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

NucPred

Fetching P25464 from www.uniprot.org...

The NucPred score for your sequence is 0.48 (see score help below)

   1  MALEQWKTTVQSVSERCDLSGLSQHPTDYQLASTGVKGAGGSSIEERSAI    50
  51  VSDELFSSLRDVCSQRQLDPRSLMLFSVHQMLKRFGNGSHTVVASLVTSS   100
 101  EGCPSTSAWRAIPSVIHHIEGGDNNNTVASAVEQAANLLNSEGSGQDLLI   150
 151  PIGLTELVKSELIDLLVIFDDETNNIRLPQDFPLILRIHQRQDHWQLSVR   200
 201  YPSPLFDTMVIDSFLSALHNLLSAVTKPSQLVRDIELLPEYQVAQLEKWN   250
 251  NTDGDYPTEKRLHHLFEEAAVRRPQHVALICGDKRITYEELNAMANRLAH   300
 301  HLVSSGIQTEQLVGLFLDKTELMIATILGIWKSGAAHVPIDPGYPDERVK   350
 351  FVLNDTKAQVVIASQRHVDRLRAEAVGGQHLRIIGLESLFDNLAQQTQHS   400
 401  PETSGNLTHLPLNSKQLAYVTYTSGTTGFPKGIYKEHTSVVNSITDLSAR   450
 451  YGVAGEDDEVILVFSAYVFEPFVRQMLMALTTGNSLAIISDEDKFDPDTL   500
 501  IPFIQKHKVTYIHATSSVLQEYDFGSCPSLKRMILVGENLTEPRYEALRQ   550
 551  RFKSRILNEYGFTESAFVTALNIFEPTSQRKDMSLGRPVRNVKCYILDAN   600
 601  LKRVPIGVTGELHIGGLGISRGYMNREELTRQKFLPNPYQTDKERQRGVN   650
 651  STMYKTGDLARWLPSGEVEYLGRADFQIKLRGIRIEPGEIESTLAMYPGI   700
 701  RASIVVSKKLLSQGQETIQDHLVGYYVCDEGHIPEGDLLSFLEKKLPRYM   750
 751  VPTRLVQLAQIPTNINGKADLRALPAVEVAVAPTHKQDGERGNQLESDLA   800
 801  AIWGNILSVPAQDIGSESNFFRLGGHSIACIQLIARVRQQLGQGITLEEV   850
 851  FQTKTLRAMAALLSEKYTKASNGTNGVTNGTAHVNGHAANGHVSDSYVAS   900
 901  SLQQGFVYHSLKNELSEAYTMQSMIHYGVPLKRDIYQAAWQRVQGEHPAL   950
 951  RLRFTWEAEVMQIVDPKSELDWRVVDWTDVSSREKQLVALEQLQTEDLAK  1000
1001  VYHLDKGPLMRLYLILLPDSKYSCLFSCHHAILDGWSLPLLFNNVHQAYL  1050
1051  DLVEGTASPVEQDATYLLGQQYLQSHRDDHLDFWAEQIGRIEERCDMNAL  1100
1101  LNEASRYKVPLADYDQVREQRQQTISLPWNNSMDAGVREELSSRGITLHS  1150
1151  ILQTVWHLVLHSYGGGTHTITGTTISGRHLPVPGIERSVGLFINTLPMIF  1200
1201  DHTVCQDMTALEAIEHVQGQVNAMNSRGNVELGRMSKNDLKHGLFDTLFV  1250
1251  LENYPNLDTEQREKHEEKLKFTIKGGTEKLSYPLAVIAQEDGDSGCSFTL  1300
1301  CYAGELFTDESIQALLDTVRDTLSDILGNIHAPIRNMEYLSSNQTAQLDK  1350
1351  WNATAFEYPNTTLHAMFESEAQQKPDKVAVVYEDIRLTYRELNSRANALA  1400
1401  FYLLSQAAIQPNKLVGLIMDKSEHMITSILAVWKTGGAYVPIDPRYPDQR  1450
1451  IQYILEDTAALAVITDSPHIDRLRSITNNRLPVIQSDFALQLPPSPVHPV  1500
1501  SNCKPSDLAYIMYTSGTTGNPKGVMVEHHGVVNLCVSLCRLFGLRNTDDE  1550
1551  VILSFSNYVFDHFVEQMTDALLNGQTLVVLNDEMRGDKERLYRYIETNRV  1600
1601  TYLSGTPSVISMYEFDRFRDHLRRVDCVGEAFSEPVFDKIRETFPGLIIN  1650
1651  GYGPTEVSITTHKRPYPFPERRTDKSIGCQLDNSTSYVLNDDMKRVPIGA  1700
1701  VGELYLGGDGVARGYHNRPDLTADRFPANPFQTEQERLEGRNARLYKTGD  1750
1751  LVRWIHNANGDGEIEYLGRNDFQVKIRGQRIELGEIEAVLSSYPGIKQSV  1800
1801  VLAKDRKNDGQKYLVGYFVSSAGSLSAQAIRRFMLTSLPDYMVPAQLVPI  1850
