 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P58107 from www.uniprot.org...
The NucPred score for your sequence is 0.56 (see score help below)
1 MSGHTLPPLPVPGTNSTEQASVPRAMAATLGAGTPPRPQARSIAGVYVEA 50
51 SGQAQSVYAAMEQGLLPAGLGQALLEAQAATGGLVDLARGQLLPVSKALQ 100
101 QGLVGLELKEKLLAAERATTGYPDPYGGEKLALFQAIGKEVVDRALGQSW 150
151 LEVQLATGGLVDPAQGVLVAPEPACHQGLLDRETWHKLSELEPGTGDLRF 200
201 LDPNTLERLTYHQLLERCVRAPGSGLALLPLKITFRSMGGAVSAAELLEV 250
251 GILDEQAVQGLREGRLAAVDVSARAEVRRYLEGTGSVAGVVLLPEGHKKS 300
301 FFQAATEHLLPMGTALPLLEAQAATHTLVDPITGQRLWVDEAVRAGLVSP 350
351 ELHEQLLVAEQAVTGHHDPFSGSQIPLFQAMKKGLVDRPLALRLLDAQLA 400
401 TGGLVCPARRLRLPLEAALRCGCLDEDTQRQLSQAGSFSDGTHGGLRYEQ 450
451 LLALCVTDPETGLAFLPLSGGPRGGEPQGPPFIKYSTRQALSTATATVSV 500
501 GKFRGRPVSLWELLFSEAISSEQRAMLAQQYQEGTLSVEKLAAKLSATLE 550
551 QAAATARVTFSGLRDTVTPGELLKAEIIDQDLYERLEHGQATAKDVGSLA 600
601 SVQRYLQGTGCIAGLLLPGSQERLSIYEARCKGLLRPGTALILLEAQAAT 650
651 GFIIDPKANKGHSVEEALRAAVIGPDVFAKLLSAERAVTGYTDPYTGQQI 700
701 SLFQAMQKGLIVREHGIRLLEAQIATGGVIDPVHSHRVPVDVAYRRGYFD 750
751 QMLNLILLDPSDDTKGFFDPNTHENLTYLQLLERCVRDPETGLYLLPLSS 800
801 TQSPLVDSATQQAFQNLLLSVKYGRFQGQRVSAWELINSEYFSEGRRRQL 850
851 LRRYRQREVTLGQVAKLLEAETQRQADIMLPALRSRVTVHQLLEAGIIDQ 900
901 QLLDQVLAGTISPEALLLMDGVRRYLCGLGAVGGVRLLPSGQRLSLYQAM 950
951 RQKLLGPRVALALLEAQAATGTIMDPHSPESLSVDEAVRRGVVGPELYGR 1000
1001 LKRAEGAIAGFRDPFSGKQVSVFQAMKKGLIPWEQAARLLEAQVATGGII 1050
1051 DPTSHHHLPMPVAIQRGYVDQEMETALSSSSETFPTPDGQGRTSYAQLLE 1100
1101 ECPRDETSGLHLLPLPESAPALPTEEQVQRSLQAVPGAKDGTSLWDLLSS 1150
1151 CHFTEEQRRGLLEDVQEGRTTVPQLLASVQRWVQETKLLAQARVMVPGPR 1200
1201 GEVPAVWLLDAGIITQETLEALAQGTQSPAQVAEQPAVKACLWGTGCVAG 1250
1251 VLLQPSGAKASIAQAVRDGLLPTGLGQRLLEAQVASGFLVDPLNNQRLSV 1300
1301 EDAVKVGLVGRELSEQLGQAERAAAGYPDPYSRASLSLWQAMEKGLVPQN 1350
1351 EGLPLLQVQLATGGVVDPVHGVHLPQAAACRLGLLDTQTSQVLTAVDKDN 1400
1401 KFFFDPSARDQVTYQQLRERCVCDSETGLLLLPLPSDTVLEVDDHTAVAL 1450
1451 RAMKVPVSTGRFKGCSVSLWDLLLSEYVGADKRRELVALCRSGRAAALRQ 1500
1501 VVSAVTTLVEAAERQPLQATFRGLRKQVSARDLFRAQLISRKTLDELSQG 1550
1551 TTTVKEVAEMDSVKRSLEGGNFIAGVLIQGTQERMSIPEALRRHILRPGT 1600
1601 ALVLLEAQAATGFIIDPVENRKLTVEEAFKAGMFGKETYVKLLSAERAVT 1650
1651 GYTDPYTGQQISLFQAMQKDLIVREHGIRLLEAQIATGGIIDPVHSHRVP 1700
