

PFRMAT RR
TARGET T0114
AUTHOR 7875-6238-4020
METHOD Prediction Made with CORNET: a Neural Network based method trained using Correlated Mutations, 
METHOD Sequence Conservation, Predicted Secondary structure of Proteins and Evolutionary Information. 
METHOD Authors Fariselli Piero, Olmea Osvaldo, Valencia Alfonso, Rita Casadio. 
METHOD This method is an extension of the two following methods 
METHOD Olmea O. and Valencia A. (1997) Fold. Des. 2: S25-32. 
METHOD Fariselli P and Casadio R. (1999) Prot. Engng 12:15-21 
MODEL  1
MINRTDCNEN SYLEIHNNEG RDTLCFANAG TMPVAIYGVN WVESGNNVVT LQFQRNLSDP
RLETITLQKW GSWNPGHIHE ILSIRIY
15	86	0	8	0.944
24	65	0	8	0.941
15	84	0	8	0.938
13	86	0	8	0.936
15	36	0	8	0.932
35	81	0	8	0.93
24	51	0	8	0.929
24	67	0	8	0.928
51	65	0	8	0.926
42	86	0	8	0.92
49	65	0	8	0.915
78	86	0	8	0.914
62	86	0	8	0.913
15	24	0	8	0.912
13	82	0	8	0.912
35	82	0	8	0.906
34	82	0	8	0.904
68	81	0	8	0.904
13	36	0	8	0.904
34	42	0	8	0.904
26	39	0	8	0.904
13	48	0	8	0.903
34	86	0	8	0.902
52	66	0	8	0.901
24	82	0	8	0.901
24	39	0	8	0.9
35	65	0	8	0.895
27	81	0	8	0.894
35	48	0	8	0.894
27	82	0	8	0.894
36	81	0	8	0.893
13	41	0	8	0.893
35	49	0	8	0.893
27	65	0	8	0.891
39	81	0	8	0.89
25	81	0	8	0.888
27	42	0	8	0.888
25	66	0	8	0.887
25	50	0	8	0.887
15	65	0	8	0.887
26	82	0	8	0.887
51	67	0	8	0.886
36	78	0	8	0.886
27	67	0	8	0.886
40	54	0	8	0.885
13	62	0	8	0.884
27	78	0	8	0.884
54	64	0	8	0.883
16	33	0	8	0.883
67	82	0	8	0.883
36	48	0	8	0.882
25	36	0	8	0.881
42	62	0	8	0.881
39	48	0	8	0.881
14	85	0	8	0.88
27	51	0	8	0.88
25	65	0	8	0.88
35	64	0	8	0.88
25	51	0	8	0.88
26	35	0	8	0.879
62	82	0	8	0.879
25	40	0	8	0.879
24	48	0	8	0.879
15	81	0	8	0.878
36	84	0	8	0.878
43	69	0	8	0.875
27	86	0	8	0.875
14	66	0	8	0.875
26	86	0	8	0.874
36	86	0	8	0.874
25	67	0	8	0.874
27	84	0	8	0.873
27	62	0	8	0.872
51	82	0	8	0.872
66	83	0	8	0.872
65	82	0	8	0.871
53	84	0	8	0.87
25	35	0	8	0.868
36	65	0	8	0.868
42	82	0	8	0.868
37	65	0	8	0.867
24	35	0	8	0.867
23	54	0	8	0.867
24	86	0	8	0.867
35	85	0	8	0.867
27	34	0	8	0.865
15	67	0	8	0.865
26	67	0	8	0.864
50	83	0	8	0.863
49	64	0	8	0.863
37	85	0	8	0.863
26	51	0	8	0.863
26	42	0	8	0.863
24	41	0	8	0.863
67	84	0	8	0.862
26	64	0	8	0.862
13	35	0	8	0.862
36	51	0	8	0.862
25	41	0	8	0.862
13	24	0	8	0.862
25	52	0	8	0.862
25	86	0	8	0.861
23	68	0	8	0.86
79	86	0	8	0.86
15	51	0	8	0.86
34	53	0	8	0.859
42	81	0	8	0.859
35	66	0	8	0.859
50	64	0	8	0.858
35	84	0	8	0.857
27	49	0	8	0.857
65	84	0	8	0.857
67	81	0	8	0.857
27	48	0	8	0.856
15	48	0	8	0.856
34	85	0	8	0.856
50	66	0	8	0.855
13	37	0	8	0.855
36	64	0	8	0.855
15	32	0	8	0.854
38	50	0	8	0.854
49	84	0	8	0.852
25	42	0	8	0.851
13	26	0	8	0.851
16	32	0	8	0.851
24	49	0	8	0.85
13	27	0	8	0.85
15	39	0	8	0.85
26	81	0	8	0.849
42	84	0	8	0.849
15	82	0	8	0.848
25	48	0	8	0.848
13	70	0	8	0.847
16	25	0	8	0.847
39	84	0	8	0.847
63	85	0	8	0.846
51	84	0	8	0.845
35	83	0	8	0.845
48	86	0	8	0.845
64	85	0	8	0.845
27	53	0	8	0.844
16	24	0	8	0.844
25	37	0	8	0.842
34	49	0	8	0.842
25	34	0	8	0.842
26	34	0	8	0.842
62	81	0	8	0.842
37	82	0	8	0.842
39	49	0	8	0.842
39	78	0	8	0.841
27	39	0	8	0.841
26	84	0	8	0.84
53	81	0	8	0.84
26	53	0	8	0.839
15	41	0	8	0.839
36	67	0	8	0.839
24	32	0	8	0.839
37	52	0	8	0.839
42	66	0	8	0.838
26	36	0	8	0.838
24	84	0	8	0.838
41	86	0	8	0.838
15	27	0	8	0.838
36	66	0	8	0.838
23	66	0	8	0.837
13	42	0	8	0.837
38	64	0	8	0.837
39	65	0	8	0.837
66	85	0	8	0.837
25	38	0	8	0.837
26	83	0	8	0.837
14	23	0	8	0.837
34	48	0	8	0.836
40	83	0	8	0.836
END



