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

NucPred

Fetching P78527 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MAGSGAGVRCSLLRLQETLSAADRCGAALAGHQLIRGLGQECVLSSSPAV    50
  51  LALQTSLVFSRDFGLLVFVRKSLNSIEFRECREEILKFLCIFLEKMGQKI   100
 101  APYSVEIKNTCTSVYTKDRAAKCKIPALDLLIKLLQTFRSSRLMDEFKIG   150
 151  ELFSKFYGELALKKKIPDTVLEKVYELLGLLGEVHPSEMINNAENLFRAF   200
 201  LGELKTQMTSAVREPKLPVLAGCLKGLSSLLCNFTKSMEEDPQTSREIFN   250
 251  FVLKAIRPQIDLKRYAVPSAGLRLFALHASQFSTCLLDNYVSLFEVLLKW   300
 301  CAHTNVELKKAALSALESFLKQVSNMVAKNAEMHKNKLQYFMEQFYGIIR   350
 351  NVDSNNKELSIAIRGYGLFAGPCKVINAKDVDFMYVELIQRCKQMFLTQT   400
 401  DTGDDRVYQMPSFLQSVASVLLYLDTVPEVYTPVLEHLVVMQIDSFPQYS   450
 451  PKMQLVCCRAIVKVFLALAAKGPVLRNCISTVVHQGLIRICSKPVVLPKG   500
 501  PESESEDHRASGEVRTGKWKVPTYKDYVDLFRHLLSSDQMMDSILADEAF   550
 551  FSVNSSSESLNHLLYDEFVKSVLKIVEKLDLTLEIQTVGEQENGDEAPGV   600
 601  WMIPTSDPAANLHPAKPKDFSAFINLVEFCREILPEKQAEFFEPWVYSFS   650
 651  YELILQSTRLPLISGFYKLLSITVRNAKKIKYFEGVSPKSLKHSPEDPEK   700
 701  YSCFALFVKFGKEVAVKMKQYKDELLASCLTFLLSLPHNIIELDVRAYVP   750
 751  ALQMAFKLGLSYTPLAEVGLNALEEWSIYIDRHVMQPYYKDILPCLDGYL   800
 801  KTSALSDETKNNWEVSALSRAAQKGFNKVVLKHLKKTKNLSSNEAISLEE   850
 851  IRIRVVQMLGSLGGQINKNLLTVTSSDEMMKSYVAWDREKRLSFAVPFRE   900
 901  MKPVIFLDVFLPRVTELALTASDRQTKVAACELLHSMVMFMLGKATQMPE   950
 951  GGQGAPPMYQLYKRTFPVLLRLACDVDQVTRQLYEPLVMQLIHWFTNNKK  1000
1001  FESQDTVALLEAILDGIVDPVDSTLRDFCGRCIREFLKWSIKQITPQQQE  1050
1051  KSPVNTKSLFKRLYSLALHPNAFKRLGASLAFNNIYREFREEESLVEQFV  1100
1101  FEALVIYMESLALAHADEKSLGTIQQCCDAIDHLCRIIEKKHVSLNKAKK  1150
1151  RRLPRGFPPSASLCLLDLVKWLLAHCGRPQTECRHKSIELFYKFVPLLPG  1200
1201  NRSPNLWLKDVLKEEGVSFLINTFEGGGCGQPSGILAQPTLLYLRGPFSL  1250
1251  QATLCWLDLLLAALECYNTFIGERTVGALQVLGTEAQSSLLKAVAFFLES  1300
1301  IAMHDIIAAEKCFGTGAAGNRTSPQEGERYNYSKCTVVVRIMEFTTTLLN  1350
1351  TSPEGWKLLKKDLCNTHLMRVLVQTLCEPASIGFNIGDVQVMAHLPDVCV  1400
1401  NLMKALKMSPYKDILETHLREKITAQSIEELCAVNLYGPDAQVDRSRLAA  1450
1451  VVSACKQLHRAGLLHNILPSQSTDLHHSVGTELLSLVYKGIAPGDERQCL  1500
1501  PSLDLSCKQLASGLLELAFAFGGLCERLVSLLLNPAVLSTASLGSSQGSV  1550
1551  IHFSHGEYFYSLFSETINTELLKNLDLAVLELMQSSVDNTKMVSAVLNGM  1600
1601  LDQSFRERANQKHQGLKLATTILQHWKKCDSWWAKDSPLETKMAVLALLA  1650
1651  KILQIDSSVSFNTSHGSFPEVFTTYISLLADTKLDLHLKGQAVTLLPFFT  1700
1701  SLTGGSLEELRRVLEQLIVAHFPMQSREFPPGTPRFNNYVDCMKKFLDAL  1750
1751  ELSQSPMLLELMTEVLCREQQHVMEELFQSSFRRIARRGSCVTQVGLLES  1800
1801  VYEMFRKDDPRLSFTRQSFVDRSLLTLLWHCSLDALREFFSTIVVDAIDV  1850
1851  LKSRFTKLNESTFDTQITKKMGYYKILDVMYSRLPKDDVHAKESKINQVF  1900
1901  HGSCITEGNELTKTLIKLCYDAFTENMAGENQLLERRRLYHCAAYNCAIS  1950
1951  VICCVFNELKFYQGFLFSEKPEKNLLIFENLIDLKRRYNFPVEVEVPMER  2000
2001  KKKYIEIRKEAREAANGDSDGPSYMSSLSYLADSTLSEEMSQFDFSTGVQ  2050
