Table 3.

The effect of long chains and learning rates on the ten-fold-cross-validated accuracy for predicting the ϕ and ψ angles, and RSA

Method	(2479,0.01)a	(2640,0.01)b	(2640,0.001)c
ψ - Q10d	50.7 ± 0.5%	49.7 ± 0.4%	49.7 ± 0.5%
ψ - Q10%e	68.5 ± 0.5%	67.5 ± 0.4%	67.5 ± 0.5%
ψ - PCCf	0.746 ± 0.007	0.743 ± 0.006	0.743 ± 0.006
ψ - MAEg	36.1 ± 0.8°	36.4 ± 0.7°	36.3 ± 0.7°
ϕ - Q10d	56.1 ± 0.5%	56.1 ± 0.6%	56.0 ± 0.5%
ϕ - Q10%e	82.4 ± 0.4%	82.1 ± 0.3%	81.2 ± 0.3%
ϕ - PCCf	0.659 ± 0.005	0.656 ± 0.005	0.656 ± 0.005
ϕ - MAEg	22.2 ± 0.4°	22.1 ± 0.3°	22.2 ± 0.2°
RSA - Q10d	39.9 ± 0.4%	40.2 ± 0.5%	40.1 ± 0.6%
RSA - Q10%e	58.1 ± 0.3%	58.2 ± 0.4%	58.0 ± 0.5%
RSA - PCCf	0.745 ± 0.004	0.739 ± 0.005	0.739 ± 0.006
RSA - MAEg	0.111 ± 0.001	0.111 ± 0.001	0.111 ± 0.001

^aExperiment IV for the dataset of 2479 proteins.

^bExperiment IV for the dataset of 2640 proteins (including chains with more than 500 residues).

^cExperiment IV for the dataset of 2640 proteins but with a slow learning rate of 0.001

^dQ₁₀: Fraction of residues with correctly predicted states. Angles are divided into 10 states with 36° per bin. Residue solvent accessibility (RSA) is divided into 10 states with 0.1 per bin.

^eQ_10%: Fraction of residues whose angles are predicted within 36° (or 10% for RSA) from the true value.

^fPearson’s correlation coefficient between predicted and actual values.

^gMean-absolute error between predicted and actual values. Degrees are used for the ϕ and ψ angles and [0, 1] normalization for the RSA.