
PFRMAT RR
TARGET T0118
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
MAGYGAKGIR KVGAFRSGLE DKVSKQLESK GIKFEYEEWK VPYVIPASNH TYTPDFLLPN
GIFVETKGLW ESDDRKKHLL IREQHPELDI RIVFSSSRTK LYKGSPTSYG EFCEKHGIKF
ADKLIPAEWI KEPKKEVPFD RLKRKGGKK
2	18	0	8	0.982
81	88	0	8	0.932
15	88	0	8	0.917
44	63	0	8	0.914
80	88	0	8	0.907
45	90	0	8	0.901
36	101	0	8	0.899
45	80	0	8	0.894
39	101	0	8	0.894
88	101	0	8	0.893
32	90	0	8	0.89
81	90	0	8	0.888
44	93	0	8	0.887
33	65	0	8	0.886
45	62	0	8	0.881
81	101	0	8	0.88
44	62	0	8	0.879
44	101	0	8	0.874
14	62	0	8	0.871
34	101	0	8	0.871
80	90	0	8	0.87
44	90	0	8	0.868
79	88	0	8	0.868
34	44	0	8	0.867
45	81	0	8	0.867
66	101	0	8	0.865
45	101	0	8	0.864
45	63	0	8	0.863
43	62	0	8	0.863
44	66	0	8	0.863
15	79	0	8	0.862
36	63	0	8	0.862
63	93	0	8	0.861
45	79	0	8	0.858
43	101	0	8	0.856
88	124	0	8	0.856
32	101	0	8	0.854
44	79	0	8	0.854
65	91	0	8	0.853
63	94	0	8	0.853
66	90	0	8	0.853
35	91	0	8	0.851
36	92	0	8	0.851
32	39	0	8	0.851
15	80	0	8	0.85
43	93	0	8	0.85
44	91	0	8	0.849
43	63	0	8	0.848
45	88	0	8	0.848
70	101	0	8	0.847
63	91	0	8	0.846
79	101	0	8	0.846
15	137	0	8	0.844
14	57	0	8	0.844
63	92	0	8	0.844
39	90	0	8	0.844
15	56	0	8	0.843
36	91	0	8	0.842
14	69	0	8	0.841
80	101	0	8	0.841
15	44	0	8	0.84
80	93	0	8	0.84
90	101	0	8	0.84
36	102	0	8	0.837
44	92	0	8	0.836
34	93	0	8	0.836
79	90	0	8	0.834
80	137	0	8	0.833
62	101	0	8	0.832
32	45	0	8	0.832
42	63	0	8	0.832
14	45	0	8	0.831
89	100	0	8	0.831
32	88	0	8	0.831
43	80	0	8	0.83
64	101	0	8	0.83
44	137	0	8	0.83
15	69	0	8	0.829
90	102	0	8	0.828
65	103	0	8	0.828
79	121	0	8	0.828
66	102	0	8	0.828
69	79	0	8	0.827
32	66	0	8	0.826
14	32	0	8	0.826
33	89	0	8	0.825
33	43	0	8	0.825
43	94	0	8	0.825
14	79	0	8	0.824
6	56	0	8	0.822
57	69	0	8	0.822
63	90	0	8	0.821
34	90	0	8	0.821
65	100	0	8	0.82
6	88	0	8	0.819
37	67	0	8	0.818
45	93	0	8	0.818
82	89	0	8	0.818
44	94	0	8	0.817
15	39	0	8	0.817
34	63	0	8	0.816
14	101	0	8	0.816
37	91	0	8	0.815
37	100	0	8	0.813
43	91	0	8	0.813
14	88	0	8	0.813
79	137	0	8	0.812
42	102	0	8	0.812
27	101	0	8	0.811
15	66	0	8	0.811
36	44	0	8	0.807
43	64	0	8	0.807
33	44	0	8	0.807
124	137	0	8	0.806
69	101	0	8	0.806
64	92	0	8	0.806
36	43	0	8	0.806
64	94	0	8	0.806
81	93	0	8	0.805
14	80	0	8	0.805
15	101	0	8	0.805
42	66	0	8	0.804
6	101	0	8	0.803
43	89	0	8	0.803
62	69	0	8	0.803
39	56	0	8	0.801
36	94	0	8	0.8
45	57	0	8	0.8
80	94	0	8	0.799
32	102	0	8	0.797
58	69	0	8	0.796
15	45	0	8	0.796
37	101	0	8	0.796
63	88	0	8	0.796
19	101	0	8	0.796
27	79	0	8	0.795
36	93	0	8	0.795
29	66	0	8	0.794
14	81	0	8	0.793
34	66	0	8	0.793
14	44	0	8	0.793
