
PFRMAT RR
TARGET T0105
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
DENINFKQSELPVTCGEVKGTLYKERFKQG 
TSKKCIQSEDKKWFTPREFEIEGDRGASKN 
WKLSIRCGGYTLKVLMENKFLPEPPSTRKKVTIK
600	607	0	8	0.793
600	608	0	8	0.781
600	609	0	8	0.791
600	610	0	8	0.775
600	612	0	8	0.792
605	612	0	8	0.782
607	614	0	8	0.853
607	616	0	8	0.904
609	616	0	8	0.833
605	616	0	8	0.832
608	616	0	8	0.829
600	616	0	8	0.772
609	617	0	8	0.802
607	628	0	8	0.789
608	628	0	8	0.789
616	628	0	8	0.781
609	629	0	8	0.919
607	629	0	8	0.889
608	629	0	8	0.884
612	629	0	8	0.834
616	629	0	8	0.822
605	629	0	8	0.804
618	629	0	8	0.798
606	629	0	8	0.797
614	629	0	8	0.789
610	629	0	8	0.776
617	629	0	8	0.773
607	630	0	8	0.874
616	630	0	8	0.847
612	630	0	8	0.847
608	630	0	8	0.816
609	630	0	8	0.809
617	630	0	8	0.768
608	631	0	8	0.865
609	631	0	8	0.82
607	631	0	8	0.819
606	631	0	8	0.768
605	637	0	8	0.776
616	638	0	8	0.79
600	638	0	8	0.764
631	639	0	8	0.796
609	639	0	8	0.777
631	640	0	8	0.796
609	640	0	8	0.785
608	640	0	8	0.766
618	642	0	8	0.864
628	642	0	8	0.854
613	642	0	8	0.838
607	642	0	8	0.804
629	643	0	8	0.912
614	643	0	8	0.883
616	643	0	8	0.86
612	643	0	8	0.846
617	643	0	8	0.827
607	643	0	8	0.813
600	643	0	8	0.777
621	643	0	8	0.771
619	643	0	8	0.769
628	644	0	8	0.879
613	644	0	8	0.868
618	644	0	8	0.834
607	644	0	8	0.824
629	645	0	8	0.924
612	645	0	8	0.882
614	645	0	8	0.87
621	645	0	8	0.87
630	645	0	8	0.866
617	645	0	8	0.863
616	645	0	8	0.861
600	645	0	8	0.827
609	645	0	8	0.81
607	645	0	8	0.803
627	645	0	8	0.788
619	645	0	8	0.777
613	646	0	8	0.768
606	654	0	8	0.801
608	654	0	8	0.794
616	655	0	8	0.899
645	655	0	8	0.897
643	655	0	8	0.895
630	655	0	8	0.863
605	655	0	8	0.804
628	655	0	8	0.79
629	655	0	8	0.784
608	655	0	8	0.782
609	655	0	8	0.765
644	656	0	8	0.873
642	656	0	8	0.86
643	657	0	8	0.911
630	657	0	8	0.909
645	657	0	8	0.905
616	657	0	8	0.891
607	657	0	8	0.871
605	657	0	8	0.785
600	657	0	8	0.764
629	657	0	8	0.763
608	658	0	8	0.801
631	658	0	8	0.792
630	658	0	8	0.782
644	658	0	8	0.766
630	659	0	8	0.86
645	659	0	8	0.809
616	659	0	8	0.808
643	659	0	8	0.779
630	660	0	8	0.859
644	660	0	8	0.847
631	660	0	8	0.844
607	660	0	8	0.807
616	660	0	8	0.803
629	660	0	8	0.799
642	660	0	8	0.79
608	660	0	8	0.769
609	661	0	8	0.937
629	661	0	8	0.874
630	661	0	8	0.862
610	661	0	8	0.832
616	661	0	8	0.821
608	661	0	8	0.776
600	661	0	8	0.767
654	661	0	8	0.766
608	662	0	8	0.81
610	662	0	8	0.784
609	664	0	8	0.802
616	666	0	8	0.84
610	683	0	8	0.792
642	683	0	8	0.78
642	684	0	8	0.869
645	684	0	8	0.862
644	684	0	8	0.859
608	684	0	8	0.839
643	684	0	8	0.836
633	684	0	8	0.798
612	684	0	8	0.783
616	684	0	8	0.771
645	685	0	8	0.926
607	685	0	8	0.921
616	685	0	8	0.921
643	685	0	8	0.917
612	685	0	8	0.894
666	685	0	8	0.885
630	685	0	8	0.88
608	685	0	8	0.868
614	685	0	8	0.849
657	685	0	8	0.842
670	685	0	8	0.838
600	685	0	8	0.828
660	685	0	8	0.826
655	685	0	8	0.813
628	685	0	8	0.812
605	685	0	8	0.804
629	685	0	8	0.803
647	685	0	8	0.801
609	685	0	8	0.787
618	685	0	8	0.78
644	685	0	8	0.777
610	685	0	8	0.766
631	686	0	8	0.878
606	686	0	8	0.872
607	686	0	8	0.835
658	686	0	8	0.828
608	686	0	8	0.828
644	686	0	8	0.81
629	686	0	8	0.809
660	686	0	8	0.798
614	686	0	8	0.788
630	686	0	8	0.783
645	686	0	8	0.776
612	686	0	8	0.77
616	687	0	8	0.943
645	687	0	8	0.931
609	687	0	8	0.917
666	687	0	8	0.914
612	687	0	8	0.897
643	687	0	8	0.89
630	687	0	8	0.873
629	687	0	8	0.858
661	687	0	8	0.856
605	687	0	8	0.852
607	687	0	8	0.848
657	687	0	8	0.844
670	687	0	8	0.843
655	687	0	8	0.827
614	687	0	8	0.822
608	687	0	8	0.807
638	687	0	8	0.802
610	687	0	8	0.791
660	687	0	8	0.781
644	688	0	8	0.797
END



