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

NucPred

Fetching Q86XX4 from www.uniprot.org...

The NucPred score for your sequence is 0.64 (see score help below)

   1  MGVLKVWLGLALALAEFAVLPHHSEGACVYQDSLLADATIWKPDSCQSCR    50
  51  CHGDIVICKPAVCRNPQCAFEKGEVLQIAANQCCPECVLRTPGSCHHEKK   100
 101  IHEHGTEWASSPCSVCSCNHGEVRCTPQPCPPLSCGHQELAFIPEGSCCP   150
 151  VCVGLGKPCSYEGHVFQDGEDWRLSRCAKCLCRNGVAQCFTAQCQPLFCN   200
 201  QDETVVRVPGKCCPQCSARSCSAAGQVYEHGEQWSENACTTCICDRGEVR   250
 251  CHKQACLPLRCGKGQSRARRHGQCCEECVSPAGSCSYDGVVRYQDEMWKG   300
 301  SACEFCMCDHGQVTCQTGECAKVECARDEELIHLDGKCCPECISRNGYCV   350
 351  YEETGEFMSSNASEVKRIPEGEKWEDGPCKVCECRGAQVTCYEPSCPPCP   400
 401  VGTLALEVKGQCCPDCTSVHCHPDCLTCSQSPDHCDLCQDPTKLLQNGWC   450
 451  VHSCGLGFYQAGSLCLACQPQCSTCTSGLECSSCQPPLLMRHGQCVPTCG   500
 501  DGFYQDRHSCAVCHESCAGCWGPTEKHCLACRDPLHVLRDGGCESSCGKG   550
 551  FYNRQGTCSACDQSCDSCGPSSPRCLTCTEKTVLHDGKCMSECPGGYYAD   600
 601  ATGRCKVCHNSCASCSGPTPSHCTACSPPKALRQGHCLPRCGEGFYSDHG   650
 651  VCKACHSSCLACMGPAPSHCTGCKKPEEGLQVEQLSDVGIPSGECLAQCR   700
 701  AHFYLESTGICEACHQSCFRCAGKSPHNCTDCGPSHVLLDGQCLSQCPDG   750
 751  YFHQEGSCTECHPTCRQCHGPLESDCISCYPHISLTNGNCRTSCREEQFL   800
 801  NLVGYCADCHHLCQHCAADLHNTGSICLRCQNAHYLLLGDHCVPDCPSGY   850
 851  YAERGACKKCHSSCRTCQGRGPFSCSSCDTNLVLSHTGTCSTTCFPGHYL   900
 901  DDNHVCQPCNTHCGSCDSQASCTSCRDPNKVLLFGECQYESCAPQYYLDF   950
 951  STNTCKECDWSCSACSGPLKTDCLQCMDGYVLQDGACVEQCLSSFYQDSG  1000
1001  LCKNCDSYCLQCQGPHECTRCKGPFLLLEAQCVQECGKGYFADHAKHKCT  1050
1051  ACPQGCLQCSHRDRCHLCDHGFFLKSGLCVYNCVPGFSVHTSNETCSGKI  1100
1101  HTPSLHVNGSLILPIGSIKPLDFSLLNVQDQEGRVEDLLFHVVSTPTNGQ  1150
1151  LVLSRNGKEVQLDKAGRFSWKDVNEKKVRFVHSKEKLRKGYLFLKISDQQ  1200
1201  FFSEPQLINIQAFSTQAPYVLRNEVLHISRGERATITTQMLDIRDDDNPQ  1250
1251  DVVIEIIDPPLHGQLLQTLQSPATPIYQFQLDELSRGLLHYAHDGSDSTS  1300
1301  DVAVLQANDGHSFHNILFQVKTVPQNDRGLQLVANSMVWVPEGGMLQITN  1350
1351  RILQAEAPGASAEEIIYKITQDYPQFGEVVLLVNMPADSPADEGQHLPDG  1400
1401  RTATPTSTFTQQDINEGIVWYRHSGAPAQSDSFRFEVSSASNAQTRLESH  1450
1451  MFNIAILPQTPEAPKVSLEASLHMTAREDGLTVIQPHSLSFINSEKPSGK  1500
1501  IVYNITLPLHPNQGIIEHRDHPHSPIRYFTQEDINQGKVMYRPPPAAPHL  1550
1551  QELMAFSFAGLPESVKFHFTVSDGEHTSPEMVLTIHLLPSDQQLPVFQVT  1600
1601  APRLAVSPGGSTSVGLQVVVRDAETAPKELFFELRRPPQHGVLLKHTAEF  1650
1651  RRPMATGDTFTYEDVEKNALQYIHDGSSTREDSMEISVTDGLTVTMLEVR  1700
1701  VEVSLSEDRGPRLAAGSSLSITVASKSTAIITRSHLAYVDDSSPDPEIWI  1750
1751  QLNYLPSYGTLLRISGSEVEELSEVSNFTMEDINNKKIRYSAVFETDGHL  1800
1801  VTDSFYFSVSDMDHNHLDNQIFTIMITPAENPPPVIAFADLITVDEGGRA  1850
1851  PLSFHHFFATDDDDNLQRDAIIKLSALPKYGCIENTGTGDRFGPETASDL  1900
1901  EASFPIQDVLENYIYYFQSVHESIEPTHDIFSFYVSDGTSRSEIHSINIT  1950
1951  IERKNDEPPRMTLQPLRVQLSSGVVISNSSLSLQDLDTPDNELIFVLTKK  2000
