 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6B8R8 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MRLLMSQEIMFKYIFPDLAAIQKESFKSFLLEGLSEVLDSFPIIVDPTGK 50
51 LELQFFGKDYKLKFPRYSVRKAKSRDKTYSAQIYIPAKLTRRDIEVIKEN 100
101 ISTSKDLSYFFNSQDINKKYRKRPVFIGDLPLMTNRGTFVISGTERIIIN 150
151 QIVRSPGVYYKKELDKNNKQIYSCSIISNRGSWLKFEIDSKAQIWAKIDK 200
201 NHKVSAYIFLRSIGLNNEDIRKRLTKYNYLINSSLTYYKKEFSKDVNNVD 250
251 IEKITEEEALVIVYSKLRPNEPATLAVAKQMLYTRFFDPKRYDLGAVGRH 300
301 KINQKLNLKVPTHFRVLSPEDILVSLDYLLNIKEQNIGTFDDIDHLGNRR 350
351 VRSVGELLQNQVRIGLNRLERIIRERMIICDLDSLSLSNLVNPKPLMASV 400
401 REFFSSSQLSQFMDQTNPLSELTHKRRISALGPGGLNKDRAGFAVRDLHP 450
451 SHYGRICPIETPEGPNAGLIGSLSIYAKVNRYGFIETPCYKVTDGQVLKK 500
501 DKLYYITADQEDYLRIAPADISLDVNDFIQDKIIAVRYKQEFITATISQV 550
551 DYMAVSPIQFISAATSLIPFLEHDDANRALMGSNMQRQAVPLLFPEKSIV 600
601 GTGLEAKIAKDSGMTIISRTNGTVSYVSGTKIGIQNKEGYTIHYRLKKYY 650
651 RSNQDTCINQRPIVWPGENIRIGQTIADGASTDGGEIALGRNIIVAYMPW 700
701 EGYNYEDAFLISERLVYEDLYTSIHIERYELECRQTKLGSEEITRDIPNV 750
751 SEASIGLLDKNGIISIGSWVDAGDILVGKVTPKGESDQLPEGKLLRAIFG 800
801 EKARDVRDTSLRLPNATKGRVVNVKIFKRQKGDELPPGTNEIIRVYVAQK 850
851 RKIQVGDKMAGRHGNKGIISRILSLQDMPFLPDGTPVDIILNPLGVPSRM 900
901 NVGQLFECLLGLAGEYSSKRFKIIPFDEMYGSEASRALVYNKLKQASSMN 950
951 DKSWLFNALHPGKVMLVDGRTGEFFDNPVTVGKAYILKLVHLVDDKIHAR 1000
1001 STGPYSLVTQQPLGGRAQHGGQRLGEMEVWALEAFGAAYTLQELLTVKSD 1050
1051 DMQGRNDALNAIVKGKPIPKPGTPESFKVLMRELQSLALDIAVHKLELLD 1100
1101 NGNKASIEIDLMSDEQVSAI 1120
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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