 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P62294 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MANRRVGRGCWEVSPTERRPPAAEEEASSPPVLSLSHFCRSPFLCFGDVL 50
51 LGASRTLSLALDNPNEEVAEVKISHFPAADLGFSVSQRCFVLQPKEKIVI 100
101 SVNWTPFKEGRVREIMTFLVNDVLKHQAILLGNAEEQKKKKRSLWDTIKK 150
151 KKISASTSHNRRVSNIQNVNKTFSVSQKVDRVRSPLQACENLAMNEGGPP 200
201 TENNSLTLEGNKIPISPISPAFNECHGETCLPLSVRRSTTYSSLHASENR 250
251 ELLNVDSASVSKVSFNEKAVTETSFNSINVNGQSGENSKLSLTPNYSSTL 300
301 NITQSQIHFLSPDSFVNNSHGANNELELVTCLSSDMFMKDNSKPVHLEST 350
351 IAHEIYQKILSPDSFIKDNYGLNQDVESESVNPILSPNQFLKDNMAYMCT 400
401 SQQTCKVPLSNENSQVPQSPQDWRKSEVLPRIPECQGSKSPKAIFEELVE 450
451 MKSNYYSFIKQNNPKFSVVQDISSHSHNKQPKRRPILSATVTKRKATCTR 500
501 ENQTEINKPKAKRCLNSAVGEHEKVINNQKEKEDFHSYLPIIDPILSKSK 550
551 SYKNEVTPSSTTASVARKRKSDGSMEDADVRVAVTEHTEVREIKRIHFSP 600
601 SEPKTSAVKKTKNVITPISKRISNREKLNLKKKTDLSIFKTPISKTNKRT 650
651 KPIIAVAQSNLTFIKPLKTDIPRHPMPFAAKNMFYDERWKEKQEQGFTWW 700
701 LNFILTPDDFTVKTNISEVNAATLLLGIENQHKISVPRAPTKEEMSLRAY 750
751 TARCRLNRLRRAACRLFTSEKMIKAIKKLEIEIEARRLIVRKDRHLWKDV 800
801 GERQKVLNWLLSYNPLWLRIGLETTYGELISLEDNSDVTGLAMFILNRLL 850
851 WNPDIAAEYRHPTVPHLYRDGHEEALSKFTLKKLLLLVCFLDYAKISRLI 900
901 DHDPCLFCKDAEFKASKEILLAFSRDFLSGEGDLSRHLGLLGLPVNHVQT 950
951 PFDEFDFAVTNLAVDLQCGVRLVRTMELLTQNWDLSKKLRIPAISRLQKM 1000
1001 HNVDIVLQVLKSRGIELSDEHGNTILSKDIVDRHREKTLRLLWKIAFAFQ 1050
1051 VDISLNLDQLKEEIAFLKHTKSIKKTISLLSCHSDDLINKKKGKRDSGSF 1100
1101 EQYSENIKLLMDWVNAVCAFYNKKVENFTVSFSDGRVLCYLIHHYHPCYV 1150
1151 PFDAICQRTTQTVECTQTGSVVLNSSSESDDSSLDMSLKAFDHENTSELY 1200
1201 KELLENEKKNFHLVRSAVRDLGGIPAMINHSDMSNTIPDEKVVITYLSFL 1250
1251 CARLLDLRKEIRAARLIQTTWRKYKLKTDLKRHQEREKAARIIQSAVINF 1300
1301 LAKQRLRKRVNAALVVQKYWRRVLAQRKLLMLKKEKLEKVQNKAASLIQG 1350
1351 YWRRYSTRRRFLKLKYYSIILQSRIRMIIAVTSYKRYLWATVTIQRHWRA 1400
1401 YLRRKQDQQRYEMLKSSTLIIQSMFRKWKRRKMQSQVKATVILQRAFREW 1450
1451 HLRKQAKEENSAIVIQSWYRMHKELRKYIYIRSCVVIIQKRFRCFQAQKL 1500
1501 YKRKKESILTIQKYYKAYLKGKIERTNYLQKRAAAIQLQAAFRRLKAHNL 1550
1551 CRQIRAAGVIQSYWRMRQDRVRFLNLKKTIIKLQAHVRKHQQLQKYKKMK 1600
1601 KAAVIIQTHFRAYIFARKVLASYQKTRSAVIVLQSAYRGMQARKMYIHIL 1650
1651 TSVIKIQSYYRAYVSKKEFLSLKNATIKLQSIVKVKQTRKQYLHLRAAAL 1700
1701 FIQQCYRSKKIAAQKREEYMQMRESCIKLQAFVRGYRVRKQMRLQRKAVI 1750
