
PFRMAT RR
TARGET T0110
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
MAREFKRSDRVAQEIQKEIAVILQREVKDPRIGMVTVSDVEVSSDLSYAKIFVTFLFDHD 
EMAIEQGMKGLEKASPYIRSLLGKAMRLRIVPEIRFIYDQSLVEGMRMSNLVTNVVREDE 
KKHVEESN
40	49	0	8	0.864
42	96	0	8	0.862
42	88	0	8	0.861
49	96	0	8	0.851
37	96	0	8	0.851
49	94	0	8	0.849
49	88	0	8	0.84
36	54	0	8	0.84
53	96	0	8	0.829
41	50	0	8	0.826
46	88	0	8	0.821
88	96	0	8	0.82
40	88	0	8	0.819
52	96	0	8	0.818
40	51	0	8	0.818
51	94	0	8	0.818
51	96	0	8	0.81
37	53	0	8	0.809
53	88	0	8	0.805
42	94	0	8	0.802
40	52	0	8	0.801
42	51	0	8	0.801
37	49	0	8	0.8
49	97	0	8	0.799
42	49	0	8	0.795
36	50	0	8	0.793
35	96	0	8	0.792
53	94	0	8	0.792
37	94	0	8	0.791
36	52	0	8	0.79
40	96	0	8	0.789
34	52	0	8	0.789
40	53	0	8	0.787
37	51	0	8	0.783
32	46	0	8	0.782
42	53	0	8	0.777
35	42	0	8	0.777
35	53	0	8	0.777
52	88	0	8	0.776
41	52	0	8	0.776
56	88	0	8	0.773
89	96	0	8	0.773
46	90	0	8	0.773
51	97	0	8	0.772
39	89	0	8	0.771
35	94	0	8	0.769
41	89	0	8	0.767
35	49	0	8	0.765
54	89	0	8	0.761
34	42	0	8	0.761
88	97	0	8	0.76
33	52	0	8	0.759
56	96	0	8	0.759
41	54	0	8	0.758
54	95	0	8	0.755
37	55	0	8	0.755
37	88	0	8	0.755
37	52	0	8	0.754
38	54	0	8	0.752
39	50	0	8	0.75
55	96	0	8	0.75
36	95	0	8	0.749
35	88	0	8	0.748
33	49	0	8	0.748
34	96	0	8	0.746
35	51	0	8	0.745
89	98	0	8	0.745
33	54	0	8	0.744
52	94	0	8	0.744
55	88	0	8	0.742
50	93	0	8	0.742
48	89	0	8	0.74
33	89	0	8	0.74
89	99	0	8	0.739
52	89	0	8	0.738
40	94	0	8	0.736
36	89	0	8	0.735
34	49	0	8	0.733
41	95	0	8	0.728
55	89	0	8	0.727
36	96	0	8	0.726
32	49	0	8	0.726
52	95	0	8	0.725
40	55	0	8	0.723
33	88	0	8	0.723
53	97	0	8	0.722
43	89	0	8	0.722
54	96	0	8	0.721
34	51	0	8	0.721
34	89	0	8	0.72
40	90	0	8	0.72
35	52	0	8	0.719
43	92	0	8	0.719
34	55	0	8	0.716
32	40	0	8	0.714
88	98	0	8	0.714
49	90	0	8	0.714
34	88	0	8	0.713
43	87	0	8	0.713
42	55	0	8	0.71
38	52	0	8	0.709
32	94	0	8	0.709
49	89	0	8	0.708
38	88	0	8	0.708
51	88	0	8	0.708
56	94	0	8	0.708
33	96	0	8	0.706
34	97	0	8	0.706
33	95	0	8	0.706
35	55	0	8	0.705
38	89	0	8	0.703
38	96	0	8	0.702
43	54	0	8	0.7
45	89	0	8	0.698
32	88	0	8	0.698
52	97	0	8	0.697
38	50	0	8	0.695
49	56	0	8	0.694
40	89	0	8	0.694
34	53	0	8	0.693
36	51	0	8	0.692
56	92	0	8	0.691
