SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q9VW71 from www.uniprot.org...

The NucPred score for your sequence is 0.56 (see score help below)

   1  MFTMKIKKYVTPVKRKAFTILQWISLLCSLWLIPTVQSKADEKHTATLEY    50
  51  RLENQLQDLYRFSHSVYNVTIPENSLGKTYAKGVLHERLAGLRVGLNAEV   100
 101  KYRIISGDKEKLFKAEEKLVGDFAFLAIRTRTNNVVLNREKTEEYVIRVK   150
 151  AHVHLHDRNVSSYETEANIHIKVLDRNDLSPLFYPTQYTVVIPEDTPKYQ   200
 201  SILKVTADDADLGINGEIYYSLLMDSEYFAIHPTTGEITLLQQLQYAENS   250
 251  HFELTVVAYDRGSWVNHQNHQASKTKVSISVKQVNFYAPEIFTKTFSSVT   300
 301  PTSNPLIYGIVRVNDKDTGINGNIGRLEIVDGNPDGTFLLKAAETKDEYY   350
 351  IELNQFAHLNQQHFIYNLTLLAEDLGTPRRFAYKSVPIQIKPESKNIPIF   400
 401  TQEIYEVSIPETAPINMPVIRLKVSDPDLGKNALVYLEIVGGNEGDEFRI   450
 451  NPDSGMLYTAKQLDAEKKSSYTLTVSAIDQANVGSRKQSSAKVKISVQDM   500
 501  NDNDPIFENVNKVISINENNLAGSFVVKLTAKDRDSGENSYISYSIANLN   550
 551  AVPFEIDHFSGIVKTTSLLDFETMKRNYELIIRASDWGLPYRRQTEIKLS   600
 601  IVVKDINDNRPQFERVNCYGKVTKSAPMGTEVFVTSAIDFDAGDIISYRL   650
 651  SDGNEDGCFNLDPTSGSLSISCDLKKTTLTNRILKVSATDGTHFSDDLII   700
 701  NVHLMPEDLGGDSSILHGFGSFECRETGVARRLAETLSLAEKNNVKSASP   750
 751  SVFSDLSLTPSRYGQNVHRPEFVNFPQELSINESVQLGETVAWIEAKDRD   800
 801  LGYNGKLVFAISDGDYDSVFRIDPDRGELQIIGYLDRERQNEYVLNITVY   850
 851  DLGNPTKSTSKMLPITILDVNDNRPVIQKTLATFRLTESARIGTVVHCLH   900
 901  ATDADSGINAQVTYALSVECSDFTVNATTGCLRLNKPLDREKQDNYALHI   950
 951  TAKDGGSPVLSSEALVYVLVDDVNDNAPVFGVQEYIFKVREDLPRGTVLA  1000
1001  VIEAVDEDIGPNAEIQFSLKEETQDEELFRIDKHTGAIRTQGYLDYENKQ  1050
1051  VHNLIVSAIDGGDPSLTSDMSIVIMIIDVNENRFAPEFDDFVYEGKVKEN  1100
1101  KPKGTFVMNVTARDMDTVDLNSKITYSITGGDGLGIFAVNDQGSITSLSQ  1150
1151  LDAETKNFYWLTLCAQDCAIVPLSNCVEVYIQVENENDNIPLTDKPVYYV  1200
1201  NVTEASVENVEIITLKAFDPDIDPTQTITYNIVSGNLVGYFEIDSKTGVI  1250
1251  KTTERKLDRENQAEHILEVAISDNGSPVLSSTSRIVVSVLDINDNSPEFD  1300
1301  QRVYKVQVPSSATVNQSIFQVHAIDSDSGENGRITYSIKSGKGKNKFRID  1350
1351  SQRGHIHIAKPLDSDNEFEIHIKAEDNGIPKKSQTARVNIVVVPVNPNSQ  1400
1401  NAPLIVRKTSENVVDLTENDKPGFLVTQILAVDDDNDQLWYNISNGNDDN  1450
1451  TFYIGQDNGNILLSKYLDYETQQSYNLTISVTDGTFTAFTNLLVQVIDIN  1500
