| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O35923 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MTVEYKRRPTFWEIFKARCSTADLGPISLNWFEELFSEAPPYNTEHPEES 50
51 EYKPQGHEPQLFKTPQRNPSYHQFASTPIMFKEQSQTLPLDQSPFKELGN 100
101 VVANSKRKHHSKKKARKDPVVDVASLPLKACPSESPCTPRCTQVAPQRRK 150
151 PVVSGSLFYTPKLEETPKHISESLGVEVDPDMSWTSSLATPPTLSSTVLI 200
201 ARDEEAHRNAFPADSPASLKSYFSNHNESLKKNDRFIPSVSDSENKSQQE 250
251 AFSQGLEKMLGDSSSKINRFRDCLRKPIPNVLEDGETAVDTSGEDSFSLC 300
301 FPKRRTRNLQKTRMGKMKKKIFSETRTDGLSEEARGQADDKNSFALEIEP 350
351 RDSEPLDPSVTNQKPLYSQSGDISSEAGQCSDSIWSQPDPSGLNGTQTRK 400
401 IPLLHISFHKQSILEDFIDMKKEGTGSITFPHISSLPEPEKMFSEETLVD 450
451 KEHEGQHLESLEDSISGKQMVSGTSQTACLSPSIRKSIVKMREPLEETLD 500
501 TVFSDSMTSSAFTEELDASAGGLEIHTACSQREDSLCPSSVDTGSWPTTL 550
551 TDTSATVKNAGLITTLKNKRRKFIYSVSDDASHQGKKLQTQRQSELTNLS 600
601 APFEASAFEVPFPFTNVDSGIPDSSIKRSNLPNDPEEPSLSLTNSFVTAA 650
651 SKEISYIHALISQDLNDKEAILSEEKPQPYTALEADFLSCLPERSCENDQ 700
701 KSPKVSDRKEKVLVSACRPSGRLAAAVQLSSISFDSQENPLGSHNVTSTL 750
751 KLTPSPKTPLSKPVVVSRGKMCKMPEKLQCKSCKDNIELSKNIPLGVNEM 800
801 CVLSENSETPELLPPLEYITEVSSSVKSQFNQNTKIAVVQKDQKDSTFIS 850
851 EVTVHMNSEELFPEKENNFAFQVTNESNKPNIGSTVEFQEEDLSHAKGHS 900
901 LKNSPMTVDRDLDDEQAGQVLITEDSDSLAVVHDCTKKSRNTIEQHQKGT 950
951 ADKDFKSNSSLYLKSDGNNDYLDKWSEFLDPLMNHKLGGSFRTASNKEIK 1000
1001 LSEDNVKKSKMFFKDIEEQYPTSLDCIDTVSTLQLANKKRLSEPHTFDLK 1050
1051 SGTTVSTQCHSQSSVSHEDTHTAPQMLSSKQDFHSSHNLTPSQKAEITEL 1100
1101 STILEESGSQFEFTQFKNPSHIAQNNTSAVLGNQMAVVRTASEEWKDVDL 1150
1151 HLPLNPSSVGQIDHNKKFECLVGVKQSSSHLLEDTCNQNTSCFLPIKEME 1200
1201 FGGFCSALGTKLSVSNEALRKAMKLFSDIENISEEPSTKVGPRGFSSCAH 1250
1251 HDSVASVFKIKKQNTDKSFDEKSSKCQVTVQNNKEMTTCILVDENPENYV 1300
1301 KNIKQDNNYTGSQRNAYKLENSDVSKSSTSGTVYINKGDSDLPFAAEKGN 1350
1351 KYPESCTQYVREENAQIKESVSDLTCLEVMKAEETCHMKSSDKEQLPSDK 1400
1401 MEQNMKEFNISFQTASGKNIRVSKESLNKSVNILDQETEDLTVTSDSLNS 1450
1451 KILCGINKDKMHISCHKKSINIKKVFEEHFPIGTVSQLPALQQYPEYEIE 1500
1501 SIKEPTLLSFHTASGKKVKIMQESLDKVKNLFDETQYVRKTTNFGHQESK 1550
1551 PLKDREDYKERLTLAYEKIEVTASKCEEMQNFVSKQTEMLPQQNDHMYRQ 1600
1601 TENLTSNGSSPKVHGNIENKIEKNPRICCICQSSYFVTEDSALACYTGDS 1650
1651 RKTCVGESSLSKGKKWLREQSDKLGTRNTIEIQCVKEHTEDFAGNALYEH 1700