1701 VDVAYRCGYFDEEMNRILADPSDDTKGFFDPNTHENLTYLQLLERCVEDP 1750
1751 ETGLYLLQIIKKGENYVYINEATRHVLQSRTAKMRVGRFADQVVSFWDLL 1800
1801 SSPYFTEDRKRELIQEYGAQSGGLEKLLEIITTTIEETETQNQGIKVAAI 1850
1851 RGEVTAADLFNSRVIDQKTLHTLRVGRTGGQALSTLECVKPYLEGSGCIA 1900
1901 GVTVPSTREVMSLHEASRKELIPAAFATWLLEAQAATGFLLDPCTRQKLS 1950
1951 VDEAVDVGLVNEELRERLLKAERAATGYRDPATGDTIPLFQAMQKQLIEK 2000
2001 AEALRLLEVQVATGGVIDPQHHHRLPLETAYRRGCLHKDIYALISDQKHM 2050
2051 RKRFVDPNTQEKVSYRELQERCRPQEDTGWLLFPVNKAARDSEHIDDETR 2100
2101 RALEAEQVEITVGRFRGQKPTLWALLNSEYVTEEKKLQLVRMYRTHTRRA 2150
2151 LQTVAQLILELIEKQETSNKHLWFQGIRRQITASELLSSAIITEEMLQDL 2200
2201 ETGRSTTQELMEDDRVKRYLEGTSCIAGVLVPAKDQPGRQEKMSIYQAMW 2250
2251 KGVLRPGTALVLLEAQAATGFVIDPVRNLRLSVEEAVAAGVVGGEIQEKL 2300
2301 LSAERAVTGYTDPYTGQQISLFQAMQKDLIVREHGIRLLEAQIATGGVID 2350
2351 PVHSHRVPVDVAYRRGYFDEEMNRVLADPSDDTKGFFDPNTHENLTYVQL 2400
2401 LRRCVPDPDTGLYMLQLAGRGSAVHQLSEELRCALRDARVTPGSGALQGQ 2450
2451 SVSVWELLFYREVSEDRRQDLLSRYRAGTLTVEELGATLTSLLAQAQAQA 2500
2501 RAEAEAGSPRPDPREALRAATMEVKVGRLRGRAVPVWDVLASGYVSRAAR 2550
2551 EELLAEFGSGTLDLPALTRRLTAIIEEAEEAPGARPQLQDAWRGPREPGP 2600
2601 AGRGDGDSGRSQREGQGEGETQEAAAAAAAAAARRQEQTLRDATMEVQRG 2650
2651 QFQGRPVSVWDVLFSSYLSEARRDELLAQHAAGALGLPDLVAVLTRVIEE 2700
2701 TEERLSKVSFRGLRRQVSASELHTSGILGPETLRDLAQGTKTLQEVTEMD 2750
2751 SVKRYLEGTSCIAGVLVPAKDQPGRQEKMSIYQAMWKGVLRPGTALVLLE 2800
2801 AQAATGFVIDPVRNLRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTDPY 2850
2851 TGQQISLFQAMQKDLIVREHGIRLLEAQIATGGVIDPVHSHRVPVDVAYQ 2900
2901 RGYFDEEMNRVLADPSDDTKGFFDPNTHENLTYVQLLRRCVPDPDTGLYM 2950
2951 LQLAGRGSAVHQLSEELRCALRDARVTPGSGALQGQSVSVWELLFYREVS 3000
3001 EDRRQDLLSRYRAGTLTVEELGATLTSLLAQAQAQARAEAEAGSPRPDPR 3050
3051 EALRAATMEVKVGRLRGRAVPVWDVLASGYVSRAAREELLAEFGSGTLDL 3100
3101 PALTRRLTAIIEEAEEAPGARPQLQDAWRGPREPGPAGRGDGDSGRSQRE 3150
3151 GQGEGETQEAAAAARRQEQTLRDATMEVQRGQFQGRPVSVWDVLFSSYLS 3200
3201 EARRDELLAQHAAGALGLPDLVAVLTRVIEETEERLSKVSFRGLRCQVSA 3250
3251 SELHTSGILGPETLRDLAQGTKTLQEVTEMDSVKRYLEGTSCIAGVLVPA 3300
3301 KDQPGRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFVIDPVRNLRLSV 3350
3351 EEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGQQISLFQAMQKDLIVRE 3400
