Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Table 7 Summary of results for the regression problem

Method	*RMSE*	r	Precision	Recall	F1 score	*MCC*
SVM	1.34 ± 0.05	0.54 ± 0.03	0.62 ± 0.05	0.44 ± 0.05	0.52 ± 0.03	0.41 ± 0.04
GP	1.36	0.52	0.58	0.47	0.52	0.39
Robetta	1.52	0.47	0.52	0.47	0.49	0.35
LLSF	1.36	0.51	0.54	0.46	0.50	0.36
LLSF^(a)	1.45	0.44	0.54	0.42	0.47	0.34
LLSF^(b)	1.43	0.47	0.55	0.43	0.48	0.35

SVM: Support Vector Machine, GP: Gaussian Processes, LLSF: Linear Least Squares Fit, LLSF^(a): Linear Least Squares Fit with inter-molecular side-chain van der Waals as only input, LLSF^(b): Linear Least Squares Fit with inter-molecular side-chain van der Waals and hydrogen bond as inputs. RMSE stands for root mean squared error, r for correlation coefficient and MCC for Matthews correlation coefficient (see Methods section for definition of the performance measures).

ISSN: 1471-2105