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

NucPred

Fetching Q00869 from www.uniprot.org...

The NucPred score for your sequence is 0.50 (see score help below)

   1  MSLHTPSDGQQDPALASKTLCEQISRALGLGQDKIENIFPGTPFQRDVID    50
  51  CAADDKQRAVGHAVFEIPKDIDAARLAAAWKETVLHTPALRTCTFTSKSG   100
 101  DVLQVVLRDSFVFSWMSGPSVDLKEAVVQDEAAAALAGPRCNRFVLLEDP   150
 151  DTKERQLIWTFSHALVDSTFQERILRRVLKAYKDANDEHPRQFETPDSSQ   200
 201  ATPEEDLQPNPSKMLKIPQAADMDRAVEFWKDHLSGLKCFCLPAFVLSSV   250
 251  YAHPDAKAEHRISYSSSAQQKMSSATICRTALAILLSRYTHSPEALFGIV   300
 301  TEQTPLLEEQLMLDGPTRTVVPIRVSCASEQSVSDIMSTIDSYDQTMRQF   350
 351  AHAGLRNIASAGDDESAACGFQTVLLVSDGDAQPASTWEILKKTEEPEGF   400
 401  IPCTNRALLLSCQMTSSGAHLTARYDQSIIDAEQMARLLRQLGHLIQNLQ   450
 451  TSTDLPVEKVDMMTQEDWLEIERWNSDSIDAQDTLIHSEMLKWTSQSPNK   500
 501  AAVAAWDGEWTYAELDNVSSRLAQHINSIDLGKEHAIVPIYFEKSKWVVA   550
 551  SMLAVLKAGHAFTLIDPSDPPARTAQVVQQTSATVALTSKLHRETVQSTV   600
 601  GRCIVVDEEFVKSLPQSSELSASVKAHDLAYVIFTSGSTGIPKGIMIEHR   650
 651  SFSSCAIKFGPALGITSDTRALQFGSHAFGACILEIMTTLIHGGCVCIPS   700
 701  DDDRMNNVLEFINRTNVQLGHATPSYMGTFQPEVVPGLKTLVLVGEQMSA   750
 751  SVNEVWAPRVQLLNGYGQSESSSICCVAKISPGSSEPNNIGHAVGAHSWI   800
 801  VDPEDPNRLAPIGAVGELVIESAGIARDYIVAPTQDKSPFIKTAPTWYPA   850
 851  KQLPDGFKIYRTGDLACYASDGSIVCLGRMDSQVKIRGQRVELGAVETHL   900
 901  RQQMPDDMTIVVEAVKFSDSSSTTVLTAFLIGAGEKNSHILDQRATREIN   950
 951  AKMEQVLPRHSIPAFYISMNNLPQTATGKVDRRKLRIMGSKILSQKTHST  1000
1001  PSQQSQAAISSGTDTYTKLESIWITSLDLEPGSANMSATFFEMGGNSIIA  1050
1051  IKMVNMARSNGIELKVSDIYQNPTLAGLKAIVIGTSLPYSLIPKVTRQGP  1100
1101  VSEQSYAQNRMWFLDQLSEGASWYLIPFAVRMRGPVDVDALTRALLALEQ  1150
1151  RHETLRTTFENQDGVGVQIIHDRLSKELQVIDALDGDEGGLKTLYKVETT  1200
1201  TFDITSEAGWSSTLIRLGKDDHILSIVMHHIISDGWSIDVLRRELIQLYA  1250
1251  AALQGKDPSSALTPLPIQYSDFAVWQKQEAQAAEHERQLQYWKKQLADSS  1300
1301  PAKIPTDFPRPDLLSGDAGVVPVAIDGELYQKLRGFCNKHNSTAFSILLA  1350
1351  AFRAAHYRLTAVDDAVIGIPIANRNRWELENMIGFFVNTQCMRIAVDETD  1400
1401  TFESLVRQVRSTTTAAFAHEDVPFERVVSALQPGHRDLSRTPLAQIMFAV  1450
1451  HSQKDLGRFELEGIQSEPIASKAYTRFDVEFHLFQQADGLKGSCNFATDL  1500
1501  FKPETIQNVVSVFFQILRHGLDQPETCISVLPLTDGVEELRRLDLLEIKR  1550
