 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P22944 from www.uniprot.org...
The NucPred score for your sequence is 0.37 (see score help below)
1 MPLLDGPRNGETVTASAHNGIPIIDGVDPSTLRGDIDQDPNRRQKIVVVG 50
51 LGMVAVAFIEKLVKLDSERRKYDIVVIGEEPHIAYNRVGLSSYFEHRKIE 100
101 DLYLNPKEWYGSFKDRSFDYYLNTRVTDVFPQHKTVKTSTGDIVSYDILV 150
151 LATGSDAVLPTSTPGHDAKGIFVYRTISDLERLMEFAANHKGQTGVTVGG 200
201 GLLGLEAAKAMTDLEDFGSVKLIDRNKWVLARQLDGDAGSLVTKKIRDLG 250
251 LEVLHEKRVAKIHTDDDNNVTGILFEDGQELDCCCICFAIGIRPRDELGG 300
301 STGIQCAKRGGFVIDESLRTSVNDIYAIGECASWENQTFGIIAPGIEMAD 350
351 VLSFNLTNPDKEPKRFNRPDLSTKLKLLGVDVASFGDFFADRDGPKFLPG 400
401 QRPSAESIGAADPNREEEPQVKALTYRDPFGGVYKKYLFTMDGKYLLGGM 450
451 MIGDTKDYVKLNQMVKSQKPLEVPPSEFILGAQSGGEENADDLDDSTQIC 500
501 SCHNVTKGDVVESVKSGTCKTIADVKSCTKAGTGCGGCMPLVQSIFNKTM 550
551 LDMGQEVSNNLCVHIPYSRADLYNVIAIRQLRTFDDVMKSAGKCPDSLGC 600
601 EICKPAIASILSSLFNPHLMDKEYHELQETNDRFLANIQRNGTFSVVPRV 650
651 PGGEITADKLIAIGQVAKKYNLYCKITGGQRIDMFGARKQDLLDIWTELV 700
701 DAGMESGHAYAKSLRTVKSCVGTTWCRFGVGDSVGMAIRLEQRYKSIRAP 750
751 HKFKGAVSGCVRECAEAQNKDFGLIATEKGFNIFVGGNGGAKPRHSELLA 800
801 KDVPPEEVIPILDRYVIFYIRTADKLQRTARWLESLPGGIEYLKDVVLND 850
851 KLGIAAEMERQMQELVDSYFCEWTETVRNPKRRKYFQQFANTDETVENVE 900
901 IVKEREQVRPTYWPKDGANEDFKGHQWSSLSWQPVIKADYFSDGPPAISS 950
951 ANIKRGDTQLAIFKVKGKYYATQQMCPHKRTFVLSDGLIGDDDNGKYWVS 1000
1001 CPYHKRNFELNGEQAGRCQNDEAMNIATFPVEEREDGWIYMKLPPVEELD 1050
1051 SVLGTEKWKVKKGEAVDPFEAYDKKYSGMKGKRAGAKGIEGSKPTRSPSN 1100
1101 TIDW 1104
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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