 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98091 from www.uniprot.org...
The NucPred score for your sequence is 0.57 (see score help below)
1 MKLIFLCLVVALCIFCKNGEALFYRLNSDDKIAERKSEIQKRESVGTESF 50
51 GWEVGAGRGNAAFAFGASGSSSFGDSSFSSKTVEGDQVARSGRSISSDLG 100
101 DTGLRSGDTFVGDSSGNLETGLGSSGQQGLKIDELERDSLSGTASVGAGF 150
151 KDLGSDVSSSVETGSFGWEVGAGRVNAAFDFGASGSSSFGDSDFSSKTVE 200
201 GNRVVRSEGSISSDLGDTSLRSVSTDVGDRSENLESNLGSSGQQGLEINE 250
251 LGGDGLSGSASVEDELKGFASDASSSGGNIWSSNSGSGEGNKGEAGLGTS 300
301 GQNVSDETGVSSTGITSSSDYSTSGPLSTPEKGSHIPEATPKYSETNAII 350
351 GEASTWGKGAYKAFNGRVFSFESSCTYTFCRHCVESGGDFNIEIKRNNDS 400
401 EIEKITVIIDNNDVSIFGDILLVNGESVQIPYNNKLIHIKKYGEHNVLNS 450
451 RRGILSLMWDKNNKLSLTLHKQYPTCGLCGNFNSTPGDDINEHIADSKIP 500
501 DDCSKAVSKSYEVCEDGVQYCNKIIGTYFEKCGKVSTLSSDYKMICIDEY 550
551 CQSRDRTSTCDTYSELSRLCASDGPGTFESWRDDPDVVCEKPICPEKHIY 600
601 KECGPSNPATCSNVAPFQDTECVSGCTCPEGYLLDDIGEKGRCVLKSDCP 650
651 CESNGKVYQSGEVREGSCGSLCTCQEAKWSCTKTLCPGRCKIEGSLITTF 700
701 DGVKYNHPGNCHFLAIHDKDWSISVELRPCPSGQSGTCLNSVTLLLNSSV 750
751 QVDKYVFNRDGTVTNDKFGNLGYYYSDKIQIFNASSSYLQAETYFHGKMQ 800
801 IQIFPVMQLYVSMPPNQFTDTVGLCGSHNNRAEDDFMSSQNILEKTSQAF 850
851 ASSWEMMPCPKASTASCISIEKERFAERHCGILLDLSGPFASCHSIVDPK 900
901 PYHEECKKYTCTCENSQDCLCTILGNYVKACAEKETSMVGWRAGLCDQSC 950
951 PSGLVFKYNVKTCNSSCRSLSERDKSCDMEGISVDGCTCPDGMYKNNEGN 1000
1001 CVSKSQCDCYINDEVMQPGKLIHIDDNKCVCRDGILLCQTPIDLTLQNCS 1050
1051 GGAEYVDCRNPKAQRRVDSTCSTRNIPSFDENLPCKRGCYCPEGMVRNSK 1100
1101 GSCVFPDDCPCSFGGREYDQGSVTSVGCNKCTCIKGSWNCTQNECQTTCH 1150
1151 IYGEGHVRTFDGKSYSFDGLCQYSFIEDYCGRENGTFRILTESVPCCEDG 1200
1201 LTCSRKIIVAFQDQNIVLHDGKVTAVKTTESKECELNGNSYSVHTVGLYL 1250
1251 ILKFLNGITIIWDKNTRISVILDPRWNGQVCGLCGNNNGDLKDDFTTRYS 1300
1301 SVAAGTLEFGNSWKTSQECSDTVAQTFPCDSNPYCKAWAVRKCEIIRDST 1350
1351 FRECHNKVDPNEYYDACIEEACACDMEGKYLGFCTAVAMYAEACSAVGVC 1400
1401 VTWRKPDLCPVYCDYYNAPGEFSWHYEPCGTVTAKTCKDRVIGQKFSALL 1450
1451 EGCYAKCPDSAPYLDENTMKCVSLAECSCFYNDIVPAGGVIQDNCGRTCY 1500