1501  DNPPQFAKDVYHVNISENIEEESVIMQLHATDRDEDKKLFYHLHATQDPS  1550
1551  SLALFRIDSISGNVIVTQRLDFEKTAQHILIVFVKDQGAPGKRNYAKIIV  1600
1601  NVHDHNDHHPEFTAKIIQSKVPESAAIGSKLAEVRAIDRDSGHNAEIQYS  1650
1651  IITGNVGSVFEIDPTFGIITLAGNLNINKIQEYMLQVKAVDLGNPPLSSQ  1700
1701  IPVHIIVTMSENDPPKFPTNNIAIEIFENLPIGTFVTQVTARSSSSIFFN  1750
1751  IISGNINESFRINPSTGVIVINGNIDYESIKVFNLTVKGTNMAAESSCQN  1800
1801  IIIHILDANDNIPYFVQNEYVGALPESAAIGSYVLKVHDSSKDHLTLQVK  1850
1851  DADVGVNGMVEYHIVDDLAKNFFKIDSTTGAIELLRQLDYETNAGYTFDV  1900
1901  TVSDMGKPKLHSTTTAHVTIRVINVNDCPPVFNERELNVTLFLPTFENVF  1950
1951  VRQVSAKDADNDTLRFDIVDGNTNECFQIEKYTGIITTRNFEILNNENDR  2000
2001  DYALHVRASDGIFSAILIVKIKVLSAIDSNFAFQRESYRFSAFENNTKVA  2050
2051  TIGLVNVIGNTLDENVEYRILNPTQLFDIGISSGALKTTGVIFDREVKDL  2100
2101  YRLFVEAKSMLYDGMNSNVRRAVTSIDISVLDVNDNCPLFVNMPYYATVS  2150
2151  IDDPKGTIIMQVKAIDLDSAENGEVRYELKKGNGELFKLDRKSGELSIKQ  2200
2201  HVEGHNRNYELTVAAYDGAITPCSSEAPLQVKVIDRSMPVFEKQFYTVSV  2250
2251  KEDVEMYSALSVSIEAESPLGRSLIYTISSESQSFEIDYNTGSIFVVNEL  2300
2301  DYEKISSHDVSIRATDSLSGVYAEVVLSVSIMDVNDCYPEIESDIYNLTI  2350
2351  PENASFGTQILKINATDNDSGANAKLSYYIESINGQNNSELFYIDVTDGN  2400
2401  LYLKTPLDYEQIKYHHIVVNVKDHGSPSLSSRSNVFITVKDLNDNAPCFV  2450
2451  EPSYFTKVSVAAVRGQFVALPKAYDKDISDTDSLEYKIVYGNELQTYSID  2500
2501  KLTGVISLQNMLNFTDKSSTVLNISVSDGVHTAYARLKISLLPENVYSPL  2550
2551  FDQSTYEAQVPENLLHGHNIITVKASDGDFGTYANLYYEIVSEEMKKIFL  2600
2601  IDQTTGVITSKVTFDREKKDEYVVLLKVSDGGGKFGFASLKVIVVDVNDN  2650
2651  VPYFLLKEYKMVVSTTVEANQTILTVKAKDDDIVDNGSVHFQIVQKSNDK  2700
2701  AVKDVIEINEKTGDIVFKSKAESYGVNSYQFFVRASDRGEPQFHSEVPVS  2750
2751  IEIIETDANIPTFEKSSVLLKIIESTPPGTVLTKLHMIGNYTFKFSIAAD  2800
2801  QDHFMISDSGELILQQTLDREQQESHNLIVVAETSTVPVFFAYADVLIDV  2850
2851  RDENDNYPKFDNTFYSASVAENSEKVISLVKVSATDADTGPNGDIRYYLE  2900
2901  SDTENIQNIFDIDIYSGWITLLTSLDREVQSEYNFKVIAADNGHPKHDAK  2950
2951  VPVTIKIVDYNDNAPVFKLPIEGLSVFENALPGTVLINLLLIDPDIEKQE  3000
3001  MDFFIVSGDKQAQFQIGKSGELFIAKPLDREQLMFYNLSIIATDGKFTAK  3050
3051  ANVEIDVKDINDNTPYCLKPRYHISTNESISIGTTLVEVKAIDFDFQSKL  3100
3101  RFYLSGKGADDFSIGKESGILKVASALDRETTPKYKLVAHVQDGKDFTQE  3150
