 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P20659 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MGRSKFPGKPSKSINRKRISVLQLEDDAANPAEPQQPAPESQQPSGSGSG 50
51 SSAAREKGNNCDNDEDDNAPGGASISGNTASSSAGSGNSGNGSSSGSSTG 100
101 SGSSGSGSTNGGSVNGGTHHKSAANLDKEAVTKDQNGDGDKTRGNVSSAP 150
151 SGKLSAAASGKALSKSSRTFSASTSVTSSGRSSGSSPDGNSGASSDGASS 200
201 GISCGKSTAKSTEASSGKLAKTTGAGTCSSAKSSKASSGTTSEATTSGLS 250
251 GACLKALFVATPATSTGLACALVSPGGSSQGGTFPISAALLRARKNSNKK 300
301 FKNLNLARGEVMLPSTSKLKQLNSPVVDNPSPSPPIASGSTPSVEGGIGV 350
351 GGVVSPGEDAALKRVLTEMPNEVARDPSPSSCTAAANGAASGKGSASNGP 400
401 PAMASSGDGSSPKSGADTGPSTSSTTAKQKKTVTFRNVLETSDDKSVVKR 450
451 FYNPDIRIPIVSIMKKDSLNRPLNYSRGGECIVRPSILSKILNKNSNIDK 500
501 LNSLKFRSAGASSSSSNQESGSSSNVFGLSRAFGAPMDEDDEGGVTFRRN 550
551 DSPEDQNNAEDDEMDDDDDDEEAEEDDENEDDNDEAVSEKSAETEKSAGA 600
601 DERDPDEKQLVMDSHFVLPKRSTRSSRIIKPNKRLLEEGAISTKKPLSLG 650
651 DSKGKNVFGTSSSSAGSTASTFSASTNLKLGKETFFNFGTLKPNSSAAGN 700
701 FVLRQPRLQFQADNQQATFTAPKACPTSPSAIPKPANSLATSSFGSLAST 750
751 NSSTVTPTPSACSICSAVVSSKEVTQARKYGVVACDVCRKFFSKMTKKSI 800
801 SANSSTANTSSGSQQYLQCKGNEGSPCSIHSAKSQLKNFKKFYKDRCTAC 850
851 WLKKCMISFQLPAAHRSRLSAILPPGMRGEAAAREEKSAELLSPTGSLRF 900
901 TSTASSSSPSVVASTSVKWKSSGDSTSALTSIKPNPLAENNVTFGSTPLL 950
951 RPAILENPLFLKISNAADQKLAAAEAISPSLTKKNSKQEKEKVKESEQSE 1000
1001 KLLSPTQAGTKKSGAAEAQVEEVQPQKEEAPQTSTTTQPSASNGASHGVP 1050
1051 QAELAGETNATGDTLKRQRIDLKGPRVKHVCRSASIVLGQPLATFGEDQQ 1100
1101 PEDAADMQQEIAAPVPSAIMEPSPEKPTHIVTDENDNCASCKTSPVGDES 1150
1151 KPSKSSGSAQAEVKKATALGKEGTASAAGGSSAKVTTRNAAVASNLIVAA 1200
1201 SKKQRNGDIATSSSVTQSSNQTQGRKTKEHRQQRTLISIDFWENYDPAEV 1250
1251 CQTGFGLIVTETVAQRALCFLCGSTGLDPLIFCACCCEPYHQYCVQDEYN 1300
1301 LKHGSFEDTTLMGSLLETTVNASTGPSSSLNQLTQRLNWLCPRCTVCYTC 1350
1351 NMSSGSKVKCQKCQKNYHSTCLGTSKRLLGADRPLICVNCLKCKSCSTTK 1400
1401 VSKFVGNLPMCTGCFKLRKKGNFCPICQRCYDDNDFDLKMMECGDCGQWV 1450
1451 HSKCEGLSDEQYNLLSTLPESIEFICKKCARRNESSKIKAEEWRQAVMEE 1500
1501 FKASLYSVLKLLSKSRQACALLKLSPRKKLRCTCGASSNQGKLQPKALQF 1550
1551 SSGSDNGLGSDGESQNSDDVYEFKDQQQQQQQRNANMNKPRVKSLPCSCQ 1600
1601 QHISHSQSFSLVDIKQKIAGNSYVSLAEFNYDMSQVIQQSNCDELDIAYK 1650
1651 ELLSEQFPWFQNETKACTDALEEDMFESCSGGNYEDLQDTGGVSASVYNE 1700
1701 HSTSQAESRSGVLDIPLEEVDDFGSCGIKMRLDTRMCLFCRKSGEGLSGE 1750
1751 EARLLYCGHDCWVHTNCAMWSAEVFEEIDGSLQNVHSAVARGRMIKCTVC 1800
1801 GNRGATVGCNVRSCGEHYHYPCARSIDCAFLTDKSMYCPAHAKNGNALKA 1850
1851 NGSPSVTYESNFEVSRPVYVELDRKRKKLIEPARVQFHIGSLEVRQLGAI 1900