2051  SYSYSSQDPRPATGRFRRREQRDPTVHDDVLELEMDELNRHECMAPLTAL  2100
2101  VKHMHRSLGPPQGEEDSVPRDLPSWMKFLHGKLGNPIVPLNIRLFLAKLV  2150
2151  INTEEVFRPYAKHWLSPLLQLAASENNGGEGIHYMVVEIVATILSWTGLA  2200
2201  TPTGVPKDEVLANRLLNFLMKHVFHPKRAVFRHNLEIIKTLVECWKDCLS  2250
2251  IPYRLIFEKFSGKDPNSKDNSVGIQLLGIVMANDLPPYDPQCGIQSSEYF  2300
2301  QALVNNMSFVRYKEVYAAAAEVLGLILRYVMERKNILEESLCELVAKQLK  2350
2351  QHQNTMEDKFIVCLNKVTKSFPPLADRFMNAVFFLLPKFHGVLKTLCLEV  2400
2401  VLCRVEGMTELYFQLKSKDFVQVMRHRDDERQKVCLDIIYKMMPKLKPVE  2450
2451  LRELLNPVVEFVSHPSTTCREQMYNILMWIHDNYRDPESETDNDSQEIFK  2500
2501  LAKDVLIQGLIDENPGLQLIIRNFWSHETRLPSNTLDRLLALNSLYSPKI  2550
2551  EVHFLSLATNFLLEMTSMSPDYPNPMFEHPLSECEFQEYTIDSDWRFRST  2600
2601  VLTPMFVETQASQGTLQTRTQEGSLSARWPVAGQIRATQQQHDFTLTQTA  2650
2651  DGRSSFDWLTGSSTDPLVDHTSPSSDSLLFAHKRSERLQRAPLKSVGPDF  2700
2701  GKKRLGLPGDEVDNKVKGAAGRTDLLRLRRRFMRDQEKLSLMYARKGVAE  2750
2751  QKREKEIKSELKMKQDAQVVLYRSYRHGDLPDIQIKHSSLITPLQAVAQR  2800
2801  DPIIAKQLFSSLFSGILKEMDKFKTLSEKNNITQKLLQDFNRFLNTTFSF  2850
2851  FPPFVSCIQDISCQHAALLSLDPAAVSAGCLASLQQPVGIRLLEEALLRL  2900
2901  LPAELPAKRVRGKARLPPDVLRWVELAKLYRSIGEYDVLRGIFTSEIGTK  2950
2951  QITQSALLAEARSDYSEAAKQYDEALNKQDWVDGEPTEAEKDFWELASLD  3000
3001  CYNHLAEWKSLEYCSTASIDSENPPDLNKIWSEPFYQETYLPYMIRSKLK  3050
3051  LLLQGEADQSLLTFIDKAMHGELQKAILELHYSQELSLLYLLQDDVDRAK  3100
3101  YYIQNGIQSFMQNYSSIDVLLHQSRLTKLQSVQALTEIQEFISFISKQGN  3150
3151  LSSQVPLKRLLNTWTNRYPDAKMDPMNIWDDIITNRCFFLSKIEEKLTPL  3200
3201  PEDNSMNVDQDGDPSDRMEVQEQEEDISSLIRSCKFSMKMKMIDSARKQN  3250
3251  NFSLAMKLLKELHKESKTRDDWLVSWVQSYCRLSHCRSRSQGCSEQVLTV  3300
3301  LKTVSLLDENNVSSYLSKNILAFRDQNILLGTTYRIIANALSSEPACLAE  3350
3351  IEEDKARRILELSGSSSEDSEKVIAGLYQRAFQHLSEAVQAAEEEAQPPS  3400
3401  WSCGPAAGVIDAYMTLADFCDQQLRKEEENASVIDSAELQAYPALVVEKM  3450
3451  LKALKLNSNEARLKFPRLLQIIERYPEETLSLMTKEISSVPCWQFISWIS  3500
3501  HMVALLDKDQAVAVQHSVEEITDNYPQAIVYPFIISSESYSFKDTSTGHK  3550
3551  NKEFVARIKSKLDQGGVIQDFINALDQLSNPELLFKDWSNDVRAELAKTP  3600
3601  VNKKNIEKMYERMYAALGDPKAPGLGAFRRKFIQTFGKEFDKHFGKGGSK  3650
3651  LLRMKLSDFNDITNMLLLKMNKDSKPPGNLKECSPWMSDFKVEFLRNELE  3700
3701  IPGQYDGRGKPLPEYHVRIAGFDERVTVMASLRRPKRIIIRGHDEREHPF  3750
3751  LVKGGEDLRQDQRVEQLFQVMNGILAQDSACSQRALQLRTYSVVPMTSRL  3800
3801  GLIEWLENTVTLKDLLLNTMSQEEKAAYLSDPRAPPCEYKDWLTKMSGKH  3850
3851  DVGAYMLMYKGANRTETVTSFRKRESKVPADLLKRAFVRMSTSPEAFLAL  3900
3901  RSHFASSHALICISHWILGIGDRHLNNFMVAMETGGVIGIDFGHAFGSAT  3950
3951  QFLPVPELMPFRLTRQFINLMLPMKETGLMYSIMVHALRAFRSDPGLLTN  4000
4001  TMDVFVKEPSFDWKNFEQKMLKKGGSWIQEINVAEKNWYPRQKICYAKRK  4050
4051  LAGANPAVITCDELLLGHEKAPAFRDYVAVARGSKDHNIRAQEPESGLSE  4100
4101  ETQVKCLMDQATDPNILGRTWEGWEPWM                        4128

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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