 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q90953 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MLLNIKSIIWMCSTLAITYMLPKVKAEKKTLVKGSLSGTSVLPCFFSTTP 50
51 TIASSYAAEYLRIKWSKVELDKSGKDAKETTVLVAQNGNIKIGQNYKDRV 100
101 SVPTHSEETGDASLTFSRLRASDAGVYRCDVMYGVEDTQGIVSLAVDGVV 150
151 FHYRAATSRYTLNFTQAQQTCLDNGAVIASPEQLKAAYEDGFEQCDAGWL 200
201 SDQTVRYPIRHPRIGCFGDKMGKKGVRTYGRRFPNETYDVYCYVEHMQDE 250
251 VVHVSVPEKLTFEEAKELCRKRDGVLASVGNMYVAWRNGFDQCDYGWLAD 300
301 GSVRYPASVARPQCGGGLLGVRTLYRYENQTGFPYPDSKFDAYCYERKKI 350
351 VSEPTTVKLVTTLKTDSVELSSAKVTLKPSVFESSVTEVAVTKTKVPAWE 400
401 EATLETEDTKMTTEVAEEKREMEVLMENIKLTTLLPQTVTDGEISPYDTL 450
451 GRTEYDVSPRLTESTSAALEVEHTYSEAELSEEQGRSESTEDAFLTSVVF 500
501 QDSTAVAKSSTGSWEDIETGDTQKHDGDNQTEQIEVGPVMTATDSLVPAS 550
551 QRELPRTGSSVSLTKENLYLGSHSTKEPTKKSMEAKSDKKLTTVVIPKAL 600
601 FTDQYDLTTGGEGRESMYTVMPDRVSGVALVSIPESDVPAVSETLMDELA 650
651 VTTGQSSTADESTPFIKFSSSATELDNEASAEGSREDLKDVHLTTSSGIP 700
701 VSFTLFTANETGSEVTALSESTSAPQKFEEGITSVLHSSQQTEGSAILEK 750
751 QEKTKEPEMSTIDAKVLYITTVVPASVTAGSEGRFGSEKFTHTPPVSGMW 800
801 LQTDKDQVYMTEETSHTKRIELDTEDDISGMEPTSSPGQIIEYTKHLGAP 850
851 VSAVTDETKTSMETAETESDEEVVSADFDQTKGTTEVFHTSSSLDLEKFT 900
901 LSKIPEDESSATVKSFSSSSGTVLPTAVATVLEVTDHEADETSGYVLNMT 950
951 FSTPEGEQRKATEKSPATSAEDEVSTGTEISKYTMTEGGQISSVTSAEKE 1000
1001 SVAALQEREEQPSVGLPETKEPFKFTDVTEIETTVPQREGDTSLVPVTVG 1050
1051 SEDIGEMQVTDHTSFDSIIHTEATVTSTKASEVFPKELSTKDQDRELGTA 1100
1101 MGSTLPVTSVQMHEQKTTAGFESPQTTTQEKHDEMGSAYDEMYPATELSV 1150
1151 PALMLTEYGQVSGPVETSTRSLHLTGTPKAETATDQEEKITEAVPVTFGT 1200
1201 QAKVYESKGTTTREEDRDVGSWNSVLPPHTMLSSPSTAGSISLLTLGASP 1250
1251 SQTPEGSGISEELEEVKTVPFSSRATDKTTVISDLTTSSISAVDKIQPTS 1300
1301 ASKPFVSSKSPRIIPEEDEEVTSSDIIVIDESISPSKASAEDDLTGKMVE 1350
1351 PEIDKEYFTSSTATAVARPTAPPTVMEATEALQPQEVSPTSHPDSGTDIR 1400
1401 LYVIQITGNDTDHPVNEFLDLFSRHILPHAVDETHTDAESAQTEPCTSDS 1450
1451 VQDSSEYIILDPFFPNFMDFEEEEEDCENTTDVTTPPALQFINGKQQVTS 1500
1501 APKSTKAEEARSDQIESVAHSKNVTFSQINETNTFIISETEASGTMQPSK 1550
1551 AGEVMGAFEVTQPTADVAMLEPVYSGESEVTTTDKYLEITSVYEQSPKKN 1600
1601 KETVMWHGTEESSTKDTKNLLLITNESSGDGSTESDLSRSVFTEILTMSS 1650
1651 HEDSEKISHTTSVPTILSVERSAVTAAPSADSDTATVGIDVKDLIPKGGT 1700
1701 ATPGNYYKSTIKLDAEFPFESNPEATSHTTKPDMTASSFIVLEGSGDVEE 1750
1751 NSTLASAMTTETAVAETLSVQDTSLGSGTVLPTEISVTISEITPALPGGT 1800
