Ridge regression estimated linear probability model predictions of O-glycosylation in proteins with structural and sequence data

Table 15 LS estimated LPM predicting O-glycosylation probabilities given structural and sequence data

Variable^a	β	White’s \|t\|	Variable^a	β	White’s \|t\|	Variable^a	β	White’s \|t\|
intercept	-0.1177	5.17	m8N	-0.0842	3.18	p7T	-0.1371	6.97
m1D	0.1827	5.30	m8P	-0.1387	6.82	p7V	-0.1173	4.68
m1L	0.0930	3.71	m8V	-0.2103	7.60	p7Y	0.1188	4.05
m1P	0.7844	13.86	p1A	0.1754	5.59	p8H	0.1194	2.51
m1R	-0.0619	2.34	p1D	0.0984	2.65	p8K	0.0484	1.52
m3A	0.2046	8.22	p1F	0.0831	2.43	p8N	0.1052	3.22
m3C	-0.1245	4.37	p1S	0.0884	3.27	p8Q	0.1466	4.52
m3L	-0.1099	6.18	p1T	0.1567	4.59	pos	0.2509	8.51
m3N	-0.2151	3.23	p1V	0.0623	3.34	ASA_zero	0.7952	9.89
m3T	-0.0743	2.90	p2A	0.0697	1.33	II	0.1387	3.12
m4E	0.1109	2.61	p2H	0.2885	4.65	II´	-0.2684	4.04
m4F	-0.0881	3.43	p2P	0.2730	9.84	Helix	0.1508	7.66
m4N	0.1405	3.27	p2Q	-0.1379	4.70	Beta Bridges	0.6330	9.35
m4R	0.1408	3.71	p2Y	-0.1906	3.98	Beta Hairpin	0.8016	13.87
m4V	-0.0560	2.91	p3N	-0.1483	5.13	Beta Hairpin Strand	-0.0916	5.34
m5D	0.2333	6.89	p3W	-0.1152	3.37	Phi angle	-0.0003	3.04
m5F	-0.0941	2.85	p4A	0.1650	4.77
m5G	0.0560	1.75	p4E	0.1711	6.75
m5I	-0.0872	2.61	p4P	0.1946	8.26
m5Y	-0.1326	4.60	p5C	-0.1742	5.23
m6E	0.0916	3.22	p5E	0.1512	4.93
m6H	-0.1270	5.34	p5H	0.2173	4.08
m6V	0.1567	3.99	p5I	-0.0783	3.52
m6W	0.1063	2.81	p5Q	0.1209	3.16
m6Y	-0.1267	4.11	p5T	0.0651	2.79
m7A	0.1622	5.51	p5Y	-0.1818	4.60
m7E	-0.1072	4.70	p6F	-0.0883	1.97
m7G	-0.0703	3.42	p6G	-0.1561	5.14
m7H	0.2640	5.12	p6M	0.3066	4.87
m7I	0.1141	5.62	p6N	-0.1144	5.29
m7K	0.1366	3.55	p6Q	-0.2447	7.92
m7S	0.0859	2.72	p7A	0.1803	3.10
m8G	0.0964	4.51	p7C	-0.1207	3.99
m8L	-0.1518	5.28	p7E	-0.1148	4.67
m8M	-0.1187	4.49	p7G	-0.0945	3.50

^a miα and piα are abbreviations for minus_iα and plus_iα, respectively. Note that the coefficient standard error is |β|÷|t|

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