 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O95714 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MPSESFCLAAQARLDSKWLKTDIQLAFTRDGLCGLWNEMVKDGEIVYTGT 50
51 ESTQNGELPPRKDDSVEPSGTKKEDLNDKEKKDEEETPAPIYRAKSILDS 100
101 WVWGKQPDVNELKECLSVLVKEQQALAVQSATTTLSALRLKQRLVILERY 150
151 FIALNRTVFQENVKVKWKSSGISLPPVDKKSSRPAGKGVEGLARVGSRAA 200
201 LSFAFAFLRRAWRSGEDADLCSELLQESLDALRALPEASLFDESTVSSVW 250
251 LEVVERATRFLRSVVTGDVHGTPATKGPGSIPLQDQHLALAILLELAVQR 300
301 GTLSQMLSAILLLLQLWDSGAQETDNERSAQGTSAPLLPLLQRFQSIICR 350
351 KDAPHSEGDMHLLSGPLSPNESFLRYLTLPQDNELAIDLRQTAVVVMAHL 400
401 DRLATPCMPPLCSSPTSHKGSLQEVIGWGLIGWKYYANVIGPIQCEGLAN 450
451 LGVTQIACAEKRFLILSRNGRVYTQAYNSDTLAPQLVQGLASRNIVKIAA 500
501 HSDGHHYLALAATGEVYSWGCGDGGRLGHGDTVPLEEPKVISAFSGKQAG 550
551 KHVVHIACGSTYSAAITAEGELYTWGRGNYGRLGHGSSEDEAIPMLVAGL 600
601 KGLKVIDVACGSGDAQTLAVTENGQVWSWGDGDYGKLGRGGSDGCKTPKL 650
651 IEKLQDLDVVKVRCGSQFSIALTKDGQVYSWGKGDNQRLGHGTEEHVRYP 700
701 KLLEGLQGKKVIDVAAGSTHCLALTEDSEVHSWGSNDQCQHFDTLRVTKP 750
751 EPAALPGLDTKHIVGIACGPAQSFAWSSCSEWSIGLRVPFVVDICSMTFE 800
801 QLDLLLRQVSEGMDGSADWPPPQEKECVAVATLNLLRLQLHAAISHQVDP 850
851 EFLGLGLGSILLNSLKQTVVTLASSAGVLSTVQSAAQAVLQSGWSVLLPT 900
901 AEERARALSALLPCAVSGNEVNISPGRRFMIDLLVGSLMADGGLESALHA 950
951 AITAEIQDIEAKKEAQKEKEIDEQEANASTFHRSRTPLDKDLINTGICES 1000
1001 SGKQCLPLVQLIQQLLRNIASQTVARLKDVARRISSCLDFEQHSRERSAS 1050
1051 LDLLLRFQRLLISKLYPGESIGQTSDISSPELMGVGSLLKKYTALLCTHI 1100
1101 GDILPVAASIASTSWRHFAEVAYIVEGDFTGVLLPELVVSIVLLLSKNAG 1150
1151 LMQEAGAVPLLGGLLEHLDRFNHLAPGKERDDHEELAWPGIMESFFTGQN 1200
1201 CRNNEEVTLIRKADLENHNKDGGFWTVIDGKVYDIKDFQTQSLTGNSILA 1250
1251 QFAGEDPVVALEAALQFEDTRESMHAFCVGQYLEPDQEIVTIPDLGSLSS 1300
1301 PLIDTERNLGLLLGLHASYLAMSTPLSPVEIECAKWLQSSIFSGGLQTSQ 1350
1351 IHYSYNEEKDEDHCSSPGGTPASKSRLCSHRRALGDHSQAFLQAIADNNI 1400
1401 QDHNVKDFLCQIERYCRQCHLTTPIMFPPEHPVEEVGRLLLCCLLKHEDL 1450
1451 GHVALSLVHAGALGIEQVKHRTLPKSVVDVCRVVYQAKCSLIKTHQEQGR 1500
1501 SYKEVCAPVIERLRFLFNELRPAVCNDLSIMSKFKLLSSLPRWRRIAQKI 1550
1551 IRERRKKRVPKKPESTDDEEKIGNEESDLEEACILPHSPINVDKRPIAIK 1600
