 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NZR2 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MSEFLLALLTLSGLLPIARVLTVGADRDQQLCDPGEFLCHDHVTCVSQSW 50
51 LCDGDPDCPDDSDESLDTCPEEVEIKCPLNHIACLGTNKCVHLSQLCNGV 100
101 LDCPDGYDEGVHCQELLSNCQQLNCQYKCTMVRNSTRCYCEDGFEITEDG 150
151 RSCKDQDECAVYGTCSQTCRNTHGSYTCSCVEGYLMQPDNRSCKAKIEPT 200
201 DRPPILLIANFETIEVFYLNGSKMATLSSVNGNEIHTLDFIYNEDMICWI 250
251 ESRESSNQLKCIQITKAGGLTDEWTINILQSFHNVQQMAIDWLTRNLYFV 300
301 DHVGDRIFVCNSNGSVCVTLIDLELHNPKAIAVDPIAGKLFFTDYGNVAK 350
351 VERCDMDGMNRTRIIDSKTEQPAALALDLVNKLVYWVDLYLDYVGVVDYQ 400
401 GKNRHTVIQGRQVRHLYGITVFEDYLYATNSDNYNIVRINRFNGTDIHSL 450
451 IKIENAWGIRIYQKRTQPTVRSHACEVDPYGMPGGCSHICLLSSSYKTRT 500
501 CRCRTGFNLGSDGRSCKRPKNELFLFYGKGRPGIVRGMDLNTKIADEYMI 550
551 PIENLVNPRALDFHAETNYIYFADTTSFLIGRQKIDGTERETILKDDLDN 600
601 VEGIAVDWIGNNLYWTNDGHRKTINVARLEKASQSRKTLLEGEMSHPRGI 650
651 VVDPVNGWMYWTDWEEDEIDDSVGRIEKAWMDGFNRQIFVTSKMLWPNGL 700
701 TLDFHTNTLYWCDAYYDHIEKVFLNGTHRKIVYSGRELNHPFGLSHHGNY 750
751 VFWTDYMNGSIFQLDLITSEVTLLRHERPPLFGLQIYDPRKQQGDNMCRV 800
801 NNGGCSTLCLAIPGGRVCACADNQLLDENGTTCTFNPGEALPHICKAGEF 850
851 RCKNRHCIQARWKCDGDDDCLDGSDEDSVNCFNHSCPDDQFKCQNNRCIP 900
901 KRWLCDGANDCGSNEDESNQTCTARTCQVDQFSCGNGRCIPRAWLCDRED 950
951 DCGDQTDEMASCEFPTCEPLTQFVCKSGRCISSKWHCDSDDDCGDGSDEV 1000
1001 GCVHSCFDNQFRCSSGRCIPGHWACDGDNDCGDFSDEAQINCTKEEIHSP 1050
1051 AGCNGNEFQCHPDGNCVPDLWRCDGEKDCEDGSDEKGCNGTIRLCDHKTK 1100
1101 FSCWSTGRCINKAWVCDGDIDCEDQSDEDDCDSFLCGPPKHPCANDTSVC 1150
1151 LQPEKLCNGKKDCPDGSDEGYLCDECSLNNGGCSNHCSVVPGRGIVCSCP 1200
1201 EGLQLNKDNKTCEIVDYCSNHLKCSQVCEQHKHTVKCSCYEGWKLDVDGE 1250
1251 SCTSVDPFEAFIIFSIRHEIRRIDLHKRDYSLLVPGLRNTIALDFHFNQS 1300
1301 LLYWTDVVEDRIYRGKLSESGGVSAIEVVVEHGLATPEGLTVDWIAGNIY 1350
1351 WIDSNLDQIEVAKLDGSLRTTLIAGAMEHPRAIALDPRYGILFWTDWDAN 1400
1401 FPRIESASMSGAGRKTIYKDMKTGAWPNGLTVDHFEKRIVWTDARSDAIY 1450
1451 SALYDGTNMIEIIRGHEYLSHPFAVSLYGSEVYWTDWRTNTLSKANKWTG 1500
