 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4U2R1 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MPSESFCLAAQSRLDSKWLKTDIQLAFTRDGLCGLWNEMVKDGEIVYTGT 50
51 ELAQNRELPLRKDDGVDAQSGTKKEDLNDKEKKEEEETPAPVYRAKSILE 100
101 SWVWGRQPDVNELKECLSVLVKEQQALAVQSATTTLSALRLKQRLVILER 150
151 YFIALNRTVFQENVKVKWKSSSISVPPTEKKSARPTGRGVEGLARVGSRA 200
201 ALSFAFAFLRRAWRSGEDADLCSELLQESLDALRALPEASLFDESTVSSV 250
251 WLEVVERATRFLRSVVTGDVHGTPGTKGPGGVPLQDQHLALAILLELAVQ 300
301 RGTLSQMLSAILLLLQLWDSGAQETDNERSAQGTSAPLLPLLQRFQSIIC 350
351 SKDVPHTESDMHLLSGPLSPNESFLRYLTLPQDNELAIDLRQTAVVVMAH 400
401 LDRLATPCMPPLCSSPTSHKGSLQEVIGWGLIGWKYYANVIGPIQCEGLA 450
451 SLGVMQVACAEKRFLILSRNGRVYTQAYNSDMLAPQLVQGLASRNIVKIA 500
501 AHSDGHHYLALAATGEVYSWGCGDGGRLGHGDTVPLEEPKVISAFSGKQA 550
551 GKHVVHIACGSTYSAAITAEGELYTWGRGNYGRLGHGSSEDEAIPMLVAG 600
601 LKGLKVIDVACGSGDAQTLAVTENGQVWSWGDGDYGKLGRGGSDGCKTPK 650
651 LIEKLQDLDVIKVRCGSQFSIALTKDGQVYSWGKGDNQRLGHGTEEHVRY 700
701 PKLLEGLQGKKVIDVAAGSTHCLALTEDSEVHSWGSNDQCQHFDTLRVTK 750
751 PEPTALPGLDSKHIVGIACGPAQSFAWSSCSEWSIGLRVPFVVDICSMTF 800
801 EQLDLLLRQVSEGMDGTADWPPPQEKECMAVATLNLLRLQLHAAISHQVD 850
851 PEFLGLGLGSVLLNSLKQTVVTLASSAGVLSTVQSAAQAVLQSGWSVLLP 900
901 TAEERARALSALLPCTVSGNEVNISPGRRFMIDLLVGSLMADGGLESALN 950
951 AAITAEIQDIEAKKEAQKEKEIDEQEASASTFHRSRTPLDKDLINTGIYE 1000
1001 SSGKQCLPLVQLIQQLLRNIASQTVARLKDVARRISSCLDFEQQSCERSA 1050
1051 SLDLLLRFQRLLISKLYPGEKIGPISDTSSPELMGVGSLLKKYTALVCTH 1100
1101 IGDILPVAASIASSSWQHFAEVACVMEGDFTGVLLPELVVSIVLLLSKNA 1150
1151 SLMQEAGAIPLLGGLLEHLDRFNHLAPGKERDDHEELAWPGIMESFFTGQ 1200
1201 NCRNNEEVTLIRKADLENHNKDGGFWTVIDGKVYGIKDFQTQSLTGNSIL 1250
1251 AQFAGEDPVVALEAALQFEDTQESMHAFCVGQYLEPDQEVVTIPDLGSLS 1300
1301 SPLIDTERNLGLLLGLHASYLAMSTPLSPVEVECAKWLQSSIFSGGLQTS 1350
1351 QIHYSYNEEKDEDHCSSPGGTPISKSRLCSHRWALGDHSQAFLQAIADNN 1400
1401 IQDYNVKDFLCQIERYCRQCHLTTPITFPPEHPVEEVGRLLLCCLLKHED 1450
1451 LGHVALSLVHVGTLGIEQVKHRTLPKSVVDVCRVVYQAKCSLIKTHQEQG 1500
1501 RSYKEVCAPVIERLRFLFNELRPAVCSDLSIMSKFKLLGSLPRWRRIAQK 1550
1551 IIRERRKKRVPKKPESIDSEEKIGNEESDLEEACVLPHSPINVDKRPISM 1600
