 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JI18 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MSQLLLAILTLSGLLPNAEVLIVGANQDQHLCDPGEFLCHDHVTCVSQSW 50
51 LCDGDPDCPDQSDESLDTCPEEVEIKCPLNHIACHGSSACVHLSKLCNGV 100
101 VDCPDGFDEGGHCQELLPSCQQLNCQFKCAMVRNATRCYCEDGFEVAEDG 150
151 RSCKDQDECSIYGICSQTCKNTYGSYACSCVEGYIMQSDNRSCKVKHEPT 200
201 DKAPMLLISSLETIELFYINGSKMTTLSSANRNEIHTLDFIYSEEMICWI 250
251 ESRESSNQLKCGQITKAGRLTDQRIINSLQSFQNVEQMAFDWLTRNIYFV 300
301 DHVSDRIFVCNFNGSVCVTLIESELHNPKAIAADPIAGKLFFTDYGNVPK 350
351 IERCDLDGMNRTRIVYSKAEQPSALALDLVNRLVYWVDLYLDYVGVVDYQ 400
401 GKNRHTIVQGRQVKHLYGITVFEDYLYATSSDNFNIIRINRFNGTDIHSI 450
451 IKMESARGIRTYQKRTQPTVRSHACEVDAYGMPGGCSHICLLSSSYKTRT 500
501 CRCRTGFNMGSDGRSCKRPKNELFLFYGKGRPGIVRGMDLNTKIADECMI 550
551 PIENLVNPRALDFHAEANYIYFADTTSFLIGRQKIDGTERETILKDDLDN 600
601 VEGIAVDWIGNNLYWTNDGHRKTINVARLEKASQSRKTLLEGGMSHPRAI 650
651 VVDPVNGWMYWTDWKEDKIDDSVGRIEKAWMDGVNRQVFVTSKMLWPNGL 700
701 TLDFHTSTLYWCDAYYDHIEKVFLNGTHRKVVYSGKELNHPFGLSHHGNY 750
751 VFWTDYMNGSIFQLDLMTNEVTLLRHERAPLFGLQIYDPRKQQGDNMCRI 800
801 NNGGCGTLCLAIPAGRVCACADNQLLDENGTTCTFNPEEIRFHICKPGEF 850
851 RCKNKHCIQARWKCDGDDDCLDGSDEDSVTCFNHSCPDDQFKCQNNRCIP 900
901 KRWLCDGANDCGSNEDESNQTCTARTCQADQFSCGNGRCIPTAWLCDRED 950
951 DCGDQTDEVASCEFPTCEPLTQFICKSGRCISNKWHCDTDDDCGDRSDEV 1000
1001 GCVHSCLDDQFRCSSGRCIPGHWACDGDNDCGDFSDETHINCTKEEARSP 1050
1051 AGCIGNEFQCRPDGNCIPDLWRCDGEKDCEDGSDEKGCNGTIRLCDHKTK 1100
1101 FSCRSTGRCINNAWVCDGDVDCEDQSDEEDCDSFLCGPPKYPCANDTSVC 1150
1151 LQPEKLCNGRKDCPDGSDEGDLCDECSLNNGGCSNHCSVVPGRGIVCSCP 1200
1201 EGHQLKKDNRTCEIVDYCASHLRCSQVCEQQKHMVKCSCYEGWALGTDGE 1250
1251 SCTSVDSFEAFIIFSIRHEIRRIDLHKGDYSLLVPGLRNTIALDFHFNQS 1300
1301 LLYWTDVVEDRIYRGKLSESGGVSAIEVVVEHGLATPEGLTVDWIAGNIY 1350
1351 WIDSNLDQIEVSKLDGSLRATLIAGAMEHPRAIALDPRYGILFWTDWDAN 1400
1401 FPRIESASMSGAGRKTIYKDMKTGAWPNGLTVDHFERRIVWTDARSDAIY 1450
1451 SAFYDGTNMIEIIRGHEYLSHPFAVSLYGSEVYWTDWRTNTLAKANKWTG 1500
1501 QNVSVIQKTSAQPFDLQIYHPSRQPQAPNPCAANEGRGPCSHLCLINHNR 1550