1901 VPRFSDSYEAVVPINFLCSRLYWSSKEPWKIVEYTVRTTIQNSSSTLTAL 1950
1951 DVGRNYTVDHTNPNSKEVQLGMAQIARWHTSLARSEFLENGGTDWSGEFP 2000
2001 NPNSCVPPDENTEEEPQQQADLLPPELKDAIFEDLPHELLDGISMLDIFL 2050
2051 YDDKTDLFAISEQSKDGTQAMTSNQAQNQNQQAGGANSVSICDEDTRNSN 2100
2101 TSLGNGWPASNPVEDAMLSAARNSSQVQMLKTLAWPKLDGNSAMATAIKR 2150
2151 RKLSKNLAEGVFLTLSSQQRNKKEMATVAGVSRRQSISETSVEGVATTSG 2200
2201 SVRSKSFTWSAAKRYFEKSEGREEAAKMRIMQMDGVDDSITEFRIISGDG 2250
2251 NLSTAQFSGQVKCDRCQCTYRNYDAFQRHLPSCSPTMSSNETESDVSGQG 2300
2301 MTNNATQISAESLNELQKQLLANAGGLNYLQSATSFPQVQSLGSLGQFGL 2350
2351 QGLQQLQLQPQSLGSGFFLSQPNPATQANTDDLQIYANSLQSLAANLGGG 2400
2401 FTLAQPTVTAPAQPQLIAVSTNPDGTQQFIQIPQTMQATTTPTATYQTLQ 2450
2451 ATNTDKKIMLPLTAAGKPLKTVATKAAQQAAVKQRQLKSGHQVKPIQAKL 2500
2501 QPHPQQHQQQQQTQVQQPITVMGQNLLQPQLLFQSSTQTQAPQIILPQAQ 2550
2551 PQNIISFVTGDGSQGQPLQYISIPTAGEYKPQPQPTATPTFLTTAPGAGA 2600
2601 TYLQTDASGNLVLTTTPSNSGLQMLTAQSLQAQPQVIGTLIQPQTIQLGG 2650
2651 GADGNQPGSNQQPLILGGTGGGSSGLEFATTSPQVILATQPMYYGLETIV 2700
2701 QNTVMSSQQFVSTAMPGMLSQNASFSATTTQVFQASKIEPIVDLPAGYVV 2750
2751 LNNTGDASSAGTFLNAASVLQQQTQDDTTTQILQNANFQFQSVPTSSGAS 2800
2801 TSMDYTSPVMVTAKIPPVTQIKRTNAQAKAAGISGVGKVPPQPQVVNKVL 2850
2851 PTSIVTQQSQVQVKNSNLKQSQVKGKAASGTGTTCGAPPSIASKPLQKKT 2900
2901 NMIRPIHKLEVKPKVMKPTPKVQNQNHSLLQQQQQQQPQLQQQIPAVVVN 2950
2951 QVPKVTISQQRIPAQTQQQQLQQAQMIHIPQQQQPLQQQQVQVQPSMPII 3000
3001 TLAEAPVVQSQFVMEPQALEQQELANRVQHFSTSSSSSSSNCSLPTNVVN 3050
3051 PMQQQAPSTTSSSTTRPTNRVLPMQQRQEPAPLSNECPVVSSPTPPKPVE 3100
3101 QPIIHQMTSASVSKCYAQKSTLPSPVYEAELKVSSVLESIVPDVTMDAIL 3150
3151 EEQPVTESIYTEGLYEKNSPGESKTEQLLLQQQQREQLNQQLVNNGYLLD 3200
3201 KHTFQVEPMDTDVYREEDLEEEEDEDDDFSLKMATSACNDHEMSDSEEPA 3250
3251 VKDKISKILDNLTNDDCADSIATATTMEVDASAGYQQMVEDVLATTAAQS 3300
3301 APTEEFEGALETAAVEAAATYINEMADAHVLDLKQLQNGVELELRRRKEE 3350
3351 QRTVSQEQEQSKAAIVPTAAAPEPPQPIQEPKKMTGPHLLYEIQSEDGFT 3400
3401 YKSSSITEIWEKVFEAVQVARRAHGLTPLPEGPLADMGGIQMIGLKTNAL 3450
3451 KYLIEQLPGVEKCSKYTPKYHKRNGNVSTAANGAHGGNLGGSSASAALSV 3500
3501 SGGDSHGLLDYGSDQDELEENAYDCARCEPYSNRSEYDMFSWLASRHRKQ 3550
3551 PIQVFVQPSDNELVPRRGTGSNLPMAMKYRTLKETYKDYVGVFRSHIHGR 3600
3601 GLYCTKDIEAGEMVIEYAGELIRSTLTDKRERYYDSRGIGCYMFKIDDNL 3650
3651 VVDATMRGNAARFINHCCEPNCYSKVVDILGHKHIIIFALRRIVQGEELT 3700
3701 YDYKFPFEDEKIPCSCGSKRCRKYLN 3726
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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