 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q62059 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MLINMKGILWMCSTLLLTHALHQAKMETSPPVKGSLSGKVVLPCHFSTLP 50
51 TLPPNYNTSEFLRIKWSKMEVDKNGKDIKETTVLVAQNGNIKIGQDYKGR 100
101 VSVPTHPDDVGDASLTMVKLRASDAAVYRCDVMYGIEDTQDTMSLAVDGV 150
151 VFHYRAATSRYTLNFAAAQQACLDIGAVIASPEQLFAAYEDGFEQCDAGW 200
201 LSDQTVRYPIRAPREGCYGDMMGKEGVRTYGFRSPQETYDVYCYVDHLDG 250
251 DVFHITAPSKFTFEEAEAECTSRDARLATVGELQAAWRNGFDQCDYGWLS 300
301 DASVRHPVTVARAQCGGGLLGVRTLYRFENQTCFPLPDSRFDAYCFKPKQ 350
351 NISEATTIEMNILAETSSPSLSKEPHMVPDRATPVIPLATELPIFTTHFP 400
401 PAGNIVNSEQKSVVYSQAITGRLATESPTTTRNTINSWDLNDSLASGSGP 450
451 LGMPDISEIKEEELRSTTVISQHATGSQAVITEDTQTHESVSQIEQIEVG 500
501 PLVTSMEITNHISLKELPEKNKTPYESTEVTLEHTTEMPTVSASPELATT 550
551 SHYGFTLREDDREDRTLTVRSDQSTRVFSQIPEVITVSKTSEDTTYSQLG 600
601 DLESISTSTITMLGTDRSLIDKEKEPKTNGKVTEDEFGQSQPTTTFPSQH 650
651 LTEVELLPYSGDTTSVEGISTVIYPSLQTDVTQGRERTETPRPELKKDPY 700
701 TVDEIPEKVTKDPFIGKTEEVFSGMPLSTSSSESSVERTESVSPALTIEK 750
751 LTGKPTEARDVEEMTTLTRLETDVTKSDKDVTRVHLTHSTLNVEVVTVSK 800
801 WPGDEDNSTSKPLPSTEHAGFTKLPPVPLSTIGINGKDKEIPSFTDGGGE 850
851 YTLFPDGTPKPLEKVSEEDLASGELTVTFHTSTSIGSAEKSASGEPTTGD 900
901 RFLPTTSTEDQVINATAEGSALGEDTEASKPLFTGPPFVHTSDVEELAFV 950
951 NYSSTQEPTTYVDISHTSPLSIIPKTEWSVLETSVPLEDEILGKSDQDIL 1000
1001 EQTHLEATMSPGALRTTGVSQGETQEEPQTPGSPFPTFSSTAVMAKETTA 1050
1051 FEEGEGSTYTPSEGRLMTGSERVPGLETTPVGTSYPPGAITDQEVEMDTM 1100
1101 VTLMSTIRPTVVSSTESEVIYEAEGSSPTEFASTLRPFQTHVTQLMEETT 1150
1151 EEGKKASLDYTDLGSGLFEPRATELPKFPSTPSDISVFTAIDSLHRTPPL 1200
1201 SPSSSFTEEQRVFEEESSEKTTGDILPGESVTQHPVTTLIDIVAMKTESD 1250
1251 IDHMTSKPPVTQPTRPSVVERKTTSKTQELSTSTPAAGTKFHPDINVYII 1300
1301 EVRENKTGRLSDMIVSGHPIDSESKEEEPCSEETDPLHDLFAEILPELPD 1350
1351 SFEIDIYHSEEDEDGEEDCVNATDVTTTPSVQYINGKQLVTTVPKDPEAA 1400
1401 EARRGQYESVAPSQNFPDSSATDTHQFILAETESSTTMQFKKSKEGTELL 1450
1451 EITWKPETYPETPDHVSSGEPDVFPTLSSHDGKTTRWSESITESSPNLEN 1500
1501 PVHKQPKPVPLFPEESSGEGAIEQASQETILSRATEVALGKETDQSPTLS 1550
1551 TSSILSSSVSVNVLEEEPLTLTGISQTDESMSTIESWVEITPSQTVKFSE 1600
1601 SSSAPIIEGSGEVEENKNKIFNMVTDLPQRDPTDTLSPLDMSKIMITNHH 1650
1651 IYIPATIAPLDSKLPSPDARPTTVWNSNSTSEWVSDKSFEGRKKKENEDE 1700