1551 SAACACPHLMKLSSDKKTCYEMKKFLLYARRSEIRGVDIDNPYVNFITAF 1600
1601 TVPDIDDVAVIDFDASEERLYWTDIKTQTITRAFINGTGLETVISRDIQS 1650
1651 IRGLAVDWVSRNLYWISSEFDETQINVARLDGSLKTSIIHGIDKPQCLAA 1700
1701 HPVRGKLYWTDGNTINMANMDGSNSKILFQNQKEPVGLSIDYVENKLYWI 1750
1751 SSGNGTINRCNLDGGNLEVIESMKEELTKATALTIMDKKLWWADQNLAQL 1800
1801 GTCNKRDGRNPSILRNKTSGVVHMKVYDKEAQQGSNSCQVNNGGCSQLCL 1850
1851 PTSETTRTCMCTVGYYLQKNRMSCQGIESFLMYSVHEGIRGIPLEPRDKV 1900
1901 DALMPISGAAFAVGIDFHAENDTIYWTDMGLNKISRAKRDQTWKEDVVTN 1950
1951 GLGRVEGIAVDWIAGNIYWTDHGFNLIEVARLNGSFRYVIISQGLDQPRS 2000
2001 IAVHPEKGFLFWTEWGQVPCIGKARLDGSEKVMIVSVGITWPNGISIDYE 2050
2051 ENKLYWCDARSDKIERIDLDTGANREVLLSGSNVDLFSVAVFGAYIYWSD 2100
2101 RAHANGSVRRGHKNDATETVTMRTGLGVNLKEIKIFNRVREKGTNVCAKE 2150
2151 NGGCQQLCLYRGNSRRTCACAHGYLAGDGVTCLRHEGYLLYSGRTILKSI 2200
2201 HLSDETNLNSPVRPYENPNYFKNIIALAFDYNQRREGTNRIFYSDAHFGN 2250
2251 IQLIKDNWEDRQVIVENVGSVEGLAYHRAWDTLYWTSSSTSSITRHTVDQ 2300
2301 TRPGAIDREAVITMSEDDHPHVLALDECQNLMFWTNWNEQHPSIMRATLT 2350
2351 GKNAHVVVSTDILTPNGLTIDHRAEKLYFSDGSLGKIERCEYDGSQRHVI 2400
2401 VKSGPGTFLSLAVYDSYIFWSDWGRRAILRSNKYTGGETKILRSDIPHQP 2450
2451 MGIIAVANDTNSCELSPCALLNGGCHDLCLLTPDGRVNCSCRGDRVLLAN 2500
2501 NRCVTKNSSCNIYSEFECGNGDCVDYVLTCDGIPHCKDKSDEKLLYCENR 2550
2551 SCRSGFKPCYNRRCVPHGKLCDGTNDCGDSSDELDCKVSTCSTVEFRCAD 2600
2601 GTCIPRSARCNQNMDCSDASDEKGCNNTDCTHFYKLGVKSTGFIRCNSTS 2650
2651 LCVLPSWICDGSNDCGDYSDELKCPVQNKHKCEENYFGCPSGRCILNTWV 2700
2701 CDGQKDCEDGLDELHCDSSCSWNQFACSVKKCISKHWICDGEDDCGDSLD 2750
2751 ESDSICGAVTCAADMFSCQGSHACVPQHWLCDGERDCPDGSDELSSAGCA 2800
2801 PNNTCDENAFMCHNKVCIPKQFVCDHDDDCGDGSDEFLQCGYRQCGPEEF 2850
2851 RCADGRCLVNTLWQCDGDFDCPDSSDEAPINPRCRSAEHSCNSSFFMCKN 2900
2901 GRCIPSDGLCDIRDDCGDGSDETNCHINECLSKKISGCSQDCQDLPVSYK 2950
2951 CKCWPGFQLKDDGKTCVDIDECSSGFPCSQQCINTYGTYKCHCAEGYETQ 3000
3001 PDNPNGCRSLSDEEPFLILADQHEIRKISTDGSNYTLLKQGLNNVIALDF 3050
3051 DYREEFIYWIDSSRPNGSRINRMCLNGSDIKVVHNTAVPNALAVDWIGKN 3100
