 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P51839 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MAGLQQGCHPEGQDWTAPHWKTCRPCQGPRGLTVRHLRTVSSISVFSVVF 50
51 WGVLLWADSLSLPAWARETFTLGVLGPWDCDPIFAQALPSMATQLAVDRV 100
101 NQDASLLLGSQLDFKILPTGCDTPHALATFVAHRNTVAAFIGPVNPGYCP 150
151 AAALLAQGWGKSLFSWACGAPEGGGALVPTLPSMADVLLSVMRHFGWARL 200
201 AIVSSHQDIWVTTAQQLATAFRAHGLPIGLITSLGPGEKGATEVCKQLHS 250
251 VHGLKIVVLCMHSALLGGLEQTVLLRCARKEGLTDGRLVFLPYDTLLFAL 300
301 PYRNRSYLVLDDDGPLQEAYDAVLTISLDTSPESHAFTATKMRGGTAANL 350
351 GPEQVSPLFGTIYDAVILLAHALNHSEAHGTGLSGAHLGNHIRALDVAGF 400
401 SQRIRIDGKGRRLPQYVILDTNGEGSQLVPTHILDVSTQQVQPLGTAVHF 450
451 PGGSPPARDASCWFDPNTLCIRGVQPLGSLLTLTITCVLALVGGFLAYFI 500
501 RLGLQQLRLLRGPHRILLTPQELTFLQRTPSRRRPHVDSGSESRSVVDGG 550
551 SPQSVIQGSTRSVPAFLEHTNVALYQGEWVWLKKFEAGTAPDLRPSSLSL 600
601 LRKMREMRHENVTAFLGLFVGPEVSAMVLEHCARGSLEDLLRNEDLRLDW 650
651 TFKASLLLDLIRGLRYLHHRHFPHGRLKSRNCVVDTRFVLKITDHGYAEF 700
701 LESHCSFRPQPAPEELLWTAPELLRGPRRPWGPGKATFKGDVFSLGIILQ 750
751 EVLTRDPPYCSWGLSAEEIIRKVASPPPLCRPLVSPDQGPLECIQLMQLC 800
801 WEEAPDDRPSLDQIYTQFKSINQGKKTSVADSMLRMLEKYSQSLEGLVQE 850
851 RTEELELERRKTERLLSQMLPPSVAHALKMGTTVEPEYFDQVTIYFSDIV 900
901 GFTTISALSEPIEVVGFLNDLYTMFDAVLDSHDVYKVETIGDAYMVASGL 950
951 PRRNGNRHAAEIANMALEILSYAGNFRMRHAPDVPIRVRAGLHSGPCVAG 1000
1001 VVGLTMPRYCLFGDTVNTASRMESTGLPYRIHVSRNTVQALLSLDEGYKI 1050
1051 DVRGQTELKGKGLEETYWLTGKTGFCRSLPTPLSIQPGDPWQDHINQEIR 1100
1101 TGFAKLARVG 1110
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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