.699
FN202 22 0.692 0.593 0.791 0.716 0.098 0.123 0.868 0.824 1.142 1.183 0.015 0.027 0.018 0.040 0.985 0.973 0.982 0.960
  1. A repeat comparison of FunFOLD against the top 3 groups at CASP8, using TS1 3D models obtained from each comparison group. LEE_TS1 models were used for the FunFOLD comparison with group FN407, LEE-S_TS1 models for the comparison with FN293 and Phyre-de-novo_TS1 for the comparison with FN202.