1801 RILYSTFDQSSEATVSTNFVSELIMEQVVGSSVATEKKVEDEKEVQTTVY 1850
1851 SSQEISTTDAKGKSELDEFGSTTNEVRTVSQEPTPLREIVPITGTMHSEI 1900
1901 KKVTATPFLREKLFINEGSAEEPADLFAGSPTRKVVSTDSPFTDSGSGDI 1950
1951 DVITESATLTSVPSRSVIETQTVKHEGNINVISVSLKNTTTEYEEHIGTG 2000
2001 GPVTSVSSTGSDGLTEESEVAIEMSENVFSTENQGEPTQEAVPTYTAPSD 2050
2051 IKSRLGSRREVTSHVTPVIRTKDLETAEVTSSPESVVNNSTLDTMVTHGT 2100
2101 IRAVAESTESKKGKGSFSAVSLGKILMIEHGSGEELKVDSSTTKLMSNGP 2150
2151 TEKLLGSHFSFFDQGSGEAETLTESFTKASVSPTGKPEPQEQYGRKTVSM 2200
2201 PSAVVHAYTAEPNELVTSTEHDITSLQTVTDTEMEEKAANELTVTSFATN 2250
2251 LPLSEDVHSWEDRPREILPKAIESSGEATEDPFFISTQANHEHVEFLSVP 2300
2301 TIRPHSEENKVEAESDEKILLPFNNDRVTESAVIERKYLSSPFTDTEQEE 2350
2351 ELVQNIFPTEDIPRLFLTPKEEKPTNNELISDPLFSGQGSGDEFTVIPSV 2400
2401 ESLAVKETTNTLSPWPFHPASVGPKLSTDKTQVFESGSTDSNAEINEEIT 2450
2451 TTAAELTETAYSMATSSPALEEESSSHSNSKDKDITHYFLVIEDPYNKEM 2500
2501 DHRRGENGTSRPLPTPGDVSLEESSHMLTTDDVTPVSVILSETPYLEMGK 2550
2551 SLATSATKMPSRVLPESSGEGSGWDGVSDSFAPDTLTHSTAPSVMEVELT 2600
2601 ASSHIPGVYSEVMTTHVPGDGSQTVITGLASLFTEEKEIVANRTAADPKT 2650
2651 GTSEELTSDTGMSLDIIPVVDDRRHVTLNVSVYGDITLIEERLQIPSEKT 2700
2701 TIIDMDHSKSMPEDIISVQTMPNLVIRSTQVSDDNMKAEEDKYDSILNFS 2750
2751 TVEENSFGSGDNLSLTTSIQPSSESVTAGHGPKLVDKDLGSGYAMQFATE 2800
2801 TLTTTVLNELGIFLPTVPSLVSPHMPHESKESEFEAKHIGRTSTTDDVYE 2850
2851 PYTSANNQVITDQSKTMSISGFSGMGQEESGDKKPMIPSLTPDLTMETEK 2900
2901 ALTTDTFDVSMVTTQSMSQHATVSSSSSEEKHSTVYMQTKSASTEYEETD 2950
2951 SVSLNSVSQNPKSSVTVWLVNGVSKYPEVIIPSTSSAKDSDQSDHSSDGT 3000
3001 FKEVSSDMAATYKPPTTDLDTTVSSLLVFSPEPESESISTESTPHFNKFV 3050
3051 TERSEETESSVNDLIIEENATVSGDSPSIHDYPTAFWNFGERTSTDVPKL 3100
3101 STIEVEFSSERVKNPSQESDRSTERERPRLSSAPVSDSPNSIEVGVFKPD 3150
3151 QEAVTMLTSSLEPLDRSLETQSALLGPLLGQQEITTISSNIATNNTAPGN 3200
3201 NPYSNEQSTISSELLNTIELVTSSFSLPEVTNGSDFLIGTSVGSVEGTAV 3250
3251 QIPGQDPCKSNPCLNGGTCYPRGSFYICTCLPGFNGEQCELDIDECQSNP 3300
3301 CRNGATCIDGLNTFTCLCLPSYIGALCEQDTETCDYGWHKFQGQCYKYFA 3350
3351 HRRTWDTAERECRLQGAHLTSILSHEEQVFVNRIGHDYQWIGLNDKMFER 3400
3401 DFRWTDGSPLQYENWRPNQPDSFFSAGEDCVVIIWHENGQWNDVPCNYHL 3450
3451 TYTCKKGTVACGQPPVVENAKTFGKMKPRYEINSLIRYHCKDGFIQRHIP 3500
3501 TIRCQGNGRWDMPKITCMNPSTYQRTYSKKYYYKHSSSGKGTSLNSSKHY 3550
3551 HRWIRTWQDSRR 3562
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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