 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q99698 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MSTDSNSLAREFLTDVNRLCNAVVQRVEAREEEEEETHMATLGQYLVHGR 50
51 GFLLLTKLNSIIDQALTCREELLTLLLSLLPLVWKIPVQEEKATDFNLPL 100
101 SADIILTKEKNSSSQRSTQEKLHLEGSALSSQVSAKVNVFRKSRRQRKIT 150
151 HRYSVRDARKTQLSTSDSEANSDEKGIAMNKHRRPHLLHHFLTSFPKQDH 200
201 PKAKLDRLATKEQTPPDAMALENSREIIPRQGSNTDILSEPAALSVISNM 250
251 NNSPFDLCHVLLSLLEKVCKFDVTLNHNSPLAASVVPTLTEFLAGFGDCC 300
301 SLSDNLESRVVSAGWTEEPVALIQRMLFRTVLHLLSVDVSTAEMMPENLR 350
351 KNLTELLRAALKIRICLEKQPDPFAPRQKKTLQEVQEDFVFSKYRHRALL 400
401 LPELLEGVLQILICCLQSAASNPFYFSQAMDLVQEFIQHHGFNLFETAVL 450
451 QMEWLVLRDGVPPEASEHLKALINSVMKIMSTVKKVKSEQLHHSMCTRKR 500
501 HRRCEYSHFMHHHRDLSGLLVSAFKNQVSKNPFEETADGDVYYPERCCCI 550
551 AVCAHQCLRLLQQASLSSTCVQILSGVHNIGICCCMDPKSVIIPLLHAFK 600
601 LPALKNFQQHILNILNKLILDQLGGAEISPKIKKAACNICTVDSDQLAQL 650
651 EETLQGNLCDAELSSSLSSPSYRFQGILPSSGSEDLLWKWDALKAYQNFV 700
701 FEEDRLHSIQIANHICNLIQKGNIVVQWKLYNYIFNPVLQRGVELAHHCQ 750
751 HLSVTSAQSHVCSHHNQCLPQDVLQIYVKTLPILLKSRVIRDLFLSCNGV 800
801 SQIIELNCLNGIRSHSLKAFETLIISLGEQQKDASVPDIDGIDIEQKELS 850
851 SVHVGTSFHHQQAYSDSPQSLSKFYAGLKEAYPKRRKTVNQDVHINTINL 900
901 FLCVAFLCVSKEAESDRESANDSEDTSGYDSTASEPLSHMLPCISLESLV 950
951 LPSPEHMHQAADIWSMCRWIYMLSSVFQKQFYRLGGFRVCHKLIFMIIQK 1000
1001 LFRSHKEEQGKKEGDTSVNENQDLNRISQPKRTMKEDLLSLAIKSDPIPS 1050
1051 ELGSLKKSADSLGKLELQHISSINVEEVSATEAAPEEAKLFTSQESETSL 1100
1101 QSIRLLEALLAICLHGARTSQQKMELELPNQNLSVESILFEMRDHLSQSK 1150
1151 VIETQLAKPLFDALLRVALGNYSADFEHNDAMTEKSHQSAEELSSQPGDF 1200
1201 SEEAEDSQCCSFKLLVEEEGYEADSESNPEDGETQDDGVDLKSETEGFSA 1250
1251 SSSPNDLLENLTQGEIIYPEICMLELNLLSASKAKLDVLAHVFESFLKII 1300
1301 RQKEKNVFLLMQQGTVKNLLGGFLSILTQDDSDFQACQRVLVDLLVSLMS 1350
1351 SRTCSEELTLLLRIFLEKSPCTKILLLGILKIIESDTTMSPSQYLTFPLL 1400
1401 HAPNLSNGVSSQKYPGILNSKAMGLLRRARVSRSKKEADRESFPHRLLSS 1450
1451 WHIAPVHLPLLGQNCWPHLSEGFSVSLWFNVECIHEAESTTEKGKKIKKR 1500
1501 NKSLILPDSSFDGTESDRPEGAEYINPGERLIEEGCIHIISLGSKALMIQ 1550
1551 VWADPHNATLIFRVCMDSNDDMKAVLLAQVESQENIFLPSKWQHLVLTYL 1600
1601 QQPQGKRRIHGKISIWVSGQRKPDVTLDFMLPRKTSLSSDSNKTFCMIGH 1650
1651 CLSSQEEFLQLAGKWDLGNLLLFNGAKVGSQEAFYLYACGPNHTSVMPCK 1700
1701 YGKPVNDYSKYINKEILRCEQIRELFMTKKDVDIGLLIESLSVVYTTYCP 1750
1751 AQYTIYEPVIRLKGQMKTQLSQRPFSSKEVQSILLEPHHLKNLQPTEYKT 1800
1801 IQGILHEIGGTGIFVFLFARVVELSSCEETQALALRVILSLIKYNQQRVH 1850
1851 ELENCNGLSMIHQVLIKQKCIVGFYILKTLLEGCCGEDIIYMNENGEFKL 1900