1601 SPKDKWQPLLSTVTGVHKYKWLKQNVQGLYPQSPLLSTIAEFALKEEPVD 1650
1651 VEKMRKCLLKQLERAEVRLEGIDTILKLASKNFLLPSVQYAMFCGWQRLI 1700
1701 PEGIDIGEPLTDCLKDVDLIPPFNRMLLEVTFGKLYAWAVQNIRNVLMDA 1750
1751 SAKFKELGIQPVPLQTITNENPSGPSLGTIPQARFLLVMLSMLTLQHGAN 1800
1801 NLDLLLNSGMLALTQTALRLIGPSCDNVEEDMNASAQGASATVLEETRKE 1850
1851 TAPVQLPVSGPELAAMMKIGTRVMRGVDWKWGDQDGPPPGLGRVIGELGE 1900
1901 DGWIRVQWDTGSTNSYRMGKEGKYDLKLAELPAAAQPSAEDSDTEDDSEA 1950
1951 EQTERNIHPTAMMFTSTINLLQTLCLSAGVHAEIMQSEATKTLCGLLRML 2000
2001 VESGTTDKTSSPNRLVYREQHRSWCTLGFVRSIALTPQVCGALSSPQWIT 2050
2051 LLMKVVEGHAPFTATSLQRQILAVHLLQAVLPSWDKTERARDMKCLVEKL 2100
2101 FDFLGSLLTTCSSDVPLLRESTLRRRRVRPQASLTATHSSTLAEEVVALL 2150
2151 RTLHSLTQWNGLINKYINSQLRSITHSFVGRPSEGAQLEDYFPDSENPEV 2200
2201 GGLMAVLAVIGGIDGRLRLGGQVMHDEFGEGTVTRITPKGKITVQFSDMR 2250
2251 TCRVCPLNQLKPLPAVAFNVNNLPFTEPMLSVWAQLVNLAGSKLEKHKIK 2300
2301 KSTKQAFAGQVDLDLLRCQQLKLYILKAGRALLSHQDKLRQILSQPAVQE 2350
2351 TGTVHTDDGAVVSPDLGDMSPEGPQPPMILLQQLLASATQPSPVKAIFDK 2400
2401 QELEAAALAVCQCLAVESTHPSSPGFEDCSSSEATTPVAVQHIRPARVKR 2450
2451 RKQSPVPALPIVVQLMEMGFSRRNIEFALKSLTGASGNASSLPGVEALVG 2500
2501 WLLDHSDIQVTELSDADTVSDEYSDEEVVEDVDDAAYSMSTGAVVTESQT 2550
2551 YKKRADFLSNDDYAVYVRENIQVGMMVRCCRAYEEVCEGDVGKVIKLDRD 2600
2601 GLHDLNVQCDWQQKGGTYWVRYIHVELIGYPPPSSSSHIKIGDKVRVKAS 2650
2651 VTTPKYKWGSVTHQSVGVVKAFSANGKDIIVDFPQQSHWTGLLSEMELVP 2700
2701 SIHPGVTCDGCQMFPINGSRFKCRNCDDFDFCETCFKTKKHNTRHTFGRI 2750
2751 NEPGQSAVFCGRSGKQLKRCHSSQPGMLLDSWSRMVKSLNVSSSVNQASR 2800
2801 LIDGSEPCWQSSGSQGKHWIRLEIFPDVLVHRLKMIVDPADSSYMPSLVV 2850
2851 VSGGNSLNNLIELKTININPSDTTVPLLNDCTEYHRYIEIAIKQCRSSGI 2900
2901 DCKIHGLILLGRIRAEEEDLAAVPFLASDNEEEEDEKGNSGSLIRKKAAG 2950
2951 LESAATIRTKVFVWGLNDKDQLGGLKGSKIKVPSFSETLSALNVVQVAGG 3000
3001 SKSLFAVTVEGKVYACGEATNGRLGLGISSGTVPIPRQITALSSYVVKKV 3050
3051 AVHSGGRHATALTVDGKVFSWGEGDDGKLGHFSRMNCDKPRLIEALKTKR 3100
3101 IRDIACGSSHSAALTSSGELYTWGLGEYGRLGHGDNTTQLKPKMVKVLLG 3150
3151 HRVIQVACGSRDAQTLALTDEGLVFSWGDGDFGKLGRGGSEGCNIPQNIE 3200
3201 RLNGQGVCQIECGAQFSLALTKSGVVWTWGKGDYFRLGHGSDVHVRKPQV 3250
