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

NucPred

Fetching Q03164 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MAHSCRWRFPARPGTTGGGGGGGRRGLGGAPRQRVPALLLPPGPPVGGGG    50
  51  PGAPPSPPAVAAAAAAAGSSGAGVPGGAAAASAASSSSASSSSSSSSSAS   100
 101  SGPALLRVGPGFDAALQVSAAIGTNLRRFRAVFGESGGGGGSGEDEQFLG   150
 151  FGSDEEVRVRSPTRSPSVKTSPRKPRGRPRSGSDRNSAILSDPSVFSPLN   200
 201  KSETKSGDKIKKKDSKSIEKKRGRPPTFPGVKIKITHGKDISELPKGNKE   250
 251  DSLKKIKRTPSATFQQATKIKKLRAGKLSPLKSKFKTGKLQIGRKGVQIV   300
 301  RRRGRPPSTERIKTPSGLLINSELEKPQKVRKDKEGTPPLTKEDKTVVRQ   350
 351  SPRRIKPVRIIPSSKRTDATIAKQLLQRAKKGAQKKIEKEAAQLQGRKVK   400
 401  TQVKNIRQFIMPVVSAISSRIIKTPRRFIEDEDYDPPIKIARLESTPNSR   450
 451  FSAPSCGSSEKSSAASQHSSQMSSDSSRSSSPSVDTSTDSQASEEIQVLP   500
 501  EERSDTPEVHPPLPISQSPENESNDRRSRRYSVSERSFGSRTTKKLSTLQ   550
 551  SAPQQQTSSSPPPPLLTPPPPLQPASSISDHTPWLMPPTIPLASPFLPAS   600
 601  TAPMQGKRKSILREPTFRWTSLKHSRSEPQYFSSAKYAKEGLIRKPIFDN   650
 651  FRPPPLTPEDVGFASGFSASGTAASARLFSPLHSGTRFDMHKRSPLLRAP   700
 701  RFTPSEAHSRIFESVTLPSNRTSAGTSSSGVSNRKRKRKVFSPIRSEPRS   750
 751  PSHSMRTRSGRLSSSELSPLTPPSSVSSSLSISVSPLATSALNPTFTFPS   800
 801  HSLTQSGESAEKNQRPRKQTSAPAEPFSSSSPTPLFPWFTPGSQTERGRN   850
 851  KDKAPEELSKDRDADKSVEKDKSRERDREREKENKRESRKEKRKKGSEIQ   900
 901  SSSALYPVGRVSKEKVVGEDVATSSSAKKATGRKKSSSHDSGTDITSVTL   950
 951  GDTTAVKTKILIKKGRGNLEKTNLDLGPTAPSLEKEKTLCLSTPSSSTVK  1000
1001  HSTSSIGSMLAQADKLPMTDKRVASLLKKAKAQLCKIEKSKSLKQTDQPK  1050
1051  AQGQESDSSETSVRGPRIKHVCRRAAVALGRKRAVFPDDMPTLSALPWEE  1100
1101  REKILSSMGNDDKSSIAGSEDAEPLAPPIKPIKPVTRNKAPQEPPVKKGR  1150
1151  RSRRCGQCPGCQVPEDCGVCTNCLDKPKFGGRNIKKQCCKMRKCQNLQWM  1200
1201  PSKAYLQKQAKAVKKKEKKSKTSEKKDSKESSVVKNVVDSSQKPTPSARE  1250
1251  DPAPKKSSSEPPPRKPVEEKSEEGNVSAPGPESKQATTPASRKSSKQVSQ  1300
1301  PALVIPPQPPTTGPPRKEVPKTTPSEPKKKQPPPPESGPEQSKQKKVAPR  1350
1351  PSIPVKQKPKEKEKPPPVNKQENAGTLNILSTLSNGNSSKQKIPADGVHR  1400
1401  IRVDFKEDCEAENVWEMGGLGILTSVPITPRVVCFLCASSGHVEFVYCQV  1450
1451  CCEPFHKFCLEENERPLEDQLENWCCRRCKFCHVCGRQHQATKQLLECNK  1500
1501  CRNSYHPECLGPNYPTKPTKKKKVWICTKCVRCKSCGSTTPGKGWDAQWS  1550
1551  HDFSLCHDCAKLFAKGNFCPLCDKCYDDDDYESKMMQCGKCDRWVHSKCE  1600
1601  NLSDEMYEILSNLPESVAYTCVNCTERHPAEWRLALEKELQISLKQVLTA  1650
1651  LLNSRTTSHLLRYRQAAKPPDLNPETEESIPSRSSPEGPDPPVLTEVSKQ  1700
1701  DDQQPLDLEGVKRKMDQGNYTSVLEFSDDIVKIIQAAINSDGGQPEIKKA  1750
1751  NSMVKSFFIRQMERVFPWFSVKKSRFWEPNKVSSNSGMLPNAVLPPSLDH  1800
1801  NYAQWQEREENSHTEQPPLMKKIIPAPKPKGPGEPDSPTPLHPPTPPILS  1850
1851  TDRSREDSPELNPPPGIEDNRQCALCLTYGDDSANDAGRLLYIGQNEWTH  1900
1901  VNCALWSAEVFEDDDGSLKNVHMAVIRGKQLRCEFCQKPGATVGCCLTSC  1950
1951  TSNYHFMCSRAKNCVFLDDKKVYCQRHRDLIKGEVVPENGFEVFRRVFVD  2000
