 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q21313 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MSPYDSSPWATKALFLIVTLLAQFTYSQVLTPSQITISHRKPITATSTCG 50
51 EIQGQPVTEIYCSLTGSTQYTPLNSYSYQDDEQQKSWSQYENPMVRGGHG 100
101 CGHCNAGNENSHPAANMVDGNNSWWMSPPLSRGLQHNEVNITIDLEQEFH 150
151 VAYVWIQMANSPRPGSWVLERSTDHGKTYQPWFNFAENAAECMRRFGMES 200
201 LSPISEDDSVTCRTDMASLQPLENAEMVIRILEHRPSSRQFATSEALQNF 250
251 TRATNVRLRLLGTRTLQGHLMDMNEWRDPTVTRRYFYAIKEIMIGGRCVC 300
301 NGHAVTCDILEPQRPKSLLCRCEHNTCGDMCERCCPGFVQKQWQAATAHN 350
351 NFTCEACNCFGRSNECEYDAEVDLNKQSIDSQGNYEGGGVCKNCRENTEG 400
401 VNCNKCSFGYFRPEGVTWNEPQPCKVCDCDPDKHTGACAEETGKCECLPR 450
451 FVGEDCDQCASGYYDAPKCKPCECNVNGTIGDVCLPEDGQCPCKAGFGGT 500
501 FCETCADGYTNVTAGCVECVCDATGSEHGNCSASTGQCECKPAYAGLSCD 550
551 KCQVGYFGDDCKFCNCDPMGTEGGVCDQTTGQCLCKEGFAGDKCDRCDIA 600
601 FYGYPNCKACACDGAGITSPECDATSGQCPCNGNFTGRTCDKCAAGFYNY 650
651 PDCRGCECLLSGAKGQTCDSNGQCYCKGNFEGERCDRCKPNFYNFPICEE 700
701 CNCNPSGVTRDFQGCDKVSPGELCSCRKHVTGRICDQCKPTFWDLQYHHE 750
751 DGCRSCDCNVNGTISGLNTCDLKTGQCMCKKNADGRRCDQCADGFYRLNS 800
801 YNQMGCESCHCDIGGALRAECDITSGQCKCRPRVTGLRCDQPIENHYFPT 850
851 LWHNQYEAEDAHTEDQKPVRFAVDPEQFADFSWRGYAVFSPIQDKILIDV 900
901 DITKATVYRLLFRYRNPTSVPVTATVTINPRFTHTHDVEQTGKATFAPGD 950
951 LPAMKEITVDGKPFVLNPGKWSLAISTKQRLFLDYVVVLPAEYYEGTVLR 1000
1001 QRAPQPCLSHSTKNTTCVDLIYPPIPSVSRQFVDMDKVPFNYINEDGTTT 1050
1051 ALEHVPVEILLSEITGPAAFVRADENPRVVEAKLDVPETGEYVIVLEYHN 1100
1101 REETDGNIGVGISQNDKEVLNGNAVIHHCPYATFCRELVSSEGTIPYIPL 1150
1151 EKGEATVRLNIKPNHEFGLAGVQLIKKSDFSSEYLQQVPVCIKKDARCVQ 1200
1201 QSYPPAADSVTTEAESGSNMDKSILGDKLPFPVSNSKEMRVVPLDDAQAT 1250
1251 VEISGVVPTRGHYMFMVHYFNPDNTPINIDVLIQNEHYFQGDSCNSFACS 1300
1301 SVPLAFCPSISGCRALIRDKERPEVIQFYMDDKYTATFYHNSSQKGPIYI 1350
1351 DSITAVPYNSYKDKLMEPLALDLSNEFLKECSEDNLKNHPESVSDFCKQK 1400
1401 IFSLTTDFNAAALSCDCVAQGSESFQCEQYGGQCKCKPGVIGRRCERCAP 1450
1451 GYYNFPECIKCQCNAGQQCDERTGQCFCPPHVEGQTCDRCVSNAFGYDPL 1500
1501 IGCQKCGCHPQGSEGGNLVCDPESGQCLCRESMGGRQCDRCLAGFYGFPH 1550
1551 CYGCSCNRAGTTEEICDATNAQCKCKENVYGGRCEACKAGTFDLSAENPL 1600
1601 GCVNCFCFGVTDSCRSSMYPVTIMSVDMSSFLTTDDNGMVDNKDDTVIYT 1650
1651 SEETSPNSVYFNVPIEKKDYTTSYGLKLTFKLSTVPRGGRKSMNADADVR 1700
1701 LTGANMTIEYWASEQPTNPEEQFTVKCKLVPENFLTAEGKTVTREELMKV 1750
1751 LHSLQNITLKASYFDHPKTSTLYEFGLEISEPNGVDSVIKASSVEQCQCP 1800
1801 APYTGPSCQLCASGYHRVQSGSFLGACVPCECNGHSATCDPDTGICTDCE 1850