3251 VEGLRGKKIVHVAVGALHCLAVTDSGQVYAWGDNDHGQQGNGTTTVNRKP 3300
3301 TLVQGLEGQKITRVACGSSHSVAWTTVDVATPSVHEPVLFQTARDPLGAS 3350
3351 YLGVPSDADSSAASNKISGASNSKPNRPSLAKILLSLDGNLAKQQALSHI 3400
3401 LTALQIMYARDAVVGALMPAAMIAPVECPSFSSAAPSDASAMASPMNGEE 3450
3451 CMLAVDIEDRLSPNPWQEKREIVSSEDAVTPSAVTPSAPSASARPFIPVT 3500
3501 DDLGAASIIAETMTKTKEDVESQNKAAGPEPQALDEFTSLLIADDTRVVV 3550
3551 DLLKLSVCSRAGDRGRDVLSAVLSGMGTAYPQVADMLLELCVTELEDVAT 3600
3601 DSQSGRLSSQPVVVESSHPYTDDTSTSGTVKIPGAEGLRVEFDRQCSTER 3650
3651 RHDPLTVMDGVNRIVSVRSGREWSDWSSELRIPGDELKWKFISDGSVNGW 3700
3701 GWRFTVYPIMPAAGPKELLSDRCVLSCPSMDLVTCLLDFRLNLASNRSIV 3750
3751 PRLAASLAACAQLSALAASHRMWALQRLRKLLTTEFGQSININRLLGEND 3800
3801 GETRALSFTGSALAALVKGLPEALQRQFEYEDPIVRGGKQLLHSPFFKVL 3850
3851 VALACDLELDTLPCCAETHKWAWFRRYCMASRVAVALDKRTPLPRLFLDE 3900
3901 VAKKIRELMADSENMDVLHESHDIFKREQDEQLVQWMNRRPDDWTLSAGG 3950
3951 SGTIYGWGHNHRGQLGGIEGAKVKVPTPCEALATLRPVQLIGGEQTLFAV 4000
4001 TADGKLYATGYGAGGRLGIGGTESVSTPTLLESIQHVFIKKVAVNSGGKH 4050
4051 CLALSSEGEVYSWGEAEDGKLGHGNRSPCDRPRVIESLRGIEVVDVAAGG 4100
4101 AHSACVTAAGDLYTWGKGRYGRLGHSDSEDQLKPKLVEALQGHRVVDIAC 4150
4151 GSGDAQTLCLTDDDTVWSWGDGDYGKLGRGGSDGCKVPMKIDSLTGLGVV 4200
4201 KVECGSQFSVALTKSGAVYTWGKGDYHRLGHGSDDHVRRPRQVQGLQGKK 4250
4251 VIAIATGSLHCVCCTEDGEVYTWGDNDEGQLGDGTTNAIQRPRLVAALQG 4300
4301 KKVNRVACGSAHTLAWSTSKPASAGKLPAQVPMEYNHLQEIPIIALRNRL 4350
4351 LLLHHLSELFCPCIPMFDLEGSLDETGLGPSVGFDTLRGILISQGKEAAF 4400
4401 RKVVQATMVRDRQHGPVVELNRIQVKRSRSKGGLAGPDGTKSVFGQMCAK 4450
4451 MSSFGPDSLLLPHRVWKVKFVGESVDDCGGGYSESIAEICEELQNGLTPL 4500
4501 LIVTPNGRDESGANRDCYLLSPAARAPVHSSMFRFLGVLLGIAIRTGSPL 4550
4551 SLNLAEPVWKQLAGMSLTIADLSEVDKDFIPGLMYIRDNEATSEEFEAMS 4600
4601 LPFTVPSASGQDIQLSSKHTHITLDNRAEYVRLAINYRLHEFDEQVAAVR 4650
4651 EGMARVVPVPLLSLFTGYELETMVCGSPDIPLHLLKSVATYKGIEPSASL 4700
4701 IQWFWEVMESFSNTERSLFLRFVWGRTRLPRTIADFRGRDFVIQVLDKYN 4750
4751 PPDHFLPESYTCFFLLKLPRYSCKQVLEEKLKYAIHFCKSIDTDDYARIA 4800
4801 LTGEPAADDSSDDSDNEDVDSFASDSTQDYLTGH 4834
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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