2001  PDHGHVLWRQTASEPLENGRVLVQGSTFTYQDILAGLVGYVPSVPGMVVD  2050
2051  EFQFSLTDGLHVDTGRMKIYTELPASDTPHLAINQGLQLSAGSVARITEQ  2100
2101  HLKVTDIDSDDHQVMYIMKEDPGAGRLQMMKHGNLEQISIKGPIRSFTQA  2150
2151  DISQGQPEYSHGTGEPGGSFAFKFDVVDGEGNRLIDKSFSISISEDKSPP  2200
2201  VITTNKGLVLDENSVKKITTLQLSATDQDSGPTELIYRITRQPQLGHLEH  2250
2251  AASPGIQISSFTQADLTSRNVQYVHSSEAEKHSDAFSFTLSDGVSEVTQT  2300
2301  FHITLHPVDDSLPVVQNLGMRVQEGMRKTITEFELKAVDADTEAESVTFT  2350
2351  IVQPPRHGTIERTSNGQHFHLTSTFTMKDIYQNRVSYSHDGSNSLKDRFT  2400
2401  FTVSDGTNPFFIIEEGGKEIMTAAPQPFRVDILPVDDGTPRIVTNLGLQW  2450
2451  LEYMDGKATNLITKKELLTMDPDTEDAQLVYEITTGPKHGFVENKLQPGR  2500
2501  AAATFTQEDVNLGLIRYVLHKEKIREMMDSFQFLVKDSKPNVVSDNVFHI  2550
2551  QWSLISFKYTSYNVSEKAGSVSVTVQRTGNLNQYAIVLCRTEQGTASSSS  2600
2601  QPGQQDYVEYAGQVQFDEREDTKSCTIVINDDDVFENVESFTVELSMPAY  2650
2651  ALLGEFTQAKVIINDTEDEPTLEFDKKIYWVNESAGFLFAPIERKGDASS  2700
2701  IVSAICYTVPKSAMGSLFYALESGSDFKSRGMSAASRVIFGPGVTMSTCD  2750
2751  VMLIDDSEYEEEEEFEIALADASDNARIGRVATAKVLISGPNDASTVSLG  2800
2801  NTAFTVSEDAGTVKIPVIRHGTDLSTFASVWCATRPSDPASATPGVDYVP  2850
2851  SSRKVEFGPGVIEQYCTLTILDDTQYPVIEGLETFVVFLSSAQGAELTKP  2900
2901  FQAVIAINDTFQDVPSMQFAKDLLLVKEKEGVLHVPITRSGDLSYESSVR  2950
2951  CYTQSHSAQVMEDFEERQNADSSRITFLKGDKVKNCTVYIHDDSMFEPEE  3000
3001  QFRVYLGLPLGNHWSGARIGKNNMATITISNDEDAPTIEFEEAAYQVREP  3050
3051  AGPDAIAILNIKVIRRGDQNRTSKVRCSTRDGSAQSGVDYYPKSRVLKFS  3100
3101  PGVDHIFFKVEILSNEDREWHESFSLVLGPDDPVEAVLGDVTTATVTILD  3150
3151  QEAAGSLILPAPPIVVTLADYDHVEEVTKEGVKKSPSPGYPLVCVTPCDP  3200
3201  HFPRYAVMKERCSEAGINQTSVQFSWEVAAPTDGNGARSPFETITDNTPF  3250
3251  TSVNHMVLDSIYFSRRFHVRCVAKAVDKVGHVGTPLRSNIVTIGTDSAIC  3300
3301  HTPVVAGTSRGFQAQSFIATLKYLDVKHKEHPNRIHISVQIPHQDGMLPL  3350
3351  ISTMPLHNLHFLLSESIYRHQHVCSNLVTTYDLRGLAEAGFLDDVVYDST  3400
3401  ALGPGYDRPFQFDPSVREPKTIQLYKHLNLKSCVWTFDAYYDMTELIDVC  3450
3451  GGSVTADFQVRDSAQSFLTVHVPLYVSYIYVTAPRGWASLEHHTEMEFSF  3500
3501  FYDTVLWRTGIQTDSVLSARLQIIRIYIREDGRLVIEFKTHAKFRGQFVM  3550
3551  EHHTLPEVKSFVLTPDHLGGIEFDLQLLWSAQTFDSPHQLWRATSSYNRK  3600
3601  DYSGEYTIYLIPCTVQPTQPWVDPGEKPLACTAHAPERFLIPIAFQQTNR  3650
3651  PVPVVYSLNTEFQLCNNEKVFLMDPNTSDMSLAEMDYKGAFSKGQILYGR  3700
3701  VLWNPEQNLNSAYKLQLEKVYLCTGKDGYVPFFDPTGTIYNEGPQYGCIQ  3750
3751  PNKHLKHRFLLLDRNQPEVTDKYFHDVPFEAHFASELPDFHVVSNMPGVD  3800
3801  GFTLKVDALYKVEAGHQWYLQVIYIIGPDTISGPRVQRSLTAPLRRNRRD  3850
3851  LVEPDGQLILDDSLIYDNEGDQVKNGTNMKSLNLEMQELAVAASLSQTGA  3900
3901  SIGSALAAIMLLLLVFLVACFINRKCQKQRKKKPAEDILEEYPLNTKVEV  3950
3951  PKRHPDRVEKNVNRHYCTVRNVNILSEPEAAYTFKGAKVKRLNLEVRVHN  4000
4001  NLQDGTEV                                            4008

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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