1901 DVDSNAIIQDVKLLEELLLDWKIWSKAEQGVWETLLAALEVLIRADHHQQ 1950
1951 MFNIKQLLKAQVVHHFLLTCQVLQEYKEGQLTPMPREVCRSFVKIIAEVL 2000
2001 GSPPDLELLTIIFNFLLAVHPPTNTYVCHNPTNFYFSLHIDGKIFQEKVR 2050
2051 SIMYLRHSSSGGRSLMSPGFMVISPSGFTASPYEGENSSNIIPQQMAAHM 2100
2101 LRSRSLPAFPTSSLLTQSQKLTGSLGCSIDRLQNIADTYVATQSKKQNSL 2150
2151 GSSDTLKKGKEDAFISSCESAKTVCEMEAVLSAQVSVSDVPKGVLGFPVV 2200
2201 KADHKQLGAEPRSEDDSPGDESCPRRPDYLKGLASFQRSHSTIASLGLAF 2250
2251 PSQNGSAAVGRWPSLVDRNTDDWENFAYSLGYEPNYNRTASAHSVTEDCL 2300
2301 VPICCGLYELLSGVLLILPDVLLEDVMDKLIQADTLLVLVNHPSPAIQQG 2350
2351 VIKLLDAYFARASKEQKDKFLKNRGFSLLANQLYLHRGTQELLECFIEMF 2400
2401 FGRHIGLDEEFDLEDVRNMGLFQKWSVIPILGLIETSLYDNILLHNALLL 2450
2451 LLQILNSCSKVADMLLDNGLLYVLCNTVAALNGLEKNIPMSEYKLLACDI 2500
2501 QQLFIAVTIHACSSSGSQYFRVIEDLIVMLGYLQNSKNKRTQNMAVALQL 2550
2551 RVLQAAMEFIRTTANHDSENLTDSLQSPSAPHHAVVQKRKSIAGPRKFPL 2600
2601 AQTESLLMKMRSVANDELHVMMQRRMSQENPSQATETELAQRLQRLTVLA 2650
2651 VNRIIYQEFNSDIIDILRTPENVTQSKTSVFQTEISEENIHHEQSSVFNP 2700
2701 FQKEIFTYLVEGFKVSIGSSKASGSKQQWTKILWSCKETFRMQLGRLLVH 2750
2751 ILSPAHAAQERKQIFEIVHEPNHQEILRDCLSPSLQHGAKLVLYLSELIH 2800
2801 NHQGELTEEELGTAELLMNALKLCGHKCIPPSASTKADLIKMIKEEQKKY 2850
2851 ETEEGVNKAAWQKTVNNNQQSLFQRLDSKSKDISKIAADITQAVSLSQGN 2900
2901 ERKKVIQHIRGMYKVDLSASRHWQELIQQLTHDRAVWYDPIYYPTSWQLD 2950
2951 PTEGPNRERRRLQRCYLTIPNKYLLRDRQKSEDVVKPPLSYLFEDKTHSS 3000
3001 FSSTVKDKAASESIRVNRRCISVAPSRETAGELLLGKCGMYFVEDNASDT 3050
3051 VESSSLQGELEPASFSWTYEEIKEVHKRWWQLRDNAVEIFLTNGRTLLLA 3100
3101 FDNTKVRDDVYHNILTNNLPNLLEYGNITALTNLWYTGQITNFEYLTHLN 3150
3151 KHAGRSFNDLMQYPVFPFILADYVSETLDLNDLLIYRNLSKPIAVQYKEK 3200
3201 EDRYVDTYKYLEEEYRKGAREDDPMPPVQPYHYGSHYSNSGTVLHFLVRM 3250
3251 PPFTKMFLAYQDQSFDIPDRTFHSTNTTWRLSSFESMTDVKELIPEFFYL 3300
3301 PEFLVNREGFDFGVRQNGERVNHVNLPPWARNDPRLFILIHRQALESDYV 3350
3351 SQNICQWIDLVFGYKQKGKASVQAINVFHPATYFGMDVSAVEDPVQRRAL 3400
3401 ETMIKTYGQTPRQLFHMAHVSRPGAKLNIEGELPAAVGLLVQFAFRETRE 3450
3451 QVKEITYPSPLSWIKGLKWGEYVGSPSAPVPVVCFSQPHGERFGSLQALP 3500
3501 TRAICGLSRNFCLLMTYSKEQGVRSMNSTDIQWSAILSWGYADNILRLKS 3550
3551 KQSEPPVNFIQSSQQYQVTSCAWVPDSCQLFTGSKCGVITAYTNRFTSST 3600
3601 PSEIEMETQIHLYGHTEEITSLFVCKPYSILISVSRDGTCIIWDLNRLCY 3650
3651 VQSLAGHKSPVTAVSASETSGDIATVCDSAGGGSDLRLWTVNGDLVGHVH 3700
3701 CREIICSVAFSNQPEGVSINVIAGGLENGIVRLWSTWDLKPVREITFPKS 3750
3751 NKPIISLTFSCDGHHLYTANSDGTVIAWCRKDQQRLKQPMFYSFLSSYAA 3800
3801 G 3801
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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