1601 KSPKDKWQPLLNTVTGVHKYKWLKQNVQGLYPQSALLNTIVEFALKEEPV 1650
1651 DVEKMRKCLLKQLERAEVRLEGIDTILKLAAKSFLLPSVQYAMFCGWQRL 1700
1701 IPEGIDIGEPLTDCLRDVDLIPPFNRMLLEVTFGKLYAWAVQNIRSVLMD 1750
1751 ASARFKELGIQPVPLQTITNENPAGPSLGTIPQARFLLVMLSMLTLQHGA 1800
1801 NNLDLLLNSGTLALTQTALRLIGPTCDSVEDDMNASARGASATVLEETRK 1850
1851 ETAPVQLPVSGPELAAMMKIGTRVMRGVDWKWGDQDGPPPGLGRVIGELG 1900
1901 EDGWIRVQWDTGSTNSYRMGKEGKYDLKLVELPVSSQPSAEDSDTEDDSE 1950
1951 AEQGERNIHPTAMMLTSVINLLQTLCLSVGVHADIMQSEATKTLCGLLRM 2000
2001 LVESGTTDKPAPPDRLVAREQHRSWCTLGFVRSIALTPQACGALSSPRWI 2050
2051 TLLMKVVEGHAPFTAASLQRQILAVHLLQAVLPSWDKTERARDMKCLVEK 2100
2101 LFGFLGSLLTTCSSDVPLLRESTLRKRRARPQASLTATHSSTLAEEVVGL 2150
2151 LRTLHSLTQWNGLINKYINSQLCSVTQSYAGKTSERAQLEDYFPDSENLE 2200
2201 VGGLMAVLAVIGGIDGRLRLGGQVMHDEFGEGTVTRITPKGRITVQFCDM 2250
2251 RMCRVCPLNQLKPLPAVAFSVNNLPFTEPMLSVWAELVNLAGSKLEKHKT 2300
2301 KKSAKPAFAGQVDLDLLRSQQLKLYILKAGRALLSHQDKLRQILSQPAVQ 2350
2351 GTGTLQTDDGAAASPDLGDMSPEGPQPPMILLQQLLSSATQPSPVKAIFD 2400
2401 KQELEAAALALCQCLAVESTHPSSPGCEDCSSSEATTPVSVQHIHLARAK 2450
2451 KRRQSPAPALPIVVQLMEMGFPRKNIEFALKSLTGTSGNASGLPGVEALV 2500
2501 GWLLDHSDVQVTEFSDAETLSDEYSDEEVVEDVDDTPYPVAAGAVVTESQ 2550
2551 TYKKRADFLSNDDYAVYVRENVQVGMMVRCCRTYEEVCEGDVGKVIKLDR 2600
2601 DGLHDLNVQCDWQQKGGTYWVRYIHVELIGYPPPSSSSHIKIGDKVRVKA 2650
2651 SVTTPKYKWGSVTHQSVGLVKAFSANGKDIIVDFPQQSHWTGLLSEMELV 2700
2701 PSIHPGVTCDGCQTFPINGSRFKCRNCDDFDFCETCFKTKKHNTRHTFGR 2750
2751 INEPGQSAVFCGRSGKQLKRCHSSQPGMLLDSWSRMVKSLNVSSSVNQAS 2800
2801 RLIDGSEPCWQSSGSQGKHWIRLEIFPDVLVHRLKMIVDPADSSYMPSLV 2850
2851 VVSGGNSLNNLIELKTININQTDTTVPLLSDCAEYHRYIEIAIKQCRSSG 2900
2901 IDCKIHGLILLGRIRAEEEDLAAVPFLASDNEEEEDDKGSTGSLIRKKTP 2950
2951 GLESTATIRTKVFVWGLNDKDQLGGLKGSKIKVPSFSETLSALNVVQVAG 3000
3001 GSKSLFAVTVEGKVYSCGEATNGRLGLGMSSGTVPIPRQITALSSYVVKK 3050
3051 VAVHSGGRHATALTVDGKVFSWGEGDDGKLGHFSRMNCDKPRLIEALKTK 3100
3101 RIRDIACGSSHSAALTSSGELYTWGLGEYGRLGHGDNTTQLKPKMVKVLL 3150
3151 GHRVIQVACGSRDAQTLALTDEGLVFSWGDGDFGKLGRGGSEGCNIPQNI 3200
3201 ERLNGQGVCQIECGAQFSLALTKSGVVWTWGKGDYFRLGHGSDVHVRKPQ 3250