1501 CIAGELECSETAPTNSTYTVSTTTATSILSTKAAITLATNSSGTVASIPG 1550
1551 ITSSSEITGTTLSFLSETFTTGVTRTPAPITSTAGSVGTTGLVGSTFTSS 1600
1601 GRISGSTGVSVSTITETEDGSTGDTGFRVGGTEGPTAPVRGEEDGTPGQP 1650
1651 STGVTSSEKQGPQELQKASQPPLGARAQMQTQLSQTQPLEANQRLPDHQL 1700
1701 VKQLENEAEQLEVKMLPPLELLVITLLEQQEIIYSEKELVYLTFLTSSMP 1750
1751 ESTTKRRRKTGIYAAGSEKNVHLYETTRTIIIGSGTSIPPSGAPVTPEPP 1800
1801 LISTGASAGPPASSESTVTLPGATGTDVLRSGTSLPVSGGAVTPASSPGG 1850
1851 SSATAGPGVGSETTVQVSGATGTDVLRSGTSLPVSGAAVSPGSSPGRSRA 1900
1901 TAVSGEGSQPTVALSGATGTSAGPSGTRSASSGIPATPGSTTGRAAGAGT 1950
1951 PGVDSQQTASLPAAARPTALGPGTSAPSGETSESRSSVPGGSETTQQPGA 2000
2001 GSESPTLSPGVTRTTALRGSETRVPSTGVSGLPGSTQGGSAATGGSGAGS 2050
2051 GPTAPVSGETRTSVISGTNVPVSGAPVTPGSSAGSSGAPGAGGPGSETAS 2100
2101 PLSGAAGTSATGSGTSIPPSGAPVTPEPPLISTGASAGPPASSESTVTLP 2150
2151 GATGTDVLRSGTSLPVSGGAVTPASSPGGSSATAGPAVGSETTVQVSGAT 2200
2201 GTDVLRSGTSLPVSGAAVSPGSSPGRSRATAVSGEGSQPTVALSGATGTS 2250
2251 AGPSGTRSSSSGIPATPGSTTGRAAGAGTPGVDSQQTARLPAAARTTAPG 2300
2301 SGSSAPSGETSESRSSVPGGSETTQQPGAGSEPTTLSPGVTRTTALRGSE 2350
2351 TGVPSTGVSGLPGSTQGGSAATGSSGAGSEPTAPVSGETRTSVISGANVP 2400
2401 VSGAPVTPGSSAGSSAAPGARAPGSETTSPLSGAAGTSAIGSGTSIPPSG 2450
2451 APVTPEPPLRSTEASARPPASSESTVTLPGATGTDVLRPGTSLPVSGGAV 2500
2501 TPASSPGGSSATAGPGVGSETTVQVSGATGADVLRSGTSLPVSGAAVSPG 2550
2551 SSPGRSGATAVSGEGSQPTVALSGATGTSAGPSGTRFSSSGIPVTPGSTT 2600
2601 GRAAGAGTPGVDSQQTARLPAAARTTAPGSGSSAPSGETSESRSSVPGGS 2650
2651 ETTQQPGAGSEPTTLSPGVTRTTALRGSETGVPSTGVSGLPGSTQGGSAA 2700
2701 TGSSGAGSEPTAPVSGETRTSVISGANVPVSGAPVTPGSSAGSSAAPGAR 2750
2751 APGSETTSPLSGAAGTSAIGSGTSIPPSGAPVTPEPPLRSTEASARPPAS 2800
2801 SESTVTLPGATGTDVLRPGTSLPVSGGAVTPASSPGGSSATAGPGVGSET 2850
2851 TVQVSGATGADVLRSGTSLPVSGAAVSPGSSPGRSGATAVSGEGSQPTVA 2900
2901 LSGATGTSAGPSGTRFSSSGIPATPGSTTGRAAGAGTPGVDSQQTARLPA 2950
2951 AARTTAPGSGSSAPSGETSESRSSVPGGSETTQQPGAGSEPTTLSPGVTR 3000
3001 TTALRGSETGVPSTGVSGLPGSTQGGSAATGSSGAGSEPTAPVSGETRTS 3050
3051 VISGANVPVSGAPVTPGSSAGSSAAPGARAPGSETTSPLSGAAGTSAIGS 3100
