 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q04833 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MILRLLIFTALAVTTANSSTRQQSTFHSIQVDSPPSVRSRIISASVNTAS 50
51 SVCNENDFRCNDGKCIRTEWKCDGSGDCSDGEDEKDCPHPGCKSDQWQCD 100
101 TYTWHSVSCIAEYQRCDNITDCADGSDEKDCPASTVDCSSQNVFMCADGR 150
151 QCFDVSKKCDGKYDCRDLSDEKDSCSRNHTACFQYQFRCADKTQCIQKSW 200
201 VCDGSKDCADGSDEPDTCEFKKCTANEFQCKNKRCQPRKFRCDYYDDCGD 250
251 NSDEDECGEYRCPPGKWNCPGTGHCIDQLKLCDGSKDCADGADEQQCSQN 300
301 LCPSLGCQAGCHPSPHGGECTCPSGYKLDDRFHRTCSDINECAEFGYCDQ 350
351 LCANHRPGFTCSCLGDCFTLQMEHGPGKDNLTMRGYCVSNNADKMKLFVA 400
401 RREGLYRLNPKNPDEEVKKLASGEFIYGIDFDYGDRKIFWTDRLAHSAFS 450
451 ADVDDEGEISQIKKLSLKSLVYPRCLAVDWITNTLYIIESGSRRIDVSSY 500
501 DGERRTVLLADGLTLPLDIALDPLRGEMFFTNQLKLEAAAMDGTNRRTLV 550
551 NTHTHQVSGIVVDITAKRVYWVDPKVDRLESIDYQGNDRRIVAQGMNVVP 600
601 HPFGLALFDQYLYWTDWTRLGVIQVEKFGSDTKLLWSNTENNVFPMGISA 650
651 YHPMAQPGPGQSECLAMKIENPCTNADCEGMCILSKDNGGFGVGYKCACP 700
701 IGQKLVNGKRCIDSIDYLLFSSNKIVRGIFPEINEKALAEAVLPISPISQ 750
751 RRIGMYFEVECDVHGNSFFYADIMDNTIYRIRPDGEGAAPVLVTHNDGLF 800
801 SMSFDWISKQLYYVDNIRNSLEVVKIGETGLVHPDELVRRQLITELRDPV 850
851 SVVVHPWKGLLFYAEAMRPAAIYRCHIDGQNCQVIRNTTLGRPSEMAIDF 900
901 AENRLCWGDTLLKTISCMDFDGKNVVKLDIDNPIPVAITIMNEYIYYVHQ 950
951 RPYSIRRVHKKNGGGSKIVREFGADERSIFSLKACSHQNQPIPDDSREHP 1000
1001 CRASQCTQLCFATPSESHPNELEAKCACRQGFMINKENNHSCQKDPAEKI 1050
1051 EQLCSSNSTQFQCKNGRCIPKEWKCDGENDCLDESDEIDEKGDKCFHETE 1100
1101 CAENTIKCRNTKKCIPAQYGCDGDNDCGDYSDEDVKYCKDGQKPVCAAKK 1150
1151 FQCDNHRCIPEQWKCDSDNDCGDGSDEKLEMCGNATCAANQFSCANGRCI 1200
1201 PIYWLCDGDNDCYDGTDEDKERCPPVQCSALQFRCANGRQCVPLRNHCDG 1250
1251 QSDCEDGSDEDSCAVTAESCTPDQFKCVSSGLCIPASWKCDGQQDCDDGS 1300
1301 DEPKFGCTSGRQCSSDQFKCGNGRCILNNWLCDGENDCGDGSDESSERGC 1350
1351 KTSMNARKCPFEHVACENDQETCIPLHQLCDGKTHCPGGTDEGGRCARDL 1400
1401 CSADRAGCSFKCHNSPNGPICSCPFGEQLVNKTKCEPENECLDSSSCSQR 1450
1451 CKDEKHGFTCSCDEGYELDVDKRTCKVADNVKDTRIYVSNRNRIYYSDHK 1500
1501 LDNWHTFGAIVENAIALAWDSLTDRIYWSDIREKKILSANRNGTNATVFI 1550
1551 ADGLDITEGIALDWVGRNLYWVDSSLNTIEVANLEDPKQRTLLVHQNVSQ 1600