1701 EGAVNAAHQGEVRAATERSDHLLLTPELESSNVDASSDLATWEGFILETT 1750
1751 PTESEKEMANSTPVFRETIGVANVEAQPFEHSSSSHPRVQEELTTLSGNP 1800
1801 PSLFTDLGSGDASTGMELITASLFTLDLESETKVKKELPSTPSPSVEISS 1850
1851 SFEPTGLTPSTVLDIEIAGVMSQTSQKTLISEISGKPTSQSGVRDLYTGF 1900
1901 PMGEDFSGDFSEYPTVSYPTMKEETVGMGGSDDERVRDTQTSSSIPTTSD 1950
1951 NIYPVPDSKGPDSTVASTTAFPWEEVMSSAEGSGEQLASVRSSVGPVLPL 2000
2001 AVDIFSGTESPYFDEEFEEVAAVTEANERPTVLPTAASGNTVDLTENGYI 2050
2051 EVNSTMSLDFPQTMEPSKLWSKPEVNLDKQEIGRETVTKEKAQGQKTFES 2100
2101 LHSSFAPEQTILETQSLIETEFQTSDYSMLTTLKTYITNKEVEEEGMSIA 2150
2151 HMSTPGPGIKDLESYTTHPEAPGKSHSFSATALVTESGAARSVLMDSSTQ 2200
2201 EEESIKLFQKGVKLTNKESNADLSFSGLGSGGALPPLPTTSVNLTDMKQI 2250
2251 ISTLYAETSHMESLGTSILGDKMEDHERMEDVSSNEVRMLISKIGSISQD 2300
2301 STEALDTTLSHTGTEEPTTSTLPFVKLMDLERSPKQDPSGGKRKPKTHRP 2350
2351 QTMSGLISNENSSASEAEEGATSPTAFLPQTYSVEMTKHFAPSESQPSDL 2400
2401 FNVNSGEGSGEVDTLDLVYTSGTTQASSQGDSMLASHGFLEKHPEVSKTE 2450
2451 AGATDVSPTASAMFLHHSEYKSSLYPTSTLPSTEPYKSPSEGIEDGLQDN 2500
2501 IQFEGSTLKPSRRKTTESIIIDLDKEDSKDLGLTITESAIVKSLPELTSD 2550
2551 KNIIIDIDHTKPVYEYIPGIQTDLDPEIKLESHGSSEESLQVQEKYEGAV 2600
2601 TLSPTEESFEGSGDALLAGYTQAIYNESVTPNDGKQAEDISFSFATGIPV 2650
2651 SSTETELHTFFPTASTLHIPSKLTTASPEIDKPNIEAISLDDIFESSTLS 2700
2701 DGQAIADQSEVISTLGHLEKTQEEYEEKKYGGPSFQPEFFSGVGEVLTDP 2750
2751 PAYVSIGSTYLIAQTLTELPNVVRPSDSTHYTEATPEVSSLAELSPQIPS 2800
2801 SPFPVYVDNGVSKFPEVPHTSAQPVSTVTSSQKSIESPFKEVHANIEETI 2850
2851 KPLGGNVHRTEPPSMSRDPALDVSEDESKHKLLEELETSPTKPETSQDFP 2900
2901 NKAKDHIPGETVGMLAGIRTTESEPVITADDMELGGATQQPHSASAAFRV 2950
2951 ETGMVPQPIQQEPERPTFPSLEINHETHTSLFGESILATSEKQVSQKILD 3000
3001 NSNQATVSSTLDLHTAHALSPFSILDNSNETAFLIGISEESVEGTAVYLP 3050
3051 GPDLCKTNPCLNGGTCYPTETSYVCTCAPGYSGDQCELDFDECHSNPCRN 3100
3101 GATCVDGFNTFRCLCLPSYVGALCEQDTETCDYGWHKFQGQCYKYFAHRR 3150
3151 TWDAAERECRLQGAHLTSILSHEEQMFVNRVGHDYQWIGLNDKMFEHDFR 3200
3201 WTDGSALQYENWRPNQPDSFFSAGEDCVVIIWHENGQWNDVPCNYHLTYT 3250
3251 CKKGTVACGQPPVVENAKTFGKMKPRYEINSLIRYHCKDGFIQRHLPTIR 3300
3301 CLGNGRWAMPKITCMNPSAYQRTYSKKYLKNSSSAKDNSINTSKHEHRWS 3350
3351 RRQETRR 3357
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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