1501 QNVSVIQKTSAQPFDLQIYHPSRQPQAPNPCAANDGKGPCSHMCLINHNR 1550
1551 SAACACPHLMKLSSDKKTCYEMKKFLLYARRSEIRGVDIDNPYFNFITAF 1600
1601 TVPDIDDVTVIDFDASEERLYWTDIKTQTIKRAFINGTGLETVISRDIQS 1650
1651 IRGLAVDWVSRNLYWISSEFDETQINVARLDGSLKTSIIHGIDKPQCLAA 1700
1701 HPVRGKLYWTDGNTINMANMDGSNSKILFQNQKEPVGLSIDYVENKLYWI 1750
1751 SSGNGTINRCNLDGGNLEVIESMKEELTKATALTIMDKKLWWADQNLAQL 1800
1801 GTCSKRDGRNPTILRNKTSGVVHMKVYDKEAQQGSNSCQLNNGGCSQLCL 1850
1851 PTSETTRTCMCTVGYYLQKNRMSCQGIESFLMYSVHEGIRGIPLEPSDKM 1900
1901 DALMPISGTSFAVGIDFHAENDTIYWTDMGFNKISRAKRDQTWKEDIITN 1950
1951 GLGRVEGIAVDWIAGNIYWTDHGFNLIEVARLNGSFRYVIISQGLDQPRS 2000
2001 IAVHPEKGLLFWTEWGQMPCIGKARLDGSEKVVLVSMGIAWPNGISIDYE 2050
2051 ENKLYWCDARTDKIERIDLETGGNREMVLSGSNVDMFSVAVFGAYIYWSD 2100
2101 RAHANGSVRRGHKNDATETITMRTGLGVNLKEVKIFNRVREKGTNVCARD 2150
2151 NGGCKQLCLYRGNSRRTCACAHGYLAEDGVTCLRHEGYLLYSGRTILKSI 2200
2201 HLSDETNLNSPIRPYENPRYFKNVIALAFDYNQRRKGTNRIFYSDAHFGN 2250
2251 IQLIKDNWEDRQVIVENVGSVEGLAYHRAWDTLYWTSSTTSSITRHTVDQ 2300
2301 TRPGAFDREAVITMSEDDHPHVLALDECQNLMFWTNWNEQHPSIMRSTLT 2350
2351 GKNAQVVVSTDILTPNGLTIDYRAEKLYFSDGSLGKIERCEYDGSQRHVI 2400
2401 VKSGPGTFLSLAVYDNYIFWSDWGRRAILRSNKYTGGDTKILRSDIPHQP 2450
2451 MGIIAVANDTNSCELSPCALLNGGCHDLCLLTPNGRVNCSCRGDRILLED 2500
2501 NRCVTKNSSCNAYSEFECGNGECIDYQLTCDGIPHCKDKSDEKLLYCENR 2550
2551 SCRRGFKPCYNRRCIPHGKLCDGENDCGDNSDELDCKVSTCATVEFRCAD 2600
2601 GTCIPRSARCNQNIDCADASDEKNCNNTDCTHFYKLGVKTTGFIRCNSTS 2650
2651 LCVLPTWICDGSNDCGDYSDELKCPVQNKHKCEENYFSCPSGRCILNTWI 2700
2701 CDGQKDCEDGRDEFHCDSSCSWNQFACSAQKCISKHWICDGEDDCGDGLD 2750
2751 ESDSICGAITCAADMFSCQGSRACVPRHWLCDGERDCPDGSDELSTAGCA 2800
2801 PNNTCDENAFMCHNKVCIPKQFVCDHDDDCGDGSDESPQCGYRQCGTEEF 2850
2851 SCADGRCLLNTQWQCDGDFDCPDHSDEAPLNPKCKSAEQSCNSSFFMCKN 2900
2901 GRCIPSGGLCDNKDDCGDGSDERNCHINECLSKKVSGCSQDCQDLPVSYK 2950
2951 CKCWPGFQLKDDGKTCVDIDECSSGFPCSQQCINTYGTYKCLCTDGYEIQ 3000
3001 PDNPNGCKSLSDEEPFLILADHHEIRKISTDGSNYTLLKQGLNNVIAIDF 3050
3051 DYREEFIYWIDSSRPNGSRINRMCLNGSDIKVVHNTAVPNALAVDWIGKN 3100