3101 GTSIPPSGAPVTPEPPLRSTEASARPPASSESTVTLPGATGTDVLRPGTS 3150
3151 LPVSGGAVTPASSPGGSSATAGPGVGSETTVQVSGETATHVKGSNTNESS 3200
3201 TEISKTTGATAGLTLTSKSSIISSATRALSSSVTKATVTYDVVSWTTGSS 3250
3251 SGRSRTNVIESASSVSSAEQIAPSLSTNGLAGTTRISDVVARTIRPSYGI 3300
3301 SGTTGSSIDEIVTTNTSPEFTETNRFSVVRLRTTRPSSGEIGTTLTESST 3350
3351 SASSSEESGTTGSIAGLRRTNRISLIRSGTTRPSSGETQTTVIESRVSGS 3400
3401 SDQGLGTIGSTAGLMRTTRISVVVSGTTGPSSGKTGSTLSEFRTSGSLVK 3450
3451 GSETTESTTGLARMTRISXGGSRTTRPSSGETGTTVIESRTSGSPSEGLG 3500
3501 RTGSTAGLTRTTSISVVGSATTEPSSRETETTVTESXNNGSLGEGSGTTG 3550
3551 AIAGLTRTTRISGVGSGTTRPSSGETRTTVIKSITRRTSAEGSETTGSAG 3600
3601 GLIIATRISSADLLTPGPLSGETRTTVIGSGTSGKSGEVSGLTQSPAELT 3650
3651 TTTRISHVASGTRAPSSGMTRTTVTSGVASRTSGLSSGEKGTSVTETRTS 3700
3701 GSSIEGSQTTGTADRLTITTRTSVVVSGTDAPSSGTSGTIRSSVDLTGTT 3750
3751 KVSVVGTGTIEPSTVESWTTEPRDLGSSTTLFSAGAIGTTRPGTSGASRP 3800
3801 SVVGSETAGPLSAKTETTVIRSGSSGSSLEGRGTSGSTDGLTGTTTISFV 3850
3851 GLGTTGPSARGSRPTGKGDIRSSTTVSSVDATGNIRSGGSGTTGPSIVGS 3900
3901 ETVGPSSGEAGTTATGSGTSGKSAERLGTTVSTDGLRRTTRISLVSLGTT 3950
3951 GPSSGVMRTTQTSIVGLETTRSSTGVLATTSTSAEGLXTTGPSPGGLWTT 4000
4001 GTSVEGSETTESSTGKITGARRTTWESGSEVATYEGTSGKFSKAAISGSS 4050
4051 HTEATTLIVSNSTSGTGLRPEDNTAVAGGQATGRVTGTTKVIPGTTVAPG 4100
4101 SSNTESTTSLGESRTRIGRITGATTGTSERSSPGSKTGNTGAISGTTVAP 4150
4151 RSSNTGATTSLGSGETSQGGIKIVTMGVTTGTTIAPGSSNTKATTPTEVR 4200
4201 TTTEVRTATETTTSRHSSDATGSGIQTGITGTGSGTTSSPGGFNAEATTF 4250
4251 KEHVRTTETRILSGTTRGASGTTVIPESSNTGTSTGVGRQTSTAVVSGRV 4300
4301 TGVSESSSPGTSKEASETTTGPGISTTGSTSKSNRITTSSRIPYPETTVV 4350
4351 ATGEQETETKTGCTTSLPPPPACYGPLGEKKSPGDIWTANCHKCTCTDAE 4400
4401 TVDCKLKECPSPPTCKPEERLVKFKDNDTCCEIAYCEPRTCLFNNNDYEV 4450
4451 GASFADPNNPCISYSCHNTGFVAVVQDCPKQTWCAEEDRVYDSTKCCYTC 4500
4501 KPYCRSSSVNVTVNYNGCKKKVEMARCAGECKKTIKYDYDIFQLKNSCLC 4550
4551 CQEENYEYREIDLDCPDGGTIPYRYRHIITCSCLDICQQSMTSTVS 4596
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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