1751 SLQSYFRMRKARQYYLKMYKAIIVIQNYYHAYKAQVNQRKNFLQVKKAAT 1800
1801 CLQAAYRGYKVRQLIKQQSIAALKIQSAFRGYNKRIKYQSVLQSIIKIQR 1850
1851 WYRAYKTLHDTRTHFLKTKAAVISLQSTYRGWKVRKQIRREHQAALKIQS 1900
1901 AFRMAKARKQFRLFKTAALVIQQNFRAWTAGRKQRMEYIELRHAVLMLQS 1950
1951 MWKGKTLRRQLQRQHKCAIIIQSYYRMHVQQKKWKIMKKAALLIQKYYRA 2000
2001 YSIGREQNHLYLKTKAAVVTLQSAYRGMKVRKRIKDCNKAAVTIQSKYRA 2050
2051 YKTKKKYATYRASAIIIQRWYRGIKITNHQHKEYLNLKKTAIKIQSVYRG 2100
2101 IRVRRHIQHMHRAATFIKAMFKMHQSRISYHTMRKAAIVIQVRFRAYYQG 2150
2151 KMQHEKYLTILKAVKILQASFRGVRVRRTLRKMQIAATLIQSNYRRYRQQ 2200
2201 TYFNKLKKITKTVQQRYRAMKERNIQFQRYNKLRHSVIYIQAIFRGKKAR 2250
2251 RHLKMMHIAATLIQRRFRTLMMRRRFLSLKKTAIWIQRKYRAHLYTKHHL 2300
2301 QFLRVQNAVIKIQSSYRRWMIRKRMREMHRAATFIQAIFRMRRLHMRYQA 2350
2351 LKQASVVIQQQYQANRAAKLQRQHYLRQRHSAVILQAAFRGMKTRRHLKS 2400
2401 MHSSATLIQSRFRSLLVRRRFISLKKATIFVQRKYRATICAKHKLHQFLH 2450
2451 LRKSAITIQSSYRRLMVKKKLQEMQRAAVLIQATFRMHRTYITFQTWKHA 2500
2501 SILIQQHYRTYRAAKLQRENYIRQWHSAVVIQAAYKGMKARQLLREKHKA 2550
2551 AIIIQSTYRMYRQYCFYQKLQWATKIIQEKYRANKKKQKAFQHNELKKET 2600
2601 CVQAGFQDMNIKKQIQDQHQAAIIIQKNCKAFKIRKHYLHLRATVVSIQR 2650
2651 RYRKLTAVRTQAVICIQSYYRGFKVRKDIQNMHRAATLIQSFYRMHRAKV 2700
2701 DYQTKKTAIVVIQNYYRLYVRVKTERKSFLAVQKSVRTIQPAFRGMKVRQ 2750
2751 KLKNLSEEKMAAIVNQSALCCYRSKTQYEAVQSEGVMIQEWYKASGLACS 2800
2801 QEAEYHSQSRAAVTIQKAFCRMATRKLETQKCAALRIQFFLQMAVYRRRF 2850
2851 VQQKRAAITLQHYFRTWQTRKQFLLYRKAAVVLQNHYRAFLSAKHQRQVY 2900
2901 LQIRSSVIIIQARSKGFIQKRKFQEIKNSTIKIQAIWRRYRAKKYLCKVK 2950
2951 AACKIQAWYRCWRAHKEYLAILKAVKIIQGCFYTKLERTRFLNVRASAII 3000
3001 IQRKWRAILSAKIAHEHFLMIKRHRATCLIQAHYRGYKGRQVFLRQKSAA 3050
3051 LVIQKYIRAREAGKRERIKYIEFKKSTVILQALVRGWLVRKRILEQRAKI 3100
3101 RLLHFTAAAYYHLNALRIQRAYKLYLAVKNANKQVNSVICIQRWFRARLQ 3150
3151 RKRFIQKYHSIKKIEHEGQECLSQQNRAASVIQKAVRHFLLRKKQEKFTS 3200
3201 GIIKIQALWRGYSWRKKNDCTKIKAIRLSLQVVNREIREENKLYKRTALA 3250
3251 LHYLLTYKHLSAILEALKHLEVVTRLSPLCCENMAQSGAISKIFVLIRSC 3300
3301 NRSVPCMEVIRYAVQVLLNVSKYEKTTSAVYDVENCIDILLELLQIYREK 3350
3351 PGNKVADKGGSIFTKTCCLLAILLKTTNRASDVRSRSKVVDRIYSLYKLT 3400
3401 AHKHKMNTERILYKQKKNSSISIPFIPETPVRTRIVSRLKPNWVLRRDNM 3450
3451 EEITNPLQAIQMVMDTLGIPY 3471
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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