 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q07878 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MLESLAANLLNRLLGSYVENFDPNQLNVGIWSGDVKLKNLKLRKDCLDSL 50
51 NLPIDVKSGILGDLVLTVPWSSLKNKPVKIIIEDCYLLCSPRSEDHENDE 100
101 EMIKRAFRLKMRKVSEWELTNQARILSTQSENKTSSSSSEKNNAGFMQSL 150
151 TTKIIDNLQVTIKNIHLRYEDMDGIFTTGPSSVGLTLNELSAVSTDSNWA 200
201 PSFIDITQNITHKLLTLNSLCLYWNTDSPPLISDDDQDRSLENFVRGFKD 250
251 MIASKNSTAPKHQYILKPVSGLGKLSINKLGSTEEQPHIDLQMFYDEFGL 300
301 ELDDTEYNDILHVLSSIQLRQITKKFKKARPSFAVSENPTEWFKYIAACV 350
351 INEIHEKNKMWTWESMKEKCEQRRLYTKLWVEKLKLKNLEAPLRDPIQEA 400
401 QLSELHKDLTYDEIILFRSVAKRQYAQYKLGMTEDSPTPTASSNIEPQTS 450
451 NKSATKNNGSWLSSWWNGKPTEEVDEDLIMTEEQRQELYDAIEFDENEDK 500
501 GPVLQVPRERVELRVTSLLKKGSFTIRKKKQNLNLGSIIFENCKVDFAQR 550
551 PDSFLSSFQLNKFSLEDGSPNALYKHIISVRNSSKDQSSIDNHATGEEEE 600
601 EDEPLLRASFELNPLDGLADSNLNIKLLGMTVFYHVHFITEVHKFFKASN 650
651 QHMETIGNIVNAAEATVEGWTTQTRMGIESLLEDHKTVNVSLDLQAPLII 700
701 LPLDPHDWDTPCAIIDAGHMSILSDLVPKEKIKEIKELSPEEYDKIDGNE 750
751 INRLMFDRFQILSQDTQIFVGPDIQSTIGKINTASSTNDFRILDKMKLEL 800
801 TVDLSILPKAYKLPTIRVFGHLPRLSLSINDIQYKTIMNLIANSIPSMID 850
851 DEENNGDYVNYSSGSEKEMKKQIQLQLKNTLKALENMQPLQIEQKFLELH 900
901 FDIDQAKIAFFQCIKNDSRNSEKLVDILCQRLNFNFDKRAKEMNLDLRVH 950
951 SLDVEDYIELTDNKEFKNLISSGVEKVTRSQKDLFTLKYKRVQRIVPHND 1000
1001 TLIELFDQDIVMHMSELQLVLTPRSVLTLMNYAMLTFTDPNAPEMPADVL 1050
1051 RHNKEDRDDAPQKINMKIKMEAVNVIFNDDSIKLATLVLSAGEFTMVLLP 1100
1101 ERYNINLKLGGLELTDETNESFSRDSVFRKIIQMKGQELVELSYESFDPA 1150
1151 TNTKDYDSFLKYSTGSMHVNFIESAVNRMVNFFAKFQKSKVSFDRARLAA 1200
1201 YNQAPSIDAVNNMKMDIVIKAPIIQFPKLVGTQENNYDTMRFYLGEFFIE 1250
1251 NKFSVIDESHKINHIKLGVREGQLSSNLNFDGSSQQLYLVENIGLLFNID 1300
1301 RDPLPQDDTPELKVTSNFESFALDLTENQLTYLLEISNKVSSAFNITDEN 1350
1351 SGESGGKGEIKSPSPDPASLSSESERTATPQSLQGSNKSNIKNPEQKYLD 1400
1401 FSFKAPKIALTLYNKTKGVTSLNDCGLTRIMFQDIGCSLGLKNDGTVDGQ 1450
1451 AHVAAFRIEDVRNIKDNKHTELIPKSKNKEYQFVANISRKNLEVGRLLNI 1500
1501 SMTMDSPKMILAMDYLVSLKEFFDAIMSKSHENNLYYPENTNQKPENKAI 1550
1551 VESVQEGGDVTKIQYSVNIIETALILLADPCDMNSEAISFKIGQFLVTDQ 1600
