Ranking near-native candidate protein structures via random forest classification

Wu, Hongjie; Huang, Hongmei; Lu, Weizhong; Fu, Qiming; Ding, Yijie; Qiu, Jing; Li, Haiou

doi:10.1186/s12859-019-3257-8

BMC Bioinformatics

Table 3 RMSD comparison of the first model of 43 proteins

From: Ranking near-native candidate protein structures via random forest classification

PDB	Len^a	Best^b	SPICKER		Calibur		Durandal
PDB	Len^a	Best^b	Original^c	RF_SPICKER^d	Original^c	RF_Calibur^e	Original^c	RF_Durandal^f
1abv_	103	4.81	13.93	*8.08*	13.17	13.17	13.57	*12.31*
1af7__	72	2.92	5.73	5.73	4.45	4.45	10.28	*3.99*
1ah9_	63	1.88	4.66	4.66	3.31	*2.81*	3.02	3.02
1b4bA	71	4.20	7.18	*5.08*	5.57	5.57	5.54	5.54
1b72A	49	2.36	*4.07*	5.08	*3.23*	3.73	3.23	3.23
1bm8_	99	6.67	7.18	7.18	7.07	7.07	7.48	7.48
1bq9A	53	3.98	7.39	*5.04*	8.18	*6.42*	8.36	8.36
1cewI	108	3.20	3.92	3.92	12.49	*4.28*	3.75	3.75
1cqkA	101	1.40	2.78	2.78	*1.69*	1.95	2.37	2.37
1dcjA_	73	9.31	11.66	*10.45*	12.18	*11.66*	11.97	*9.96*
1di2A_	69	1.32	2.49	2.49	2.62	*2.19*	2.49	2.49
1dtjA_	74	1.58	*2.54*	3.22	2.83	*1.88*	1.88	1.88
1egxA	115	1.93	2.31	2.31	*2.59*	2.95	2.59	2.59
1g1cA	98	2.16	2.97	2.97	2.65	*2.59*	2.49	2.49
1gjxA	77	5.01	7.30	*5.58*	14.09	*13.23*	8.09	8.09
1gnuA	117	4.06	7.09	*6.78*	9.15	*7.74*	9.54	9.54
1gpt_	47	2.79	5.52	5.53	6.29	*4.64*	3.68	3.68
1gyvA	117	2.69	3.78	3.78	*3.41*	3.63	3.39	3.39
1hbkA	89	2.69	3.57	3.57	3.48	*3.52*	3.48	3.48
1itpA	68	4.10	11.23	*8.04*	10.92	*8.07*	11.48	11.48
1jnuA	104	2.30	3.45	3.45	*2.68*	3.21	2.76	2.76
1kjs_	74	4.65	8.67	*5.88*	8.44	*5.89*	8.75	*5.92*
1mkyA3	81	3.68	5.16	5.16	5.54	*5.33*	5.49	5.49
1mla_2	70	2.04	3.18	3.18	2.82	*2.98*	3.38	3.38
1mn8A	84	5.14	6.69	6.69	7.45	7.45	10.38	10.38
1n0uA4	69	3.14	4.59	4.59	4.62	*4.37*	4.28	4.28
1ne3A	56	3.16	*5.12*	6.63	6.09	*4.05*	5.96	5.96
1no5A	93	6.12	10.82	*10.56*	10.69	*10.54*	11	11
1npsA	88	1.81	3.07	3.07	*2.28*	2.74	*6.07*	8.29
1o2fB_	77	4.08	7.41	*7.12*	9.03	*6.80*	3.91	3.91
1ogwA_	77	0.96	1.81	1.81	*1.29*	1.34	2.43	3.00
1pgx_	59	2.79	3.42	3.42	*3.26*	4.19	3.26	3.26
1r69_	61	1.30	2.28	2.28	*1.97*	2.14	1.99	1.99
1shfA	59	1.18	2.86	2.86	*1.49*	2.75	1.29	1.29
1sro_	71	2.59	3.54	*3.00*	*3.54*	3.89	3.54	3.54
1tfi_	47	2.49	*4.61*	5.72	5.08	5.08	4.48	4.48
1thx_	108	1.71	2.67	2.67	*2.26*	2.27	2.10	2.10
1tif_	59	6.47	7.45	7.45	7.57	7.57	*7.62*	9.44
1tig_	88	3.00	9.12	*6.11*	3.58	3.58	4.25	4.25
1vcc_	76	4.52	6.53	6.53	8.13	*6.43*	7.46	7.46
256bA	106	2.75	3.20	3.20	*5.93*	6.23	3.73	*2.78*
2pcy_	99	3.87	5.46	5.46	*1.94*	2.12	4.71	4.71
2a0b_	118	2.05	2.20	2.20	3.48	*2.98*	2.75	2.75
Average	81.09	3.28	5.36	*4.91*	5.53	*4.99*	5.36	*5.15*

^a:Length of protein sequence
^b:RMSD between the best model in the decoy and native
^c:RMSD of the first model predicted by SPICKER,Calibur and Durandal
^d:RMSD of the first model predicted by the random forest classification from SPICKER results
^e:RMSD of the first model predicted by the random forest classification from Calibur results
^f:RMSD of the first model predicted by the random forest classification from Durandal result
The RMSD in bold and italic indicates RF(RF_SPICKER, RF_Calibur and RF_Durandal) methods obtain lower RMSD than their original methods

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