3101 LYWSDTEKRIIEVSKLNGLYPTILVSKRLKFPRDLSLDPQAGYLYWIDCC 3150
3151 EYPHIGRVGMDGTNQSVVIETKISRPMALTIDYVNRRLYWADENHIEFSN 3200
3201 MDGSHRHKVPNQDIPGVIALTLFEDYIYWTDGKTKSLSRAHKTSGADRLS 3250
3251 LIYSWHAITDIQVYHSYRQPDVSKHLCMINNGGCSHLCLLAPGKTHTCAC 3300
3301 PTNFYLAADNRTCLSNCTASQFRCKTDKCIPFWWKCDTVDDCGDGSDEPD 3350
3351 DCPEFRCQPGRFQCGTGLCALPAFICDGENDCGDNSDELNCDTHVCLSGQ 3400
3401 FKCTKNQKCIPVNLRCNGQDDCGDEEDERDCPENSCSPDYFQCKTTKHCI 3450
3451 SKLWVCDEDPDCADASDEANCDKKTCGPHEFQCKNNNCIPDHWRCDSQND 3500
3501 CSDNSDEENCKPQTCTLKDFLCANGDCVSSRFWCDGDFDCADGSDERNCE 3550
3551 TSCSKDQFRCSNGQCIPAKWKCDGHEDCKYGEDEKSCEPASPTCSSREYI 3600
3601 CASDGCISASLKCNGEYDCADGSDEMDCVTECKEDQFRCKNKAHCIPIRW 3650
3651 LCDGIHDCVDGSDEENCERGGNICRADEFLCNNSLCKLHFWVCDGEDDCG 3700
3701 DNSDEAPDMCVKFLCPSTRPHRCRNNRICLQSEQMCNGIDECGDNSDEDH 3750
3751 CGGKLTYKARPCKKDEFACSNKKCIPMDLQCDRLDDCGDGSDEQGCRIAP 3800
3801 TEYTCEDNVNPCGDDAYCNQIKTSVFCRCKPGFQRNMKNRQCEDLNECLV 3850
3851 FGTCSHQCINVEGSYKCVCDQNFQERNNTCIAEGSEDQVLYIANDTDILG 3900
3901 FIYPFNYSGDHQQISHIEHNSRITGMDVYYQRDMIIWSTQFNPGGIFYKR 3950
3951 IHGREKRQANSGLICPEFKRPRDIAVDWVAGNIYWTDHSRMHWFSYYTTH 4000
4001 WTSLRYSINVGQLNGPNCTRLLTNMAGEPYAIAVNPKRGMMYWTVVGDHS 4050
4051 HIEEAAMDGTLRRILVQKNLQRPTGLAVDYFSERIYWADFELSIIGSVLY 4100
4101 DGSNSVVSVSSKQGLLHPHRIDIFEDYIYGAGPKNGVFRVQKFGHGSVEY 4150
4151 LALNIDKTKGVLISHRYKQLDLPNPCLDLACEFLCLLNPSGATCVCPEGK 4200
4201 YLINGTCNDDSLLDDSCKLTCENGGRCILNEKGDLRCHCWPSYSGERCEV 4250
4251 NHCSNYCQNGGTCVPSVLGRPTCSCALGFTGPNCGKTVCEDFCQNGGTCI 4300
4301 VTAGNQPYCHCQPEYTGDRCQYYVCHHYCVNSESCTIGDDGSVECVCPTR 4350
4351 YEGPKCEVDKCVRCHGGHCIINKDSEDIFCNCTNGKIASSCQLCDGYCYN 4400
4401 GGTCQLDPETNVPVCLCSTNWSGTQCERPAPKSSKSDHISTRSIAIIVPL 4450
4451 VLLVTLITTLVIGLVLCKRKRRTKTIRRQPIINGGINVEIGNPSYNMYEV 4500
4501 DHDHNDGGLLDPGFMIDPTKARYIGGGPSAFKLPHTAPPIYLNSDLKGPL 4550
4551 TAGPTNYSNPVYAKLYMDGQNCRNSLGSVDERKELLPKKIEIGIRETVA 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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