Structure-aware protein solubility prediction from sequence through graph convolutional network and predicted contact map

Table 2 The \({\text{R}}^{2}\) between the actual solubility scores and those predicted by GraphSol based on individual feature groups, removing each feature group from the final GraphSol model, and recursively adding feature groups according to their importance, respectively

Feature groups^a	CV^d	Ind. test	Feature groups^b	CV^d	Ind. test	Features groups^c	CV^d	Ind. test
–	–	–	GraphSol	0.476 \(\pm \) 0.014	0.483	–	–	–
BLOSUM	0.329 \(\pm \) 0.016	0.317	− BLOSUM	0.460 \(\pm \) 0.011	0.465	BLOSUM	0.329 \(\pm \) 0.016	0.317
AAPHY7	0.293 \(\pm \) 0.014	0.289	− AAPHY7	0.465 \(\pm \) 0.012	0.479	+ SPIDER3	0.413 \(\pm \) 0.012	0.409
PSSM	0.333 \(\pm \) 0.012	0.332	− PSSM	0.457 \(\pm \) 0.017	0.467	+ PSSM	0.456 \(\pm \) 0.011	0.453
HMM	0.337 \(\pm \) 0.015	0.341	− HMM	0.455 \(\pm \) 0.016	0.458	+ HMM	0.465 \(\pm \) 0.012	0.479
SPIDER3	0.231 \(\pm \) 0.019	0.227	− SPIDER3	0.428 \(\pm \) 0.018	0.449	+ AAPHY7	0.476 \(\pm \) 0.014	0.483

ISSN: 1758-2946