 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88778 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MGNEASLEGGAGEGPLPPGGSGLGPGPGAGKPPSALAGGGQLPVAGAARA 50
51 AGPPTPGLGLVPGPGPGPGPGSVSRRLDPKEPLGSQRATSPTPKQASATA 100
101 PGRESPRETRAQGLSGQEAEGPRRTLQVDSRTQRSGRSPSVSPDRGSTPT 150
151 SPYSVPQIAPLPSSTLCPICKTSDLTSTSSQPNFNTCTQCHNKVCNQCGF 200
201 NPNPHLTQVKEWLCLNCQMQRALGMDMTTAPRSKSQQQLHSPALSPAHSP 250
251 AKQPLGKPEQERSRSPGATQSGPRQAEAARATSVPGPTQATAPPEVGRVS 300
301 PQPPLSTKPSTAEPRPPAGEAQGKSATTVPSGLGAAEQTQGGLTGKLFGL 350
351 GASLLTQASTLMSVQPEADTQGQPSPSKGPPKIVFSDASKEAGPRPPGSG 400
401 PGPGPTPGAKTEPGPRTGPGSGPGALAKTGGTPSPKHGRADHQAASKAAA 450
451 KPKTMPKERAACPLCQAELNVGSRGPANYNTCTACKLRVCTLCGFNPTPH 500
501 LVEKTEWLCLNCQTKRLLEGSLGEPAPLPLPTPQEPPAGVPQRAAGASPL 550
551 KQKGPQGPGQPSGSLPPKASPQAAKASPQAAKASPQAKPLRASEPSKTSS 600
601 SAPEKKTGIPVKAEPVPKPPPETAVPPGTPKAKSGVKRTDPATPVVKPVP 650
651 EAPKSGEAEEPVPKPYSQDLSRSPQSLSDTGYSSDGVSSSQSEITGVVQQ 700
701 EVEQLDSAGVTGPRPPSPSELHKVGSSMRPSLEAQAVAPSGEWSKPPSGS 750
751 AVEDQKRRPHSLSIMPEAFDSDEELGDILEEDDSLAMGRQREQQDTAESS 800
801 DDFGSQLRHDYVEDSSEGGLSPLPPQPPARADMTDEEFMRRQILEMSAEE 850
851 DNLEEDDTAVSGRGLAKHGAQKASARPRPESSQESVALPKRRLPHNATTG 900
901 YEELLSEEGPAEPTDGALQGGLRRFKTIGLNSTGRLWSTSLDLGQGSDPN 950
951 LDREPELEMESLTGSPEDRSRGEHSSTLPASTPSYTSGTSPTSLSSLEED 1000
1001 SDSSPSRRQRLEEAKQQRKARHRSHGPLLPTIEDSSEEEELREEEELLRE 1050
1051 QEKMREVEQQRIRSTARKTRRDKEELRAQRRRERSKTPPSNLSPIEDASP 1100
1101 TEELRQAAEMEELHRSSCSEYSPSPSLDSEAETLDGGPTRLYKSGSEYNL 1150
1151 PAFMSLCSPTETPSGSSTTPSSGRPLKSAEEAYEDMMRKAELLQRQQGQA 1200
1201 AGARGPHGGPSQPTGPRSQGSFEYQDTLDHDYGGRASQPAADGTPAGLGA 1250
1251 TVYEEILQTSQSIARMRQASSRDLAFTEDKKKEKQFLNAESAYMDPMKQN 1300
1301 GGPLTPGTSPTQLAAPVSFPTSTSSDSSGGRVIPDVRVTQHFAKEPQEPL 1350
1351 KLHSSPASPSLASKEVGMTFSQGPGTPATTAMAPCPASLPRGYMTPAGPE 1400
1401 RSPSTSSTIHSYGQPPTTANYGSQTEELPHAPSGPAGSGRASREKPLSGG 1450
1451 DGEVGPPQPSRGYSYFTGSSPPLSPSTPSESPTFSPSKLGPRATAEFSTQ 1500
1501 TPSLTPSSDIPRSVGTPSPMVAQGTQTPHRPSTPRLVWQQSSQEAPVMVI 1550
1551 TLASDASSQTRMVHASASTSPLCSPTDSQPASHSYSQTTPPSASQMPSEP 1600
1601 AGPPGFPRAPSAGVDGPLALYGWGALPAENISLCRISSVPGTSRVEPGPR 1650
1651 PPGTAVVDLRTAVKPTPIILTDQGMDLTSLAVEARKYGLALDPVPGRQST 1700
1701 AVQPLVINLNAQEQTHTFLATATTVSITMASSVLMAQQKQPVVYGDPFQS 1750
1751 RLDFGQGSGSPVCLAQVKQVEQAVQTAPYRGGPRGRPREAKFARYNLPNQ 1800
1801 VTPLARRDILITQMGTAQSVSLKPGPVPEPGAEPHRATPAELRAHALPGT 1850
1851 RKPHTVVVQMGEGAAGTVTTLLPEEPAGALDLTGMRPESRLACCDMAYKF 1900
1901 PFGSSCTGTFHPAPSAPDKSVTDAALPGQSSGPFYSPRDPEPPEPLTFRA 1950
1951 QGVVGPGPHEEQRPYPQGLPGRLYSSMSDTNLAEAGLNYHAQRIGQLFQG 2000
