
PFRMAT RR
TARGET T0104
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
MESLTQYIPD EFSMLRFGKK FAEILLKLHT EKAIMVYLNG DLGAGKTTLT RGMLQGIGHQ 
GNVKSPTYTL VEEYNIAGKM IYHFDLYRLA DPEELEFMGI RDYFNTDSIC LIEWSEKGQG 
ILPEADILVN IDYYDDARNI ELIAQTNLGK NIISAFSN 
38	70	0	8	0.888
87	133	0	8	0.881
38	111	0	8	0.874
36	70	0	8	0.874
38	114	0	8	0.868
38	109	0	8	0.865
70	111	0	8	0.861
36	111	0	8	0.859
38	131	0	8	0.856
38	144	0	8	0.855
35	70	0	8	0.849
68	83	0	8	0.847
87	131	0	8	0.843
38	81	0	8	0.841
38	83	0	8	0.833
67	132	0	8	0.83
37	70	0	8	0.83
70	142	0	8	0.829
83	132	0	8	0.828
82	109	0	8	0.828
70	81	0	8	0.827
37	108	0	8	0.827
71	81	0	8	0.825
74	83	0	8	0.825
37	110	0	8	0.824
83	110	0	8	0.824
38	142	0	8	0.824
70	128	0	8	0.822
36	110	0	8	0.821
87	144	0	8	0.82
70	83	0	8	0.82
89	144	0	8	0.818
36	112	0	8	0.817
81	109	0	8	0.817
38	71	0	8	0.817
36	109	0	8	0.815
70	108	0	8	0.814
38	129	0	8	0.813
35	83	0	8	0.813
83	111	0	8	0.812
35	110	0	8	0.81
70	131	0	8	0.808
87	127	0	8	0.808
36	82	0	8	0.808
109	131	0	8	0.806
70	114	0	8	0.806
35	111	0	8	0.804
70	144	0	8	0.804
35	81	0	8	0.804
70	80	0	8	0.801
68	110	0	8	0.801
70	109	0	8	0.799
67	83	0	8	0.798
69	108	0	8	0.798
71	109	0	8	0.797
37	71	0	8	0.797
70	129	0	8	0.797
38	82	0	8	0.796
71	110	0	8	0.796
82	108	0	8	0.795
38	127	0	8	0.794
87	114	0	8	0.794
81	110	0	8	0.793
68	111	0	8	0.792
74	81	0	8	0.791
54	70	0	8	0.791
36	71	0	8	0.791
68	81	0	8	0.791
81	111	0	8	0.79
38	68	0	8	0.79
83	126	0	8	0.79
88	126	0	8	0.79
87	108	0	8	0.789
144	155	0	8	0.789
33	114	0	8	0.789
35	68	0	8	0.788
83	114	0	8	0.788
33	70	0	8	0.787
37	81	0	8	0.786
37	68	0	8	0.786
70	82	0	8	0.786
70	110	0	8	0.785
87	109	0	8	0.784
70	112	0	8	0.784
38	108	0	8	0.784
36	68	0	8	0.783
69	110	0	8	0.783
37	83	0	8	0.783
49	70	0	8	0.782
81	131	0	8	0.782
87	125	0	8	0.781
38	84	0	8	0.781
38	110	0	8	0.78
35	109	0	8	0.779
37	80	0	8	0.777
37	109	0	8	0.776
36	131	0	8	0.776
36	83	0	8	0.775
69	83	0	8	0.775
36	80	0	8	0.774
35	112	0	8	0.774
80	110	0	8	0.773
37	112	0	8	0.773
36	84	0	8	0.771
70	130	0	8	0.77
37	111	0	8	0.769
38	112	0	8	0.769
87	97	0	8	0.768
110	130	0	8	0.768
89	142	0	8	0.768
33	131	0	8	0.767
38	74	0	8	0.767
81	112	0	8	0.765
35	71	0	8	0.764
36	81	0	8	0.764
112	131	0	8	0.762
35	80	0	8	0.762
70	84	0	8	0.76
68	108	0	8	0.759
68	112	0	8	0.759
38	86	0	8	0.758
38	89	0	8	0.758
109	129	0	8	0.757
37	82	0	8	0.757
84	109	0	8	0.756
67	126	0	8	0.755
74	85	0	8	0.755
71	80	0	8	0.755
83	109	0	8	0.754
113	130	0	8	0.754
111	142	0	8	0.753
33	68	0	8	0.752
80	109	0	8	0.752
87	132	0	8	0.752
69	81	0	8	0.752
68	109	0	8	0.752
68	80	0	8	0.751
84	108	0	8	0.75
69	109	0	8	0.747
87	142	0	8	0.747
74	109	0	8	0.747
83	112	0	8	0.746
68	82	0	8	0.746
71	111	0	8	0.746
83	144	0	8	0.746
38	80	0	8	0.746
86	109	0	8	0.742
33	142	0	8	0.74
89	133	0	8	0.739
26	114	0	8	0.739