49	98	0	8	0.69
53	89	0	8	0.687
54	93	0	8	0.686
87	98	0	8	0.685
42	89	0	8	0.684
33	43	0	8	0.683
54	88	0	8	0.682
32	98	0	8	0.682
46	89	0	8	0.681
54	97	0	8	0.681
37	97	0	8	0.681
50	95	0	8	0.681
32	56	0	8	0.679
43	52	0	8	0.679
56	90	0	8	0.679
36	55	0	8	0.678
33	55	0	8	0.677
42	52	0	8	0.677
86	94	0	8	0.675
46	96	0	8	0.673
35	97	0	8	0.673
32	42	0	8	0.671
55	94	0	8	0.67
47	92	0	8	0.669
35	48	0	8	0.666
38	49	0	8	0.665
33	47	0	8	0.664
36	94	0	8	0.663
52	90	0	8	0.663
46	55	0	8	0.662
33	41	0	8	0.662
48	98	0	8	0.661
48	96	0	8	0.661
32	53	0	8	0.661
51	95	0	8	0.661
32	97	0	8	0.66
38	95	0	8	0.66
33	97	0	8	0.66
55	90	0	8	0.659
34	94	0	8	0.659
48	97	0	8	0.659
49	95	0	8	0.658
50	97	0	8	0.657
33	51	0	8	0.657
56	89	0	8	0.657
33	98	0	8	0.656
89	97	0	8	0.656
47	88	0	8	0.656
33	40	0	8	0.656
55	97	0	8	0.656
36	97	0	8	0.655
32	91	0	8	0.654
39	54	0	8	0.654
36	88	0	8	0.654
42	92	0	8	0.653
86	96	0	8	0.653
40	97	0	8	0.653
36	43	0	8	0.652
42	86	0	8	0.651
33	50	0	8	0.651
34	98	0	8	0.65
32	96	0	8	0.65
38	90	0	8	0.649
39	95	0	8	0.649
52	98	0	8	0.648
47	89	0	8	0.648
38	47	0	8	0.647
42	90	0	8	0.647
56	97	0	8	0.647
48	90	0	8	0.646
39	52	0	8	0.645
50	94	0	8	0.645
35	54	0	8	0.645
42	97	0	8	0.644
34	54	0	8	0.644
52	92	0	8	0.643
50	98	0	8	0.643
37	89	0	8	0.642
54	94	0	8	0.641
40	48	0	8	0.641
35	50	0	8	0.641
33	56	0	8	0.641
32	90	0	8	0.64
34	95	0	8	0.64
36	53	0	8	0.639
40	50	0	8	0.639
38	51	0	8	0.639
48	94	0	8	0.638
31	45	0	8	0.638
37	48	0	8	0.638
37	95	0	8	0.638
41	96	0	8	0.637
33	87	0	8	0.637
36	49	0	8	0.636
35	98	0	8	0.636
43	88	0	8	0.635
46	53	0	8	0.635
34	50	0	8	0.634
37	90	0	8	0.634
48	88	0	8	0.633
32	55	0	8	0.632
47	96	0	8	0.632
48	91	0	8	0.631
43	93	0	8	0.631
38	48	0	8	0.63
53	91	0	8	0.629
35	95	0	8	0.628
48	95	0	8	0.627
36	48	0	8	0.627
86	97	0	8	0.627
50	96	0	8	0.626
33	42	0	8	0.625
43	96	0	8	0.625
33	44	0	8	0.625
33	90	0	8	0.624
34	90	0	8	0.623
47	90	0	8	0.623
47	98	0	8	0.622
54	90	0	8	0.622
40	95	0	8	0.621
38	55	0	8	0.621
37	54	0	8	0.621
53	95	0	8	0.621
50	88	0	8	0.62
40	56	0	8	0.62
38	97	0	8	0.619
56	91	0	8	0.619
39	96	0	8	0.619
36	93	0	8	0.619
38	93	0	8	0.618
32	51	0	8	0.618
38	94	0	8	0.617
55	95	0	8	0.617
41	97	0	8	0.617
END