3401 HGIRLLEAQIATGGVIDPVHSHRVPVDVAYRRGYFDEEMNRVLADPSDDT 3450
3451 KGFFDPNTHENLTYVQLLRRCVPDPDTGLYMLQLAGRGSAVHQLSEELRC 3500
3501 ALRDARVTPGSGALQGQSVSVWELLFYREVSEDRRQDLLSRYRAGTLTVE 3550
3551 ELGATLTSLLAQAQAQARAEAEAGSPRPDPREALRAATMEVKVGRLRGRA 3600
3601 VPVWDVLASGYVSRAAREELLAEFGSGTLDLPALTRRLTAIIEEAEEAPG 3650
3651 ARPQLQDAWRGPREPGPAGRGDGDSGRSQREGQGEGETQEAAAATAAARR 3700
3701 QEQTLRDATMEVQRGQFQGRPVSVWDVLFSSYLSEARRDELLAQHAAGAL 3750
3751 GLPDLVAVLTRVIEETEERLSKVSFRGLRRQVSASELHTSGILGPETLRD 3800
3801 LAQGTKTLQEVTEMDSVKRYLEGTSCIAGVLVPAKDQPGRQEKMSIYQAM 3850
3851 WKGVLRPGTALVLLEAQAATGFVIDPVRNLRLSVEEAVAAGVVGGEIQEK 3900
3901 LLSAERAVTGYTDPYTGQQISLFQAMQKDLIVREHGIRLLEAQIATGGVI 3950
3951 DPVHSHRVPVDVAYRRGYFDEEMNRVLADPSDDTKGFFDPNTHENLTYVQ 4000
4001 LLRRCVPDPDTGLYMLQLAGRGSAVHQLSEELRCALRDARVTPGSGALQG 4050
4051 QSVSVWELLFYREVSEDRRQDLLSRYRAGTLTVEELGATLTSLLAQAQAQ 4100
4101 ARAEAEAGSPRPDPREALRAATMEVKVGRLRGRAVPVWDVLASGYVSRAA 4150
4151 REELLAEFGSGTLDLPALTRRLTAIIEEAEEAPGARPQLQDAWRGPREPG 4200
4201 PAGRGDGDSGRSQREGQGEGETQEAAAATAAARRQEQTLRDATMEVQRGQ 4250
4251 FQGRPVSVWDVLFSSYLSEARRDELLAQHAAGALGLPDLVAVLTRVIEET 4300
4301 EERLSKVSFRGLRRQVSASELHTSGILGPETLRDLAQGTKTLQEVTEMDS 4350
4351 VKRYLEGTSCIAGVLVPAKDQPGHQEKMSIYQAMWKGVLRPGTALVLLEA 4400
4401 QAATGFVIDPVRNLRLSVEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYT 4450
4451 GQQISLFQAMQKDLIVREHGIRLLEAQIATGGVIDPVHSHRVPVDVAYRR 4500
4501 GYFDEEMNRVLAHPSDDTKGFFDPNTHENLTYVQLLRRCVPDPDTGLYML 4550
4551 QLAGRGSAVHQLSEELRCALRDARVMPGSGALQGQSVSVWELLFYREVSE 4600
4601 DRRQDLLSRYRAGTLTVEELGATLTSLLAQAQAQARAEAEAEAGSPRPDP 4650
4651 REALRAATMEVKVGRLRGRAVPVWDVLASGYVSGAAREELLAEFGSGTLD 4700
4701 LPALTRRLTAIIEEAEEAPGARPQLQDAWRGPREPGPAGRGDGDSGRSQR 4750
4751 EGQGEGETQEAAAAARRQEQTLRDATMEVQRGQFQGRPVSVWDVLFSSYL 4800
4801 SEAHRDELLAQHAAGALGLPDLVAVLTRVIEETEERLSKVSFRGLRRQVS 4850
4851 ASELHTSGILGPETLRDLAQGTKTLQEVTEMDSVKRYLEGTSCIAGVLVP 4900
4901 AKDQPGRQEKMSIYQAMWKGVLRPGTALVLLEAQAATGFVIDPVRNLRLS 4950
4951 VEEAVAAGVVGGEIQEKLLSAERAVTGYTDPYTGQQISLFQAMQKDLIVR 5000
5001 EHGIRLLEAQIATGGVIDPVHSHRVPVDVAYRRGYFDEEMNRVLADPSDD 5050
5051 TKGFFDPNTHENLTYLQLLQRATLDPETGLLFLSLSLQ 5088
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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