1601 PRGIAVDPRKGVMFWTDWGQNPCIERASMDGTDRQIIVKTKIYWPNTIAL 1650
1651 DYTTDRVYFADSKLDFIDFVNYDGSGRTQVLASSKFVQHPHALAIFEDMM 1700
1701 YYSDRRLQKLQVYPKYPNGTTSEYPSHTFSKALGVVAVHPVLQPVIKNNP 1750
1751 CSTNPCSHLCLLNNKNTFTCKCPMGEKLDASGKKCIDDAKPFLVIIQKTN 1800
1801 VFGIEMNSASEKETPVLAGMVPLSGLGNAFDAAYDALSEEMFILEHTNHA 1850
1851 KTLAQITTDSAIYRSTVNGGNKTKMFSSAVPDDAYCLGFDWNGRNLVVGN 1900
1901 KITQTIEIIRTQGKQYRSVILSNDQSPTAVVTPVAIAVDADKGYVFWLDR 1950
1951 GGGAADAKVARAGLDGSNPLVIASNDLAELDHIAIDTTNTRVYFSEAKAG 2000
2001 RISSVTYDGQDRHYVLSDGGRQPNGLAFYGDRLFYADSAFDSIEVATING 2050
2051 DSQPPQWTHFKKDVENLANIKALQPRASSSGHPCHINNGNCDHICIPLMF 2100
2101 AQRTCTCANGYVKDGQTSCKLFDESFVIVATKTKVIGYPIDETQSKGVAM 2150
2151 EPIGGLSITGVDYDYESKTIYVAEASGINKGITAYTIGESSPRAVIRDSI 2200
2201 GSLTIKSLAIDWINYNMYFINHDAERTNIEVSKLDGTYRKILLTTKTETP 2250
2251 SSIAVDPVSRYLYWADQGQKPTIQRSFLDGSRREVIVSSGIAEPTDLVVD 2300
2301 VASKMIYWSDAKMDGIYRVRSTGGTPELVRSDIASAAGVALHGQNMYWTD 2350
2351 NRLEKLFRATSKPNQTSLLLSPTTVAASLKDIGDVAVFSSNNQPRASSPC 2400
2401 QITDNLRKSPCTQLCFATPGTQTPTCSCARGVLKGRTCEEPDTYIMFSDG 2450
2451 DKIIDVAIEPDVKASRPLKDPFPEISNLQTFDVDVNLRRVYFVVESPVGV 2500
2501 NISWFSMNNAENPRLVFGASKQPHAKEIRHISDMKLDWLTQKIYFTTGRG 2550
2551 GKVMAIDTAGEHLSTIASGDWTYALAIDPCSGLLFWSDSGYKTSGGLYEP 2600
2601 RIERSNLAGGSRKVIVSESISLPAAIAVDFRNQKIYWADVNRLNIEVADY 2650
2651 DGQNRKVIASGYRAKSLDIWDRWLYMSDPLSNGVFRIDKESGSGLENVVS 2700
2701 DRRIPGALRVFASESDVRTRNQVCNALTSQLCKTDNGGCDQLCTVVADDI 2750
2751 GLAASKVQCSCNDTYELVQEPGKDYPTQCVLRGSNSEPAKECLPPYNFQC 2800
2801 GDGSCILLGATCDSKPDCADASDENPNYCNTRSCPEDYNLCTNRRCIDSA 2850
2851 KKCNHIDDCGDGSDELDCPSAVACAEGTFPCSNGHCINQTKVCDGHNDCH 2900
2901 DEQVSDESLATCPGLPIDCRGVKVRCPNTNICIQPADLCDGYDDCGDKAD 2950
2951 ENQLFCMNQQCAQHYVRCPSGRCIPETWQCDGDNDCSDGWDETHTNCTDT 3000
3001 AGKKICVGDYLFQCDNLKCISRAFICDGEDDCGDGSDEHSRHGCGNRTCT 3050
3051 DQEFHCTSNAKLAQPKYECIPRAWLCDGDVTCAGGEDESTELCKTEKKEC 3100
3101 NKGEFRCSNQHCIHSTWECDGDNDCLDGSDEHANCTYSSCQPDFFQCANH 3150
3151 KCVPNSWKCDGNDDCEDGSDEKDCPKNSASAQKASKCSNGQFQCTSGECI 3200
