 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JKL8 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MFQLPVNNLGSLRKARKTVKKILSDIGLEYCKEHIEDFKQFEPNDFYLKN 50
51 TTWEDVGLWDPSLTKNQDYRTKPFCCSACPFSSKFFSAYKSHFRNVHSED 100
101 FENRILLNCPYCTFNADKKTLETHIKIFHAPNSSAPSSSLSTFKDKNKND 150
151 GLKPKQADNVEQAVYYCKKCTYRDPLYEIVRKHIYREHFQHVAAPYIAKA 200
201 GEKSLNGAVSLGTNAREECNIHCKRCLFMPKSYEALVQHVIEDHERIGYQ 250
251 VTAMIGHTNVVVPRAKPLMLIAPKPQEKKSMGLPPRISSLASGNVRSLPS 300
301 QQMVNRLSIPKPNLNSTGVNMMSNVHLQQNNYGVKSVGQSYGVGQSVRLG 350
351 LGGNAPVSIPQQSQSVKQLLPSGNGRSYGLGAEQRPPAAARYSLQTANTS 400
401 SLPPGQVKSPSVSQSQASRVLGQSSSKPPPAATGPPPSNHCATQKWKICT 450
451 ICNELFPENVYSVHFEKEHKAEKVPAVANYIMKIHNFTSKCLYCNRYLPT 500
501 DTLLNHMLIHGLSCPYCRSTFNDVEKMAAHMRMVHIDEEMGPKTDSTLSF 550
551 DLTLQQGSHTNIHLLVTTYNLRDAPAESVAYHAQNNAPVPPKPQPKVQEK 600
601 ADVPVKSSPQAAVPYKKDVGKTLCPLCFSILKGPISDALAHHLRERHQVI 650
651 QTVHPVEKKLTYKCIHCLGVYTSNMTASTITLHLVHCRGVGKTQNGQDKT 700
701 NAPSRLNQSPGLAPVKRTYEQMEFPLLKKRKLEDENDSPGCFEEKPEEPV 750
751 VLALDPKGHEDDSYEARKSFLTKYFNKQPYPTRREIEKLAASLWLWKSDI 800
801 ASHFSNKRKKCVRDCEKYKPGVLLGFNMKELNKVKHEMDFDAEWLFENHD 850
851 EKASRVNASKTVDKKLNLGKEDDSFSDSFEHLEEESNGSGGPFDPVFEVE 900
901 PKIPSDNAEEPVPKVIPEGALESEKLDQKEEEDGSKYETIHLTEERAKLM 950
951 HDASDSEVDQDDVVEWKDGASPSESGPGSRQVSDFEDNTCEMKPGTWSDE 1000
1001 SSQSEDARSSKPAAKKKATVQDDTEQLKWKNSSYGKVEGFWSKDQSQWEN 1050
1051 ASENAERLPNPQIEWQNSTIDSEDGEQFDSMTDGVADPMHGSLTGVKLSS 1100
1101 QQA 1103
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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