 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9BPS0 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MMWKTLLCCLLAVSAAALDGWEPGKRYEYHVRGRTLTALHEVANQYSGFR 50
51 FKGKLVIEPHTPSVLRGQLKDTYHMTVHRMLPDGWDQKFEERESNWERVG 100
101 MKNKPFEVHVGNEFQFNKLIVTEDTPVWETNMIKGVLSQIQVNLKEVGPD 150
151 PRDEQEDRLRKIFKVHESSVTGRCEVLYDITPITKFNMLPQPLVEIEEEN 200
201 VNVLQVMKTQNFTDCKKLPSYVHGFYNFHNVFPAQNKAGFMSRSQQTRTI 250
251 VSRNKETGRFTIRSSVTFHEVVLKPELFNSQQGISVSRMNVTLEEIKSQQ 300
301 HIPPPRPPKDVGDLVYRYSAETGEPSQRDSAYALESNSDSSSSSSSSSSE 350
351 ENAANSRHRSSSSSSSSRSSEEMRDSKKHPRASTTESQPRNSRSRRSLQN 400
401 SKRSINMYNDSSSSSSSSSSEEYLLPRPHIENAPNIPFMPYFVGNQGSKI 450
451 GEVDPEKIVLLARTISSELQEPDTMVKKNILSRFSILTNLVRAASFSQLE 500
501 EATKRLYYRVERADNGDESKLDAWKAYRDSVAQAGTPAALKMVHTWIRKE 550
551 YIKDEEAAKVVAVIPHAADTPTDNYIAYFFEMVKDPVVHGEKYLNSSAVL 600
601 AFSKLLRLAAVDSEAVRRYPVHVFGRMVPKNFSARVKEYIEYFANKLKNA 650
651 VKDKDSHKIQVYTRALGNTGHADIIRHFEPYLVGRESVSTHERVTMVFCL 700
701 DEFVKTQPSVAQYILLRLFENVGETQEIRVAALYLLMKTDVSAELFQRLA 750
751 EYTKFDKNHQVVSAVQSAIRSAAKVEGPYKKETAKNAQAAVKILSSKPYD 800
801 DSYSKSFILNNYRREIDVGYSRLYNQIGSRDSFMPKSVFYKLVNIIDGDR 850
851 DDQAKFGGAVSSVRDVIDFIRQQFKKDDSQDELENSKYAEDDDIWDLREI 900
901 ANLLEMEEENVDPLEGNVHYDYFGAQRFFTLNKTSFEFREELKKYFKKPQ 950
951 ITNINKLYNRMELKVGYPNVMGVPFFFTFKRPTLVKLTAKTFIMPLKPCD 1000
1001 HGKPHKFPRIFNVTSDVSFVYSFDMHSHMGVVAPFNKKEYVTGIQRKHMI 1050
1051 QIPLNVSVHVNLDKNKVAADFKPYYEDNFKVAEARGIPFTTVHDIKSLVP 1100
1101 YVEAEHTSYIRVRPSKAYEGNFGKSVGMVYHYNFETDQQFFDYKWFSSNY 1150
1151 FLHYPNVAFYYGWEAQPVFYYDFKLYLDSHNSPAKTVQLKASYDNRYTQP 1200
1201 EEEEETRQHSKIRRPRSASRKHRRSRHEERAPLENLEVSDTETQREELYD 1250
1251 IVLPAVRAGRLYYASVSVAFKGEENVYSKYEVEGALASSQVNEHISTMLR 1300
1301 AHSNDAERKHQYAHVRVNVTMPQVPVIDYRKALEFDPTSKIQCEVHFGDT 1350
1351 PEKKSKVYFQGKFERTDERKKFVAESDMAQLCSAQQNNKNYLLPACRNVT 1400
1401 EEASKLDKYFFKVKYENLSEKCRNRTYKAYSYLRHYFFPYITENVYPDER 1450
1451 KTDSVEVQVQFNEEINAVNVSVKAPILNVEFTDVRVYNKYARALFSLNPR 1500
1501 YPLLSQVAKTAFPQYYEPTCVVDYSKVNTFDNRTYEHDMLNDWVEVMFHK 1550
1551 PRSKIYKQVSVSAKQAHSKMVLKVLRGDEVKFEMKQPRDSSSSPELKMND 1600
1601 KVIEYERSPAFIHYKHELIAVAYALPSKALHLDLLNDSLVFVYDGERVML 1650
1651 HAGNHYRNQVRGLCGTFDGEPSTDFKAPQNCHVRDVEDLILAYTLVRDLD 1700
1701 RSRLRDENICVREDVQLVNLTNHRHAEKSGIRPYDIDDDSSSSSSSSSSS 1750
1751 SSSSSSSKSNSTSSSSSESNESALPRGENKLHRAQQPSRNCHMHLHRIVT 1800
1801 HNGKQCISKLALTECAPLCREESHTTKTEAFVCFPPGPTADHYTKLVRKG 1850
1851 VSPDFSRKTDIVNLRVTIPSRCVSKI 1876
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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