1551  TNYPRDSSVVDVFREQAAANPEVIAVTDSSSRLTYAELDNKSELLSRWLR  1600
1601  RRNLTPETLVSVLAPRSCETIVAYVGILKANLAYLPLDVRSPVTRMKDIL  1650
1651  SSVSGNTIVLMGSGVEDPGFDLPQLELVRITDTFDETIEDVQDSPQPSAT  1700
1701  SLAYVVFTSGSTGKPKGVMIEHRAIVRLVKSDNFPGFPSPARMSNVFNPA  1750
1751  FDGAIWEINWMLLNGGTVVCIDYLTTLDGKELAAVFAKERVNAAFFAPAM  1800
1801  LKLYLVDAREALKNLDFLIVGGERFDTKEAVEAMPLVRGKIANIYGPTEA  1850
1851  GIISTCYNIPKDEAYTNGVPIGGSIYNSGAYVMDPNQQLVGLGVMGELVV  1900
1901  TGDGVGRGYTNPELNKNRFIDITIEGKTFKAYRTGDRMRARVGDGLLEFF  1950
1951  GRMDNQFKIRGNRIEAGEVESAMLSLKNVLNAAIVVRGGGEDEGPLEMVG  2000
2001  FIVADDKNDTTEEEETGNQVEGWQDHFESGMYSDISTAVDQSAIGNDFKG  2050
2051  WTSMYDGKDIDKGEMQEWLDDAIHTLHNGQIPRDVLEIGTGSGMILFNLN  2100
2101  PGLNSYVGLDPSKSAVEFVNRAVESSPKFAGKAKVHVGMATDVNKLGEVH  2150
2151  PDLVVFNSVVQYFPTPEYLAEVIDGLIAIPSVKRIFLGDIRSYATNGHFL  2200
2201  AARAIHTLGTNNNATKDRVRQKIQELEDREEEFLVEPAFFTTLKERRPDV  2250
2251  VKHVEIIPKNMKATNELSAYRYTAVVHLRDETDEPVYHIEKDSWVDFEAK  2300
2301  QMDKTALLDHLRLSKDAMSVAVSNITYAHTAFERRIVESLDEDSKDDTKG  2350
2351  TLDGAAWLSAVRSEAENRASLTVPDILEIAKEAGFRVEVSAARQWSQSGA  2400
2401  LDAVFHHFPPSSTDRTLIQFPTDNELRSSLTLANRPLQKLQRRRAALQVR  2450
2451  EKLQTLVPSYMVPPNIVVLDTMPLNTNGKIDRKELTRRARTLPKQQTAAP  2500
2501  VPDFPISDIEITLCEEATEVFGMKVEISDHFFQLGGHSLLATKLISRIQH  2550
2551  RLHVRVTVKDVFDSPVFADLAVIIRQGLAMQNPVAEGQDKQGWSSRVAPR  2600
2601  TEVEKMLCEEFAAGLGVPVGITDNFFDLGGHSLMATKLAVRIGRRLIRHH  2650
2651  SQGHLRLPCAFQLAKKLESSHSKSYEESGDDIQMADYTAFQLLDLEDPQD  2700
2701  FVQSQIRPQLDSCYGTIQDVYPSTQMQKAFLFDPTTGEPRGLVPFYIDFP  2750
2751  SNADAETLTKAIGALVDKLDMFRTVFLEAAGDLYQVVVEHLNLPIETIET  2800
2801  EKNVNTATGDYLDVHGKDPVRLGHPCIQFAILKTASSVRVLLRMSHALYD  2850
2851  GLSFEYIVRGLHVLYSGRNLPPPTQFARYMQYAAHSREEGYPFWREVLQN  2900
2901  APMTVLHDTNNGMSEQEMPASKAVHLSEVVNVPAQAIRNSTNTQATVFNT  2950
2951  ACALVLAKESGSQDVVFGRIVSGRQGLPVVWQDIIGPCTNAVPVHARVDD  3000
3001  GNPQRIIRDLRDQYLRTLPFESLGFEEIKRNCTDWPEELTNFSVCVTYHN  3050
3051  FEYHPESEVDNQKVEMGVLAKYVELSENEPLYDLAIAGEVEADGVNLKVT  3100
3101  VVAKARLYNEARIRHVLEEVCKTFNGLNEAL                     3131

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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