1851  AKFPVTVSGKLDAKALPVPDDTVEDDIVPPRTEVERILAGIWSELLEIPV  1900
1901  DRISIYSDFFSLGGDSLKSTKLSFAATRALGVAVSVRNLFSHPTIEALSQ  1950
1951  WIIRGSNEVKDVAVVKGGASLDIPLSPAQERLMFIHEFGHSGEDTGAYNV  2000
2001  PLQLQLHHDVCLESLEKALRDVVSRHEALRTLITRTQKSSVHCQKILDAE  2050
2051  EAQKLFSVDVLRLTSETEMQGRMAESTAHAFKLDEELPIHVRLYQVVRDG  2100
2101  RTLSFASIVCHHLAFDAWSWDVFQRDLDAFYAVHTKHKAAANLPTLRVQY  2150
2151  KEYAIEHRRALRAEQHRVLADYWLRKLSDMEASYLVPDRPRPAQFDYTGN  2200
2201  DLQFSTTPETTAQLKELAKREGSSLYTVVAAAYFLLLYVYTNQRDITIGI  2250
2251  PVAHRNHPDFESVVGFFVNLLPLRVNVSQSDIHGLIQAVQKELVDAQIHQ  2300
2301  DLPFQEITKLLHVQHDPSRHPLLQAVFNWENVPANVHEEQLLQEYKPPSP  2350
2351  LPSAAKFDLNVTVKESVNSLNVNFNYPTSLFEEETVQGFMETFHLLLRQL  2400
2401  AHNKASTSLSKLSVEDGVLNPEPTNLQPSSRDSGNSLHGLFEDIVASTPD  2450
2451  RIAIADGTRSLSYSELNERANQLVHLIISSASIVADDRIALLLDKSIDMV  2500
2501  IALLAVWKAGAAYVPLDPTYPSQRTELILEESSARTLITTRKHTPRGGTV  2550
2551  ANVPSVVLDSPETLACLNQQSKENPTTSTQKPSDLAYVIFTSGTTGKPKG  2600
2601  VLVEHQSVVQLRNSLIERYFGETNGSHAVLFLSNYVFDFSLEQLCLSVLG  2650
2651  GNKLIIPPEEGLTHEAFYDIGRREKLSYLSGTPSVLQQIELSRLPHLHMV  2700
2701  TAAGEEFHASQFEKMRSQFAGQINNAYGITETTVYNIITTFKGDAPFTKA  2750
2751  LCHGIPGSHVYVLNDRLQRVPFNAVGELYLGGDCLARGYLNQDALTNERF  2800
2801  IPNPFYEPKQASDSRPQRLYKTGDLVRFRGPHHLEYLGRKDQQVKLRGFR  2850
2851  IELSEVRDAVLAISAVKEAAVIPKYDEDGSDSRRVSAIVCYYTLNAGTVC  2900
2901  EASSIRDHLHANLPPYMVPSQIHQLEGSLPVTVNGKLDLNRLSTTQVSQP  2950
2951  ELYTAPRNSTEETLCQLWASLLGVDHCGIDDDLFARGGDSISSLRLVGDI  3000
3001  YRALGRKVTVKDIYLHRSVRALSENVLTDQKDKGTLPASPPLQRAEQGQV  3050
3051  EGDAPLLPIQDWFLSKPLDNPAYWNHCFTIRTGALSVEGLRGALKLLQER  3100
3101  HDVLRLRLQRRDEGRHVQTFARDCAQPRLTVLDRRSFEDAEDVQEALCEI  3150
3151  QSHFDLENGPLYTVAYIHGYEDGSARVWFACHHVMVDTVSWNIILQDLQA  3200
3201  LYHGDSLGPKSSSVQQWSLAVSDYKMPLSERAHWNVLRKTVAQSFETLPI  3250
3251  CMGGVLQCQEKFSRETTTALLSKACPALDSGMHEILLMAVGSALQKAAGD  3300
3301  VPQVVTIEGHGREDTIDATLDVSRTVGWFTSMYPFEIPKVTDPAQGVVDV  3350
3351  KEAMRRVPNRGVGYGPAYGYGGSCLPAVSFNYLGRLDQASSGAQRDWTLV  3400
3401  MDEDEYPVGLCTSAEDSGRSSSMVDFTFSISGGQLVMDMSSSWGHGARNE  3450
3451  FVRTVRNTLDDLIKTTSSRDFSAPLPPSDQESSFTPYFVFEEGERHGAPL  3500
3501  FLLPPGEGGAESYFHNIVKGLPNRNLVVFNNHYREEKTLRTIEALAEYYL  3550
3551  SHIRSIQPEGPYHILGWSFGGILGLEAAKRLTGEGHKIATLALIDPYFDI  3600
3601  PSASKAIGQPDDACVLDPIYHVYHPSPESFRTVSSLTNHIALFKATETND  3650
3651  QHGNATQQALYEWFATCPLNNLDKFLAADTIKVVPLEGTHFTWVHHPEQV  3700
3701  RSMCTMLDEWLG                                        3712

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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