1851 HNTNGDHCEFCNEGHYGNATNGSPYDCMACACPFAPTNNFAKSCDVSEEG 1900
1901 QLLQCNCKPGYTGDRCDRCASGFFGHPQISGESCSPCQCNGNNNLTDSRS 1950
1951 CHPNSGDCYLCEQNTDGRHCESCAAWFYGDAVTAKNCSSCECSQCGSQYC 2000
2001 DNKSGGCECKINVEGDSCDRCKPDHWGFSKCQGCQGCHCGTAAFNTQCNV 2050
2051 ENGQCTCRPGATGMRCEHCEHGYWNYGEHGCDKCDCEADLSMGTVCDVRT 2100
2101 GQCHCQEGATGSRCDQCLPSYLRIPTYGCRRCDECVHHLIGDVDNLELEI 2150
2151 DVLGTAIANISSATIVGARLARNKKEFNDINEITKMLNDEENSFGNVFGD 2200
2201 AQDILTNSTQIQNKLVRTKTHSQNSVSSAKNITLNGTEFLQEVMKRAQRA 2250
2251 RQSVRSLAEIALAIGSSSKAVNVDPRLLKEAEETLMTLEAASADQYPEKA 2300
2301 QTVPGKLEEIQKKIQEETEKLDKQKETFEAQKKRAEELAAYLNSAQQLLK 2350
2351 ESKSKADKSNNIAKMLQLTKVENLVAAITDDLERVEAAKGEFQKLNVAIG 2400
2401 NITENLKDKREEMTHAVTTLNETRNDVAEALEAAKKRVRRDEKSVDMQLV 2450
2451 NAKAHELHLQATTLRQTFDNNKDNTDQAVEAANAFSNLTDTLKNAKAQID 2500
2501 NAYEALSAEPAFAESVQNARDKPFPDETKEKIDALSKTVSQDLKETEKLK 2550
2551 KQLEQLTELSEKLRKRKEAVKAGIPKYSKNTLDSIDEKVQEVEKLKAEID 2600
2601 ANIEETRAKISEIAGKAEEITEKANSAMEGIRLARRNSVQLNKLAPVIVS 2650
2651 KFEELKKLSSARSAKVDSVSDKVSQIKEMIAVARDAANRIKLGAHFEKGS 2700
2701 SLDLNIPQRVTRSAAHADISFYFRTEQEHGIPLFFGNEETAVGSRAVPTA 2750
2751 DYVAAEIEYGRPKITVDLGDAPAVVKLDTPVNDGLWRRLNIERIGKTVSV 2800
2801 TLSKPNSVETAETKSSVAGGNKSVLNLNQQISRLFVGGVPTSARISKDLY 2850
2851 NRDFVGDIESLKLHGEPIGLWNSREKGNTNVNGAQKKPKITDNADELVVS 2900
2901 LDGEGYTSYKPSHWNPRKATKISLSFLTFSPHGLLFFVGKDKDFMALELS 2950
2951 DGGVKLSVDLGSGVGQWITESSNYNDGKWHTVSIVREEKHVKIMIDGETE 3000
3001 VLEGDVPGKDSEMSVTEFLYIGGTPSGLSVRTTIVPLRGCIKSVKLGSDN 3050
3051 VDLESSHASKGVRSGCPLHSVRTVSFLSDRTTASFNNATEFSEDVSVTFK 3100
3101 FKTRSIRQPSSLFTVNDDEDSVLSVSINEDGILTVTSGEDIATLELAASP 3150
3151 DEKWHYVSIRKTKYIIRIDADDSFSNEVARKHADDSNPDASFLSAFFGKS 3200
3201 GETPSFVGCIGDVTLNGKLLDFANSEIKEISLNGCSLSDDENISTTTTAA 3250
3251 PKPTDDSDVAVLPIDEEEESTTTTTTTTTEEPTEEPAEARPDGHCSLPED 3300
3301 PMVQFEDAEGFNFGSQQYSRIEYDILPEAIDKSGEFTFKIRPTSDNGIIF 3350
3351 IATNKRTDHIAVMLEHGRVVFTYDTGSGQVIIKSDKSIIDGRWHTIKVSR 3400
3401 RGKSAHLIVDDNSYESEGAANQNEDLIETQPPFYVGGVPADLAGFARNLV 3450
3451 VGVRSQFSGCIKDFKLNGKSLDNGKEFGTEQCSQFSEPGMYFGKDGGYAI 3500
3501 VQKDYEVGLTFGLEVEMRPRMKNGILFSVGVLEYITVEFVNGSIKTTVES 3550
3551 GSGGEELWHHPDIENQYCDGQWQSFKISKKRNLLTVAVNGKAHLKILKKA 3600
3601 KTDVLTKDPLYFGGLPEGVTNKGIKTNKPFVGCIRFVSFGLKKDRKIRRK 3650
3651 KQVDTERFDVFGDVHRNACPAI 3672
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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