2001  FEGISLRRKFLNGLEPENIHMMIGSMTIDCLGILNDLSDCEDKLFPIGYQ  2050
2051  CSRVYWSTTDARKRCVYTCKIVECRPPVVEPDINSTVEHDENRTIAHSPT  2100
2101  SFTESSSKESQNTAEIISPPSPDRPPHSQTSGSCYYHVISKVPRIRTPSY  2150
2151  SPTQRSPGCRPLPSAGSPTPTTHEIVTVGDPLLSSGLRSIGSRRHSTSSL  2200
2201  SPQRSKLRIMSPMRTGNTYSRNNVSSVSTTGTATDLESSAKVVDHVLGPL  2250
2251  NSSTSLGQNTSTSSNLQRTVVTVGNKNSHLDGSSSSEMKQSSASDLVSKS  2300
2301  SSLKGEKTKVLSSKSSEGSAHNVAYPGIPKLAPQVHNTTSRELNVSKIGS  2350
2351  FAEPSSVSFSSKEALSFPHLHLRGQRNDRDQHTDSTQSANSSPDEDTEVK  2400
2401  TLKLSGMSNRSSIINEHMGSSSRDRRQKGKKSCKETFKEKHSSKSFLEPG  2450
2451  QVTTGEEGNLKPEFMDEVLTPEYMGQRPCNNVSSDKIGDKGLSMPGVPKA  2500
2501  PPMQVEGSAKELQAPRKRTVKVTLTPLKMENESQSKNALKESSPASPLQI  2550
2551  ESTSPTEPISASENPGDGPVAQPSPNNTSCQDSQSNNYQNLPVQDRNLML  2600
2601  PDGPKPQEDGSFKRRYPRRSARARSNMFFGLTPLYGVRSYGEEDIPFYSS  2650
2651  STGKKRGKRSAEGQVDGADDLSTSDEDDLYYYNFTRTVISSGGEERLASH  2700
2701  NLFREEEQCDLPKISQLDGVDDGTESDTSVTATTRKSSQIPKRNGKENGT  2750
2751  ENLKIDRPEDAGEKEHVTKSSVGHKNEPKMDNCHSVSRVKTQGQDSLEAQ  2800
2801  LSSLESSRRVHTSTPSDKNLLDTYNTELLKSDSDNNNSDDCGNILPSDIM  2850
2851  DFVLKNTPSMQALGESPESSSSELLNLGEGLGLDSNREKDMGLFEVFSQQ  2900
2901  LPTTEPVDSSVSSSISAEEQFELPLELPSDLSVLTTRSPTVPSQNPSRLA  2950
2951  VISDSGEKRVTITEKSVASSESDPALLSPGVDPTPEGHMTPDHFIQGHMD  3000
3001  ADHISSPPCGSVEQGHGNNQDLTRNSSTPGLQVPVSPTVPIQNQKYVPNS  3050
3051  TDSPGPSQISNAAVQTTPPHLKPATEKLIVVNQNMQPLYVLQTLPNGVTQ  3100
3101  KIQLTSSVSSTPSVMETNTSVLGPMGGGLTLTTGLNPSLPTSQSLFPSAS  3150
3151  KGLLPMSHHQHLHSFPAATQSSFPPNISNPPSGLLIGVQPPPDPQLLVSE  3200
3201  SSQRTDLSTTVATPSSGLKKRPISRLQTRKNKKLAPSSTPSNIAPSDVVS  3250
3251  NMTLINFTPSQLPNHPSLLDLGSLNTSSHRTVPNIIKRSKSSIMYFEPAP  3300
3301  LLPQSVGGTAATAAGTSTISQDTSHLTSGSVSGLASSSSVLNVVSMQTTT  3350
3351  TPTSSASVPGHVTLTNPRLLGTPDIGSISNLLIKASQQSLGIQDQPVALP  3400
3401  PSSGMFPQLGTSQTPSTAAITAASSICVLPSTQTTGITAASPSGEADEHY  3450
3451  QLQHVNQLLASKTGIHSSQRDLDSASGPQVSNFTQTVDAPNSMGLEQNKA  3500
3501  LSSAVQASPTSPGGSPSSPSSGQRSASPSVPGPTKPKPKTKRFQLPLDKG  3550
3551  NGKKHKVSHLRTSSSEAHIPDQETTSLTSGTGTPGAEAEQQDTASVEQSS  3600
3601  QKECGQPAGQVAVLPEVQVTQNPANEQESAEPKTVEEEESNFSSPLMLWL  3650
3651  QQEQKRKESITEKKPKKGLVFEISSDDGFQICAESIEDAWKSLTDKVQEA  3700
3701  RSNARLKQLSFAGVNGLRMLGILHDAVVFLIEQLSGAKHCRNYKFRFHKP  3750
3751  EEANEPPLNPHGSARAEVHLRKSAFDMFNFLASKHRQPPEYNPNDEEEEE  3800
3801  VQLKSARRATSMDLPMPMRFRHLKKTSKEAVGVYRSPIHGRGLFCKRNID  3850
3851  AGEMVIEYAGNVIRSIQTDKREKYYDSKGIGCYMFRIDDSEVVDATMHGN  3900
3901  AARFINHSCEPNCYSRVINIDGQKHIVIFAMRKIYRGEELTYDYKFPIED  3950
3951  ASNKLPCNCGAKKCRKFLN                                 3969

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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