 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02505 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MQLLGLLGLLWMLKASPWATGTLSTATSISQVPFPRAEAASAVLSNSPHS 50
51 RDLAGWPLGVPQLASPAPGHRENAPMTLTTSPHDTLISETLLNSPVSSNT 100
101 STTPTSKFAFKVETTPPTVLVYSATTECVYPTSFIITISHPTSICVTTTQ 150
151 VAFTSSYTSTPVTQKPVTTVTSTYSMTTTEKGTSAMTSSPSTTTARETPI 200
201 VTVTPSSVSATDTTFHTTISSTTRTTERTPLPTGSIHTTTSPTPVFTTLK 250
251 TAVTSTSPITSSITSTNTVTSMTTTASQPTATNTLSSPTRTILSSTPVLS 300
301 TETITSGITNTTPLSTLVTTLPTTISRSTPTSETTYTTSPTSTVTDSTTK 350
351 IAYSTSMTGTLSTETSLPPTSSSLPTTETATTPMTNLVTTTTEISSHSTP 400
401 SFSSSTIYSTVSTSTTAISSLPPTSGTMVTSTTMTPSSLSTDIPFTTPTT 450
451 ITHHSVGSTGFLTTATDLTSTFTVSSSSAMSTSVIPSSPSIQNTETSSLV 500
501 SMTSATTPNVRPTFVSTLSTPTSSLLTTFPATYSFSSSMSASSAGTTHTE 550
551 SISSPPASTSTLHTTAESTLAPTTTTSFTTSTTMEPPSTTAATTGTGQTT 600
601 FTSSTATFPETTTPTPTTDMSTESLTTAMTSPPITSSVTSTNTVTSMTTT 650
651 TSPPTTTNSFTSLTSMPLSSTPVPSTEVVTSGTINTIPPSILVTTLPTPN 700
701 ASSMTTSETTYPNSPTGPGTNSTTEITYPTTMTETSSTATSLPPTSPLVS 750
751 TAKTAKTPTTNLVTTTTKTTSHSTTSFTSSTVYSTASTYTTAITSVPTTL 800
801 GTMVTSTSMISSTVSTGIPTSQPTTITPSSVGISGSLPMMTDLTSVYTVS 850
851 NMSARPTTVIPSSPTVQNTEISISVSMTSATTPSGGPTFTSTENTPTRSL 900
901 LTSFPMTHSFSSSMSESSAGTTHTESISSPRGTTSTLHTTVESTPSPTTT 950
951 TSFTTSTMMEPPSSTVSTTGRGQTTFPSSTATFPETTTLTPTTDISTVSL 1000
1001 TTAMTSPPPVSSSITPTNTMTSMRTTTYWPTATNTLSPLTSSILSSTPVP 1050
1051 STEMITSHTTNTTPLSTLVTTLLTTITRSTPTSETTYPTSPTSIVSDSTT 1100
1101 EITYSTSITGTLSTATTLPPTSSSLPTTETATMTPTTTLITTTPNTTSLS 1150
1151 TPSFTSSTIYSTVSTSTTAISSASPTSGTMVTSTTMTPSSLSTDTPSTTP 1200
1201 TTITYPSVGSTGFLTTATDLTSTFTVSSSSAMSTSVIPSSPSIQNTETSS 1250
1251 LVSMTSATTPSLRPTITSTDSTLTSSLLTTFPSTYSFSSSMSASSAGTTH 1300
1301 TETISSLPASTNTIHTTAESALAPTTTTSFTTSPTMEPPSTTVATTGTGQ 1350
1351 TTFPSSTATFLETTTLTPTTDFSTESLTTAMTSTPPITSSITPTDTMTSM 1400
1401 RTTTSWPTATNTLSPLTSSILSSTPVPSTEVTTSHTTNTNPVSTLVTTLP 1450
1451 ITITRSTLTSETAYPSSPTSTVTESTTEITYPTTMTETSSTATSLPPTSS 1500
1501 LVSTAETAKTPTTNLVTTTTKTTSHSTTSFTSSTIYSTASTPTTAITSVP 1550
1551 TTLGTMVTSTSMIPSTVSTGIPTSQPTTITPSSVGISGSLPMMTDLTSVY 1600
1601 TVSSMSARPTSVIPSSPTVQNTETSIFVSMMSATTPSGGPTFTSTENTPT 1650
1651 RSLLTSFPVTHSFSSSMSASSVGTTHTQSISSPPAITSTLHTTAESTPSP 1700