1601 NIMTVAANNVGIFLFKMNSSEEKLRLLDDFSSSLTIDKRNSTPQTLMTNI 1650
1651 QLSVQPLLMRISLRDIRLAMLIFKRVTTLLNKMTEKEDNGEEEESTDKIQ 1700
1701 FSHEFERKLAVLDPSILGERSRASQSSDSESIEVPTAILKNETFNADLGG 1750
1751 LRFILIGDVHEMPILDMNVNEITASAKDWSTDFEALASLETYVNIFNYSR 1800
1801 SSWEPLLEMIPITFHLSKGHSEMDPAFSFDILTQRIAEITLSARSIAMLS 1850
1851 HIPASLTEELPLASRVSQKPYQLVNDTELDFDVWIQDKTTEDNKNEVVLL 1900
1901 KANTSLPWEFEDWRSIREKLDIDKSKNILGVCVSGQNYKTIMNIDATTEG 1950
1951 ENLHVLSPPRNNVHNRIVCEARCDENNVKIITFRSTLVIENTTSTEIELL 2000
2001 VDSKDPNKPSLKYAIKPHQSKSVPVEYAYDSDIRIRPASEDIYDWSQQTL 2050
2051 SWKSLLSNQMSIFCSSKEDSNQRFHFEIGAKYDEREPLAKIFPHMKIVVS 2100
2101 ASMTIENLLPADINFSIFDKREEKRTDFLKTGESMEVHHISLDSFLLMSV 2150
2151 QPLQDEASASKPSIVNTPHKSPLNPEDSLSLTLSGGQNLLLKLDYKNIDG 2200
2201 TRSKVIRIYSPYIIMNSTDRELYIQSSLLNIAQSKILLENEKRYTIPKMF 2250
2251 SFDKEDDKSNRARIRFKESEWSSKLSFDAIGQSFDASVRIKNKEQESNLG 2300
2301 INISEGKGKYLLSKVIEIAPRYIISNTLDIPIEVCETGSMDVQQIESNIT 2350
2351 KPLYRMRNIVDKQLVLKFLGGDSNWSQPFFIKNVGVTYLKVLKNSRHKLL 2400
2401 KIEILLDKATIFIRIKDGGDRWPFSIRNFSDHDFIFYQRDPRKVSDPYKD 2450
2451 DQSNESSSRSFKPIFYRIPSKSIMPYAWDFPTAKEKYLVLESGTRTREVR 2500
2501 LAEIGELPPLRLDKRSKDKPAPIVGLHVVADDDMQALVIVNYKANVGLYK 2550
2551 LKTASATTTSSVSVNSSVTDGFVQKDEDEKVNTQIVVSFKGVGISLINGR 2600
2601 LQELLYINMRGIELRYNESKAYQTFSWKMKWMQIDNQLFSGNYSNILYPT 2650
2651 EIPYTEKEIENHPVISGSISKVNDSLQAVPYFKHVTLLIQEFSIQLDEDM 2700
2701 LYAMMDFIKFPGSPWIMDSRDYKYDEEIQLPDVSELKTAGDIYFEIFHIQ 2750
2751 PTVLHLSFIRSDEISPGLAEETEESFSSSLYYVHMFAMTLGNINEAPVKV 2800
2801 NSLFMDNVRVPLPILMDHIERHYTTQFVYQIHKILGSADCFGNPVGLFNT 2850
2851 ISSGVWDLFYEPYQGYMMNDRPQEIGIHLAKGGLSFAKKTVFGLSDSMSK 2900
2901 FTGSMAKGLSVTQDLEFQRVRRLQQRINKNNRNALANSAQSFASTLGSGL 2950
2951 SGIALDPYKAMQKEGAAGFLKGLGKGIVGLPTKTAIGFLDLTSNLSQGVK 3000
3001 STTTVLDMQKGCRVRLPRYVDHDQIIKPYDLREAQGQYWLKTVNGGVFMN 3050
3051 DEYLSHVILPGKELAVIVSMQHIAEVQMATQELMWSTGYPSIQGITLERS 3100
3101 GLQIKLKSQSEYFIPISDPEERRSLYRNIAIAVREYNKYCEAIL 3144
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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