66	89	0	8	0.793
64	93	0	8	0.793
81	137	0	8	0.792
70	78	0	8	0.792
16	79	0	8	0.792
32	56	0	8	0.792
34	92	0	8	0.791
39	81	0	8	0.79
67	89	0	8	0.79
64	91	0	8	0.788
2	88	0	8	0.788
6	137	0	8	0.788
57	79	0	8	0.788
44	64	0	8	0.787
34	94	0	8	0.787
39	102	0	8	0.787
67	102	0	8	0.786
93	101	0	8	0.786
34	43	0	8	0.786
65	95	0	8	0.786
44	88	0	8	0.786
57	101	0	8	0.785
39	80	0	8	0.785
64	90	0	8	0.785
39	88	0	8	0.785
14	93	0	8	0.784
39	66	0	8	0.784
62	93	0	8	0.784
37	103	0	8	0.784
41	88	0	8	0.784
81	94	0	8	0.784
6	79	0	8	0.784
81	102	0	8	0.783
32	81	0	8	0.782
43	92	0	8	0.782
42	91	0	8	0.78
69	81	0	8	0.78
65	90	0	8	0.78
45	69	0	8	0.78
33	93	0	8	0.779
79	94	0	8	0.778
15	57	0	8	0.778
62	102	0	8	0.777
16	89	0	8	0.776
63	80	0	8	0.776
32	64	0	8	0.776
62	90	0	8	0.775
83	103	0	8	0.774
39	94	0	8	0.774
63	101	0	8	0.774
34	64	0	8	0.773
34	102	0	8	0.772
88	104	0	8	0.771
62	81	0	8	0.771
80	91	0	8	0.77
79	95	0	8	0.77
32	44	0	8	0.77
65	93	0	8	0.77
43	61	0	8	0.769
14	43	0	8	0.769
23	79	0	8	0.769
66	81	0	8	0.769
42	90	0	8	0.769
57	90	0	8	0.768
57	137	0	8	0.768
45	92	0	8	0.767
45	64	0	8	0.767
36	90	0	8	0.766
57	125	0	8	0.766
19	32	0	8	0.765
39	68	0	8	0.765
34	62	0	8	0.763
43	138	0	8	0.761
63	89	0	8	0.761
39	62	0	8	0.76
52	67	0	8	0.76
94	101	0	8	0.76
36	45	0	8	0.76
79	125	0	8	0.76
45	94	0	8	0.76
44	57	0	8	0.759
39	91	0	8	0.759
57	81	0	8	0.759
56	79	0	8	0.758
62	80	0	8	0.758
88	102	0	8	0.757
39	78	0	8	0.757
6	15	0	8	0.756
46	63	0	8	0.755
39	63	0	8	0.755
6	90	0	8	0.755
15	72	0	8	0.755
58	79	0	8	0.754
31	66	0	8	0.754
42	93	0	8	0.754
70	104	0	8	0.754
15	36	0	8	0.753
37	66	0	8	0.753
15	94	0	8	0.751
82	102	0	8	0.75
79	93	0	8	0.749
36	66	0	8	0.748
79	92	0	8	0.748
14	58	0	8	0.747
10	89	0	8	0.747
33	63	0	8	0.747
89	98	0	8	0.747
6	57	0	8	0.746
58	88	0	8	0.746
72	104	0	8	0.746
56	69	0	8	0.745
14	63	0	8	0.745
36	62	0	8	0.745
52	62	0	8	0.745
46	102	0	8	0.745
32	80	0	8	0.744
41	62	0	8	0.744
43	67	0	8	0.744
44	81	0	8	0.743
82	101	0	8	0.743
43	102	0	8	0.743
44	80	0	8	0.743
42	101	0	8	0.742
36	78	0	8	0.742
33	45	0	8	0.742
69	88	0	8	0.742
45	102	0	8	0.741
6	80	0	8	0.741
23	88	0	8	0.741
44	56	0	8	0.741
19	79	0	8	0.74
45	56	0	8	0.74
62	79	0	8	0.739
63	102	0	8	0.738
15	63	0	8	0.738
89	103	0	8	0.737
28	100	0	8	0.737
37	93	0	8	0.737
112	124	0	8	0.736
15	70	0	8	0.736
58	81	0	8	0.735
14	109	0	8	0.735
70	79	0	8	0.735
14	52	0	8	0.735
41	103	0	8	0.734
79	139	0	8	0.733
80	102	0	8	0.733
53	68	0	8	0.733
45	91	0	8	0.733
16	52	0	8	0.732
69	80	0	8	0.732
14	39	0	8	0.732
80	118	0	8	0.732
6	45	0	8	0.732
69	90	0	8	0.732
80	139	0	8	0.732
42	95	0	8	0.731
END