1701 TTTMSFTTFTKMETPSSTVATTGTGQTTFTSSTATSPKTTTLTPTSDIST 1750
1751 GSFKTAVSSTPPITSSITSTYTVTSMTTTTPLGPTATNTLPSFTSSVSSS 1800
1801 TPVPSTEAITSGTTNTTPLSTLVTTFSNSDTSSTPTSETTYPTSLTSALT 1850
1851 DSTTRTTYSTNMTGTLSTVTSLRPTSSSLLTTVTATVPTTNLVTTTTKIT 1900
1901 SHSTPSFTSSIATTETPSHSTPRFTSSITTTETPSHSTPRFTSSITNTKT 1950
1951 TSHSSPSFTSSITTTETTSHNTPSLTSSITTTKTTSHSTPSYTSLITTTT 2000
2001 TTSHSTPSFTSSITTTETTSHNTPSLTSSITTTETTSHSTPSFTSSITTE 2050
2051 TTSHSTPSFTSLITITEITSHSTLSYTTSITTTETPSHSTLSFTSSITTT 2100
2101 ETTSHSTPSFTSSITTSEMPSHSTPSFTSSITTTENATHSTPNFTSSITT 2150
2151 TETTSHSTPSFTSLITTTETTSHRWGTTETTSYSTPSFTSSNTITETTSH 2200
2201 STPSYITSITTTETPSSSTPSFSSSITTTETTSHSTPGFTSSITTTETTS 2250
2251 HSTPSFTSSITTTETTSHDTPSFTSSITTSETPSHSTPSSTSLITTTKTT 2300
2301 SHSTPSFTSSITTTETTSHSAHSFTSSITTTETTSHNTRSFTSSITTTET 2350
2351 NSHSTTSFTSSITTTETTSHSTPSFSSSITTTETPLHSTPGLTSWVTTTK 2400
2401 TTSHITPGLTSSITTTETTSHSTPGFTSSITTTETTSESTPSLSSSTIYS 2450
2451 TVSTSTTAITSHFTTSETAVTPTPVTPSSLSTDIPTTSLRTLTPSSVGTS 2500
2501 TSLTTTTDFPSIPTDISTLPTRTHIISSSPSIQSTETSSLVGTTSPTMST 2550
2551 VRMTLRITENTPISSFSTSIVVIPETPTQTPPVLTSATGTQTSPAPTTVT 2600
2601 FGSTDSSTSTLHTLTPSTALSTIVSTSQVPIPSTHSSTLQTTPSTPSLQT 2650
2651 SLTSTSEFTTESFTRGSTSTNAILTSFSTIIWSSTPTIIMSSSPSSASIT 2700
2701 PVFSTTIHSVPSSPYIFSTENVGSASITGFPSLSSSATTSTSSTSSSLTT 2750
2751 ALTEITPFSYISLPSTTPCPGTITITIVPASPTDPCVEMDPSTEATSPPT 2800
2801 TPLTVFPFTTEMVTCPTSISIQTTLTTYMDTSSMMPESESSISPNASSST 2850
2851 GTGTVPTNTVFTSTRLPTSETWLSNSSVIPLPLPGVSTIPLTMKPSSSLP 2900
2901 TILRTSSKSTHPSPPTTRTSETPVATTQTPTTLTSRRTTRITSQMTTQST 2950
2951 LTTTAGTCDNGGTWEQGQCACLPGFSGDRCQLQTRCQNGGQWDGLKCQCP 3000
3001 STFYGSSCEFAVEQVDLDVVETEVGMEVSVDQQFSPDLNDNTSQAYRDFN 3050
3051 KTFWNQMQKIFADMQGFTFKGVEILSLRNGSIVVDYLVLLEMPFSPQLES 3100
3101 EYEQVKTTLKEGLQNASQDVNSCQDSQTLCFKPDSIKVNNNSKTELTPAA 3150
3151 ICRRAAPTGYEEFYFPLVEATRLRCVTKCTSGVDNAIDCHQGQCVLETSG 3200
3201 PTCRCYSTDTHWFSGPRCEVAVHWRALVGGLTAGAALLVLLLLALGVRAV 3250
3251 RSGWWGGQRRGRSWDQDRKWFETWDEEVVGTFSNWGFEDDGTDKDTNFYV 3300
3301 ALENVDTTMKVHIKRPEMTSSSV 3323
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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