3101 LYWSDTEKRIIEVSKLNGLYPTVLVSKRLKFPRDLSLDPRAGNLYWIDCC 3150
3151 EYPHIGRVGMDGTNQSVVIETKISRPMALTIDYVNHRLYWADENHIEFSN 3200
3201 MDGSHRHKVPNQDIPGVIALTLFEDYIYWTDGKTKSLSRVHKTSGADRLS 3250
3251 LINSWHAITDIQVYHSYRQPDVSKHLCTVNNGGCSHLCLLGPGKTHTCAC 3300
3301 PTNFYLAADNRTCLSNCTASQFRCKTDKCIPFWWKCDTVDDCGDGSDEPD 3350
3351 DCPEFKCQPGRFQCGTGLCALPAFICDGENDCGDNSDELNCDTHVCLAGQ 3400
3401 FKCTKNKKCIPVNLRCNGQDDCGDEEDEKDCPENSCSPDYFQCKTTKHCI 3450
3451 SKLWVCDEDPDCADASDEANCDKKTCGPHEFQCKNNNCIPDHWRCDNQND 3500
3501 CSDNSDEDNCKPQTCTLKDFLCSNGDCVSSRFWCDGEFDCADGSDEKNCE 3550
3551 TSCSKDQFQCSNGQCLSAKWKCDGHEDCKYGEDEKNCEPAFPVCSSSEYM 3600
3601 CASGGCLSASLKCNGEPDCVDGSDEMDCVIECKEDQFQCKNKAYCIPIRW 3650
3651 LCDGIYDCVDGSDEETCGRGGSICRDDEFLCNNSLCKLHFWVCDGEDDCG 3700
3701 DNSDEAPDMCVKFLCPPTRPYRCRNDRICLQLEKICNGINDCGDNSDEEH 3750
3751 CSGKLSLKSKPCKKDEFTCSNRNCIPMELQCDSLDDCGDGSDEQGCLKTP 3800
3801 IEHTCENNGNPCGDDAYCNQIKTSVFCRCKPGFQRNMKGRECADLNECLL 3850
3851 FGICSHHCLNTRGSYKCVCDQNFQEKNNSCIAKGSEDQALYIANDTDILG 3900
3901 FVYPFNYSGGHQQISHVEHNSRITGMDVHYQRNVIVWSTQFNPGGIFYKM 3950
3951 IDAREKRQANSGLICPEFKRPRDIAVDWVAGNVYWTDHSRMHWFSYYTTH 4000
4001 WTSLRYSINVGQLNGPNCTRLLTNMAGEPYAIAVNPKRGMMYWTVIGDHS 4050
4051 HIEEAAMDGTLRRVLVQKNLQRPTGLTVDHFGERIYWADFELSIIGSVLY 4100
4101 DGSSPVVSVSSKQGLLHPHRIDVFEDYIYGAGPKNGIFRVQKFGHGSVEV 4150
4151 LALGVDKTKSILVSHRYKQLNLPNPCLDLSCDFLCLLNPSGATCICPEGK 4200
4201 YMMNGTCHDDSLLDDSCKLTCENGGRCILNEKGDLRCHCWPSYSGGRCEV 4250
4251 NHCSNYCQNGGTCIPSTLGRPTCICALGFTGPNCGKAVCEDSCHNGGSCV 4300
4301 VTAGNQPYCHCQADYTGDRCQYYVCHHYCVNSESCTIGNDGSVECVCPTR 4350
4351 YEGPKCEIDKCVRCHGGHCIINKDNEDIFCNCTNGKIASSCQLCDGYCYN 4400
4401 GGTCQLDPETSIPVCVCSTNWSGTQCERPAPKSSKSEHISTRSIAIIVPL 4450
4451 VLLVTLVTTLVIGLVVCKRKRRTKTIRRQPIINGGINVEIGNPSYNMYEV 4500
4501 DHDHSDGGLLEPSFMIDPVKSRYIGGGSSAFKLPHTAPPIYLNSDLKGPL 4550
4551 TFGPTNYSNPVYAKLYMDGQNCRNSLASVDERKELLPKKIEIGIRETVA 4599
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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