2001 PGRDSAVDLSSLKHSYSLGFADGRYLGQGLQYGSFTDLRHPTDLLSHPLP 2050
2051 MRPYSSVSNIYSDHRYGPRGDAVGFQEASLAQYSATTAREISRMCAALNS 2100
2101 MDQYGGRHGGGSGGPDLVPYQPQHGPGLNAPQGLASLRSGLLGNPTYPEG 2150
2151 QPSPGNLAQYGPAASQGTAVRQLLPSTATVRAADGMIYSTINTPIAATLP 2200
2201 ITTQPASVLRPMVRGGMYRPYGSGGVTAVPLTSLTRVPMIAPRVPLGPAG 2250
2251 LYRYPAPSRFPIASTIPPAEGPVYLGKPAAAKASGAGGPPRPELPAGGAR 2300
2301 EEPLSTTAPPAVIKEAPVAQAPAPPPGQKPAGDAAAGSGSGVLGRPVMEK 2350
2351 EEASQEDRQRKQQEQLLQLERERVELEKLRQLRLQEELERERVELQRHRE 2400
2401 EEQLLVQRELQELQTIKHHVLQQQQEERQAQFALQREQLAQQRLQLEQIQ 2450
2451 QLQQQLQQQLEEQKQRQKAPFPATCEAPSRGPPPAATELAQNGQYWPPLT 2500
2501 HTAFIAVAGTEGPGQAREPVLHRGLPSSASDMSLQTEEQWEAGRSGIKKR 2550
2551 HSMPRLRDACEPESGPDPSTVRRIADSSVQTDDEEGEGRYLLTRRRRTRR 2600
2601 SADCSVQTDDEDNAEWEQPVRRRRSRLSRHSDSGSDSKHEASASSSAAAA 2650
2651 AARAMSSVGIQTISDCSVQTEPEQLPRVSPAIHITAATDPKVEIVRYISA 2700
2701 PEKTGRGESLACQTEPDGQAQGVAGPQLIGPTAISPYLPGIQIVTPGALG 2750
2751 RFEKKKPDPLEIGYQAHLPPESLSQLVSRQPPKSPQVLYSPVSPLSPHRL 2800
2801 LDTSFASSERLNKAHVSPQKQFIADSTLRQQTLPRPMKTLQRSLSDPKPL 2850
2851 SPTAEESAKERFSLYQHQGGLGSQVSALPPNGLVRKVKRTLPSPPPEEAH 2900
2901 LPLAGQVPSQLYAASLLQRGLAGPTTVPATKASLLRELDRDLRLVEHEST 2950
2951 KLRKKQAELDEEEKEIDAKLKYLELGITQRKESLAKDRVGRDYPPLRGLG 3000
3001 EHRDYLSDSELNQLRLQGCTTPAGQYVDYPASAAVPATPSGPTAFQQPRF 3050
3051 PPAATQYTAGSSGPTQNGFLAHQAPTYTGPSTYPAPTYPPGTSYPAEPGL 3100
3101 PSQPAFHPTGHYAAPTPMPTTQSAPFPVQADSHAAHQKPRQTSLADLEQK 3150
3151 VPTNYEVISSPAVTVSSTPSETGYSGPAVSSSYEHGKAPEHPRGGDRSSV 3200
3201 SQSPAPTYPSDSHYTSLEQNVPRNYVMIDDISELTKDSTPTASDSQRPEP 3250
3251 LGPGGVSGRPGKDPGEPAVLEGPTLPCCYGRGEEESEEDSYDPRGKSGHH 3300
3301 RSMESNGRPASTHYYSDSDYRHGARADKYGPGPMGPKHPSKNLAPAAISS 3350
3351 KRSKHRKQGMEQKISKFSPIEEAKDVESDLASYPPPTVSSSLTSRSRKFQ 3400
3401 DEITYGLKKNVYEQQRYYGVSSRDTAEEDDRMYGGSSRSRVASAYSGEKL 3450
3451 SSHDFSSRSKGYERERETAQRLQKAGPKPSSLSMAHGRARPPMRSQASEE 3500
3501 ESPVSPLGRPRPAGGALPPGDTCPQFCSSHSMPDVQEHVKDGPRAHAYKR 3550
3551 EEGYILDDSHCVVSDSEAYHLGQEETDWFDKPRDARSDRFRHHGGHTVSS 3600
3601 SQKRGPARHSYHDYDEPPEEGLWPHDEGGPGRHTSAKEHRHHGDHGRHSG 3650
3651 RHAGEEPGRRAARPHARDMGRHETRPHPQASPAPAMQKKGQPGYPSSADY 3700
3701 SQPSRAPSAYHHASDSKKGSRQAHSGPTVLQPKPEAQAQPQMQGRQAVPG 3750
3751 PQQSQPPSSRQTPSGTASRQPQTQQQQQQQQQQQQQQQQQQQQQQQQGLG 3800
3801 QQAPQQAPSQARLQQQSQPTTRSTAPAASHPAGKPQPGPTTAPGPQPAGL 3850
3851 PRAEQAGSSKPAAKAPQQGRAPQAQSAPGPAGAKTGARPGGTPGAPAGQP 3900
3901 AAEGESVFSKILPGGAAEQAGKLTEAVSAFGKKFSSFW 3938
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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