3151  CFSEIIITVNDINDNMPIFSMAQYRVSVPEDAQLNTLITKVHAMDKDFGV  3200
3201  NRQIKYSLMGENHDYFKISKSTGIIRLHKSLDRETISLFNLTVKAEDCGV  3250
3251  PKLHSIATVAVNILDINDNPPEFSMRQYSCKILENATHGTEVCKVYATSI  3300
3301  DIGVNADIHYFIMSGNEQGKFKMDSTTGDLVLNATLDYEMSKFYFLTIQA  3350
3351  IDGGTPPLSNNAYVNISILDINDNSPTFLQNLYRINVNEDIFVGSKILDV  3400
3401  KATDEDSDVNGLVTYNIERGDNIGQFSIDPKNGTISVSRPLDRETISHYT  3450
3451  LEIQACDQGDPQRCNSVPININILDTNDNAPIFSSSNYSVVLQENRLLGY  3500
3501  VFLTFKISDADETPNTTPYTFDIRSGNEGGLFRLEQDGSLRTASRFNHNL  3550
3551  QDEFVIQVRVFDNGTPPLYSDAWVVVKIIEESQYPPIVTPLEVTINSFED  3600
3601  DFSGAFIGKVHASDQDKYDELNFSLVSGPDDMYQSSKLFNISNNTGKIYA  3650
3651  ISNLDIGLYKLNVSVSDGKFHVFSIVKINVELVTNDMLKESVVIRFRRIS  3700
3701  ASEFLLSHRKTFMRSIRNIMRCRQKDVILITLQSDYQKASQHAVGNRRAR  3750
3751  SIDSDLNVVFAVRKQQIIPDSDEFFTSDEIRQTLIDKKNEIENETNLVVE  3800
3801  DVLPSTCQSNKNDCVHGECKQILQILKNNVTTTFTDVISFAAPSYIPVNT  3850
3851  CVCRPGFDGKHCKETVNACSTDPCSPQRICMPSGSALGYQCVCPKGFSGT  3900
3901  YCERKSSKCSNESCDMGLFTAVSFGGKSYAHYKINKVKAKFTLENGFSYS  3950
3951  LQIRTVQQTGTLLYASGKVDYNILEIINGAVQYRFDLGSGEGVISVSSIN  4000
4001  ISDGEWHQISLERSLNSAKVMVDNKHVSHGSAPGVNGILNIQSNDIFVGA  4050
4051  EVRPHPSIIGYEDIQRGFIGCMANIKIAKESLPLYISGGSTIAALKRFTN  4100
4101  VEFKCDPSNVLVRLGICGSQPCANSGICKELDTDVFECACQPRYSGKHCE  4150
4151  IDLDPCSSGPCLFGGRCDYHGPNNYSCTCPIHLSGKRCEYGKFCTPNPCK  4200
4201  NGGICEEGDGISHCMCRGYTGPTCEIDVDECENQPCGNGATCINEPGSFR  4250
4251  CICPSYLTGASCGDPLYSNSISTKLKNFSIEHISGIISGVAVVLVIISCV  4300
4301  LCCVVLKRSSSSKRRNRLEKDKNKSSYKEANLNSLVDKDNYCKPNVKLSN  4350
4351  LEVNQRPISYTAVPNDNLVLSNRNFVNNLDILRSYGSAGDELENVPFEYQ  4400
4401  KVNRNKQHVNINSCHSTDADNAYKQEWCEQMHLRTFSENKLNNELKRDFG  4450
4451  PSVSRFSTGKLIQVEMPNVCHSSSANFVDYSALANGQYHWDCSDWVRKSH  4500
4501  NPLPDITEVPGAEIADSSSLHSNDSNESKSKKAFFVHREDGDVDPTRDIA  4550
4551  ALNEDIGSEYLDSEAESCLEPFMLPRSSNQPLSRLSSFNNIENEDYKSNT  4600
4601  VPLPSKVSHSCKVYLRHPDSYLPTMHFPSETDGESSMTEGPISRMEIKTR  4650
4651  RTISENSEEAYLFPCTVGEIGSNSNISVRLCEIEDSELEEFLPQQQTNN   4699

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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