34	110	0	8	0.738
36	129	0	8	0.737
89	131	0	8	0.737
38	87	0	8	0.737
42	121	0	8	0.736
45	133	0	8	0.735
82	110	0	8	0.734
47	83	0	8	0.733
33	74	0	8	0.733
83	113	0	8	0.733
33	83	0	8	0.733
83	131	0	8	0.732
69	80	0	8	0.732
68	85	0	8	0.732
44	86	0	8	0.731
35	82	0	8	0.731
71	84	0	8	0.731
38	133	0	8	0.73
87	110	0	8	0.73
47	67	0	8	0.729
85	108	0	8	0.728
85	133	0	8	0.728
87	107	0	8	0.727
36	74	0	8	0.727
67	115	0	8	0.726
88	107	0	8	0.726
71	82	0	8	0.725
35	72	0	8	0.725
34	128	0	8	0.725
89	127	0	8	0.725
71	83	0	8	0.725
33	111	0	8	0.724
114	142	0	8	0.724
83	108	0	8	0.724
33	127	0	8	0.723
70	89	0	8	0.723
71	112	0	8	0.722
45	114	0	8	0.721
46	132	0	8	0.721
38	69	0	8	0.721
89	97	0	8	0.721
71	108	0	8	0.721
72	83	0	8	0.721
37	69	0	8	0.72
88	132	0	8	0.719
37	127	0	8	0.719
131	144	0	8	0.718
81	108	0	8	0.718
37	84	0	8	0.717
35	74	0	8	0.717
69	111	0	8	0.717
38	140	0	8	0.716
33	133	0	8	0.716
36	69	0	8	0.715
68	131	0	8	0.715
35	128	0	8	0.715
70	127	0	8	0.713
70	87	0	8	0.713
66	133	0	8	0.713
35	130	0	8	0.711
67	82	0	8	0.71
89	114	0	8	0.709
86	108	0	8	0.709
87	156	0	8	0.709
113	129	0	8	0.708
69	84	0	8	0.707
88	133	0	8	0.707
111	131	0	8	0.707
34	131	0	8	0.707
38	67	0	8	0.707
36	113	0	8	0.706
71	89	0	8	0.705
80	108	0	8	0.704
33	86	0	8	0.704
47	66	0	8	0.704
71	131	0	8	0.704
110	127	0	8	0.703
82	132	0	8	0.702
57	68	0	8	0.702
114	144	0	8	0.702
89	108	0	8	0.702
68	84	0	8	0.701
127	142	0	8	0.701
26	42	0	8	0.701
111	128	0	8	0.701
36	127	0	8	0.701
74	108	0	8	0.701
40	67	0	8	0.701
73	80	0	8	0.7
71	86	0	8	0.7
69	127	0	8	0.698
71	113	0	8	0.698
69	82	0	8	0.698
70	86	0	8	0.698
87	126	0	8	0.697
69	85	0	8	0.696
72	81	0	8	0.695
81	127	0	8	0.695
89	122	0	8	0.695
33	69	0	8	0.694
34	82	0	8	0.693
34	70	0	8	0.693
28	38	0	8	0.692
44	122	0	8	0.692
87	112	0	8	0.691
36	108	0	8	0.691
33	81	0	8	0.691
89	111	0	8	0.691
49	68	0	8	0.691
87	111	0	8	0.691
35	131	0	8	0.69
68	129	0	8	0.69
33	80	0	8	0.69
37	128	0	8	0.689
37	130	0	8	0.689
112	127	0	8	0.688
37	72	0	8	0.688
57	86	0	8	0.688
67	80	0	8	0.687
33	71	0	8	0.686
37	131	0	8	0.686
67	131	0	8	0.686
67	133	0	8	0.686
33	110	0	8	0.686
38	128	0	8	0.686
57	89	0	8	0.686
33	84	0	8	0.686
44	131	0	8	0.685
54	89	0	8	0.685
83	133	0	8	0.684
84	131	0	8	0.684
44	121	0	8	0.684
110	131	0	8	0.683
82	111	0	8	0.683
83	128	0	8	0.683
84	110	0	8	0.683
44	133	0	8	0.683
81	113	0	8	0.683
57	133	0	8	0.682
33	132	0	8	0.682
37	85	0	8	0.682
87	123	0	8	0.682
85	126	0	8	0.682
57	87	0	8	0.681
33	109	0	8	0.681
82	133	0	8	0.681
85	132	0	8	0.68
34	81	0	8	0.68
80	112	0	8	0.68
67	85	0	8	0.68
35	69	0	8	0.68
83	107	0	8	0.679
131	142	0	8	0.679
42	86	0	8	0.678
67	113	0	8	0.678
38	73	0	8	0.678
81	114	0	8	0.677
80	142	0	8	0.676
36	128	0	8	0.676
36	67	0	8	0.676
45	86	0	8	0.676
68	87	0	8	0.675
54	109	0	8	0.675
36	87	0	8	0.674
72	80	0	8	0.674
34	113	0	8	0.674
END