1701 SLVIIRTEIDTSHVSENQASTLFSDPNVCHSYLSHSSFCHHDDMHNDSGY 1750
1751 FLKDKIDSDVQPDMKNTEGNAIFPKISATKEIKLHPQTVNEECVQKLETN 1800
1801 ASPYANKNIAIDSAMLDLRNCKVGSPVFITTHSQETVRMKEIFTDNCSKI 1850
1851 VEQNRESKPDTCQTSCHKALDNSEDFICPSSSGDVCINSPMAIFYPQSEQ 1900
1901 ILQHNQSVSGLKKAATPPVSLETWDTCKSIRGSPQEVHPSRTYGFFSTAS 1950
1951 GKAVQVSDASLEKARQVFSEIDGDAKQLASMVSLEGNEKSHHSVKRESSV 2000
2001 VHNTHGVLSLRKTLPGNVSSFVFSGFSTAGGKLVTVSESALHKVKGMLEE 2050
2051 FDLIRTEHTLQHSPTPEDVSKIPPQPCLESRTPEYSVSSKLQKTYNDKSR 2100
2101 SPSNYKESGSSGNTQSLEVSPQLSQMERKQETQSVLGTKVSQRKTNILEK 2150
2151 KQNLPQNIKIESNKMETFSDVSMKTNVGEYYSKEPENYFETEAVEIAKAF 2200
2201 MEDDELTDSEQTHAKCSLFACPQNEALLNSRTRKRGGMAGVAVGQPPIKR 2250
2251 SLLNEFDRIIESKGKSLTPSKSTPDGTIKDRRLFTHHMSLEPVTCGPFCS 2300
2301 SKERQETQSPHVTSPAQGLQSKEHPSRHSAVGKSSSNPTVSALRSERTRH 2350
2351 SVSDKSTKVFVPPFKVKSRFHRDEHFDSKNVNLEGKNQKSADGVSEDGND 2400
2401 SDFPQFNKDLMSSLQNARDLQDIRIKNKERHHLCPQPGSLYLTKSSTLPR 2450
2451 ISLQAAVGDSVPSACSPKQLYMYGVSKACISVNSKNAEYFQFAIEDHFGK 2500
2501 EALCAGKGFRLADGGWLIPSDDGKAGKEEFYRALCDTPGVDPKLISSVWV 2550
2551 SNHYRWIVWKLAAMEFAFPKEFANRCLNPERVLLQLKYRYDVEIDNSSRS 2600
2601 ALKKILERDDTAAKTLVLCVSDIISLSTNVSETSGSKASSEDSNKVDTIE 2650
2651 LTDGWYAVKAQLDPPLLALVKSGRLTVGQKIITQGAELVGSPDACAPLEA 2700
2701 PDSLRLKISANSTRPARWHSKLGFFHDPRPFPLPLSSLFSDGGNVGCVDV 2750
2751 IVQRVYPLQWVEKTVSGSYIFRNEREEEKEALRFAEAQQKKLEALFTKVH 2800
2801 TELKEHEEDIAQRRVLSRALTRQQVHALQDGAELYAAVQDASDPEHLETC 2850
2851 FSEEQLRALNNYRQMLSDKKQARIQSEFRKALEAAEKEEGLSRDVSTVWK 2900
2901 LRVTSYKKREKSALLSIWRPSSDLPSLLTEGQRYRIYHLSVSKSKNKFEW 2950
2951 PSIQLTATKRTQYQQLPVSSETLLQLYQPRELLPFSKLSDPAFQPPCSEV 3000
3001 DVVGVVVSVVKPIGLAPLVYLSDECLHLLVVKFGIDLNEDIKPRVLIAAS 3050
3051 NLQWRPESTSRVPTLFAGNFSVFSASPKEAHFQERVTNMKHAIENIDTFY 3100
3101 KEAEKKLIQVLKGDSPKWSTPNKDPTREPYPASTCSASDLASGGQLPRSS 3150
3151 PTDQQSYRSPLSCCTPTGKSTPLAHSAWMAAKSCSGENEIEDPKTCRKKR 3200
3201 ALDLLSRLPLPPPLSPVCTFVSPAAQKAFQPPRSCGTKYPTPLKKEGPSS 3250
3251 PWSRAPFQKASGVSLLDCDSVADEELALLSTQALVPHSVGGSEQVFPSDS 3300
3301 TRTEGPSASTEARPANRSKRESLRDCRDDSDGKLAAETVPDYS 3343
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.) |
Go back to the NucPred Home Page.