3251 VVEGLRGKKIVHVAVGALHCLAVTDSGQVYAWGDNDHGQQGNGTTTVNRK 3300
3301 PTLVQGLEGQKITRVACGSSHSVAWTTVDVATPSVHEPVLFQTARDPLGA 3350
3351 SYLGVPSDADSSSSSNKISGANNCKPNRPSLAKILLSLEGNLAKQQALSH 3400
3401 ILTALQIMYARDAVVGALMPAGMLAPVECPSFSSSAPASDVSAMASPMHM 3450
3451 EDSTLAADLEDRLSPNLWQEKREIVSSEDAVTPSAVTPSAPSASSRPFIP 3500
3501 VTDDPGAASIIAETMTKTKEDVESQNKTSGPEPQSLDEFTSLLIPDDTRV 3550
3551 VVELLKLSVCSRAGDKGREVLSAVLSGMGTAYPQVADMLLELCVTELEDV 3600
3601 ATDSQSGRLSSQPVVVESSHPYTDDTSTSGTVKIPGAEGLRVEFDRQCST 3650
3651 ERRHDPLTVMDGVNRIVSVRSGREWSDWSSELRIPGDELKWKFISDGSVN 3700
3701 GWGWRFTVYPIMPAAGPKDLLSDRCVLSCPSMDLVTCLLDFRLNLTSNRS 3750
3751 IVPRLAASLAACAQLSALAASHRMWALQRLRRLLTTEFGQSININRLLGE 3800
3801 NDGESRALSFTGSALAALVKGLPEALQRQFEYEDPIVRGGKQLLHSPFFK 3850
3851 VLVALACDLELDTLPCCAETHKWAWFRRYCMASRVAVALDKRTPLPRLFL 3900
3901 DEVAKKIRELMADSESMDVLHESHSIFKREQDEQLVQWMNRRPDDWTLSA 3950
3951 GGSGTIYGWGHNHRGQLGGIEGAKVKVPTPCEALATLRPVQLIGGEQTLF 4000
4001 AVTADGKLYATGYGAGGRLGIGGTESVSTPTLLESIQHVFIKKVAVNSGG 4050
4051 KHCLALSSEGEVYSWGEAEDGKLGHGNRSPCDRPRVIESLRGIEVVDVAA 4100
4101 GGAHSACVTAAGDLYTWGKGRYGRLGHSDSEDQLKPKLVEALQGHRVIDI 4150
4151 ACGSGDAQTLCLTDDDTVWSWGDGDYGKLGRGGSDGCKVPMKIDSLTGLG 4200
4201 VVKVECGSQFSVALTKSGAVYTWGKGDYHRLGHGSDDHVRRPRQVQGLQG 4250
4251 KKVIAIATGSLHCVCCTEDGEVYTWGDNDEGQLGDGTTNAIQRPRLVAAL 4300
4301 QGKKVNRVACGSAHTLAWSTSKPASAGKLPAQVPMEYNHLQEIPIIALRN 4350
4351 RLLLLHHISELFCPCIPMFDLEGSLDETGLGPSVGFDTLRGILISQGKEA 4400
4401 AFRKVVQATMVRDRQHGPVVELNRIQVKRSRSKGGLAGPDGTKSVFGQMC 4450
4451 AKMSSFSPDSLLLPHRVWKVKFVGESVDDCGGGYSESIAEICEELQNGLT 4500
4501 PLLIVTPNGRDESGANRDCYLLNPATRAPVHCSMFRFLGVLLGIAIRTGS 4550
4551 PLSLNLAEPVWKQLAGMSLTIADLSEVDKDFIPGLMYIRDNEATSEEFEA 4600
4601 MSLPFTVPSASGQDIQLSSKHTHITLDNRAEYVRLAINYRLHEFDEQVAA 4650
4651 VREGMARVVPVPLLSLFTGYELETMVCGSPDIPLHLLKSVATYKGIEPSA 4700
4701 SLVQWFWEVMESFSNTERSLFLRFVWGRTRLPRTIADFRGRDFVIQVLDK 4750
4751 YNPPDHFLPESYTCFFLLKLPRYSCKQVLEEKLKYAIHFCKSIDTDDYAR 4800
4801 IALTGEPAADDSSEDSDNEDADSFASDSTQDYLTGH 4836
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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