Table 2.

Effects of experimental and sequence variables on prediction power

Model	Variables Used in Prediction Model									DSPred Errorf	Corre- lationg	ROC Areah
	Experimental Variables					Sequence Variables						DS>3	DS≥5
A. Best with expt & seqa	R30	YldS	SECR1	DLSMR		MW	Dismax			1.96 (0.13)	0.56 (0.06)	0.77 (0.04)	0.87 (0.05)
B. Leave out seq from Ab	R30	(YldS)	SECR1	DLSMR						2.73 (0.08)	−0.07 (0.06)	0.61 (0.05)	0.49 (0.06)
C. Leave out expt from Ac						MW	Dismax			2.46 (0.10)	0.18 (0.07)	0.65 (0.05)	0.69 (0.06)
D. Best with expt onlyd	R30	YldS	SECPP d	DLSMW d	LPav d					1.90 (0.06)	0.57 (0.04)	0.70 (0.08)	0.71 (0.08)
E. Best with seq onlye						MW	Dismax	Hydav e	XPe	2.58 (0.12)	0.17 (0.08)	0.64 (0.05)	0.63 (0.06)

For descriptions of variables see Table 1.

^aBest partition model combining experimental and sequence variables from 77-sample training set.

^bThe 4 experimental variables from model A were supplied to the partition algorithm. The algorithm discarded Yld_S as a criterion.

^cThe 2 sequence variables from A were supplied to the algorithm; the algorithm used both as criteria.

^dAll experimental variables were supplied. The algorithm used 2 of the same variables as in A, replaced SEC_R1 and DLS_MR with related variables SEC_PP and DLS_MW, and added LP_av.

^eAll sequence variables were supplied; hydropathy (Hyd_av) and XtalPred score (XP) were added to the sequence variables used in A.

^f,g,hThree measures of predictive power for the 30-sample test set (parentheses: standard deviation estimated from synthetic data).

^fSquare root of the mean square difference between predicted and observed diffraction scores (DS).

^gPearson‘s correlation coefficient for predicted and observed DS.

^hArea under ROC curves as in Figure 4b, with success defined as “better than 10 Å diffraction” (DS > 3) or as “2.8 Å or better diffraction” (DS ≥ 5).