3201 DDAKVCDRNFDCTDRSDESSLCFIDECSLAEKPLCEQKCMDMKIGYKCDC 3250
3251 FEGFAIDISDQKSCHNVNECYEGISGCSQKCDDKIGSYKCGCVDGYQLSS 3300
3301 DDHSCKRTEMEPEPFFLLANKHYIRKISIDGNKYELAAQGFDNVVSLDID 3350
3351 LTEKKAYLIDQGKLRLLRVDLDEMDSPLSSYETVLRHNVYGTEGIAVDWV 3400
3401 GRKLYMLNRQERSIRVCELDGRFCKTLIRDRIQQPKAIVVHPGKGYLFFT 3450
3451 EWSLQPYIGRIALDGSPELQDPIFKLAEHDLGWPNAIAIDYFSDRLFWGD 3500
3501 AHLNEIGFMDFDGNGRRHIPAQRTSHVSSMVVFDDYLYWADWNLREVLRC 3550
3551 DKWTGKNETILKKTVQLPNDLRIVHPMRQPAYPNPCGDNNGGCSHLCLIG 3600
3601 AGGNGYTCSCPDQFVLLSDQKTCEPNCTERQFACGGDDAKCIPKLWYCDG 3650
3651 EPDCRDGSDEPGESICGQRICPVGEFQCTNHNCTRPFQICDGNDDCGDSS 3700
3701 DEQNCDKACDPWMFKCAATGRCIPRRFTCDGDDDCGDRSDEADTLCMSAE 3750
3751 RNCTAEEFRCNNNKCIAKAWRCDNDDDCGDGSDETPECAQIECKKGWTRC 3800
3801 SSSYRCIPNWAFCNGQDDCRDNSDEDKQRCPTCDDVGEFRCATSGKCIPR 3850
3851 RWMCDTENDCGDNSDELDASCGGTTRPCSESEFRCNDGKCIPGSKVCDGT 3900
3901 IQCSDGLDESQCTLRRCLPGHRQCDDGTCIAEHKWCDRKKDCPNAADELH 3950
3951 CEDVSRRTCSPFEFECANSVCIPRKFMCDGDNDCGDNSDETSSECRSAQC 4000
4001 DPPLRFRCAHSRLCLNILQLCNGFNDCGPNDFSDEHLSMCSSFSEYGDCS 4050
4051 SDQFKCANGKCVNGTVACDRKDDCGDASDEIGCSKHGGKTSCEAFGNNGG 4100
4101 CKHICTDVRDGFYCHCRDGFRPDPQSPKECIDIDECAGNNTCTQLCLNTK 4150
4151 GSYLCRCHEDYENNVVVGSMTGKDCRAKGDAANVMIGADDSLVQLSLHGS 4200
4201 GTNRHAAAKANDDDNDIIGIAFDPRKELMYWIDGSERTIYRSAIANGNQS 4250
4251 HEGQKLDVDFAAMGVVPTAIAVDYTTGNLFIAAVSENIENGLVTARKKRM 4300
4301 SEPIDNQNTGFIFVCLPDGRYLKKIVAGHLQQPTALITAPSAGRICYSDA 4350
4351 GLHAKIECADMDGTHRQIIVKDLVFSPTSMAIDEGKGNRIYWVDPKYRRV 4400
4401 DAVNIDGSERTTVVHDRHIPYAVDVFENHIYWLSRESKTLYVQDKFGRGR 4450
4451 VSVLASDLEDGHTVRVSQKYAKDTQRTVSGCERAQCSHLCVSLPSTGFAC 4500
4501 LCPDGIVPQLDGSCATQHVEALTMPKQCKCTNGGKCRLDGSCECTSDFEG 4550
4551 DQCEKESSVSRKIIGTLSENFITVLLYILAFLFAFGLIGFCALNLYKRRQ 4600
4601 LLFKKNEAADGSVSFHGNVISFSNPVLENKQDAPGSEFNMQQMTSMHDDS 4650
4651 TTFTNPVYELEDVDMSSPPPSNDQPSTSASAMSPNRPSTSAASSFVPPTF 4700
4701 DQDEIELKTADEIIVPKAEISKPPIPARPKKEKADPLRVDNPLYDPDSEV 4750
4751 SDV 4753
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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