 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10583 from www.uniprot.org...
The NucPred score for your sequence is 0.56 (see score help below)
1 MLSVRLLIVVLALANAENLVRKSVEHLTQEETLDLQAALRELQMDSSSIG 50
51 FQKIAAAHGAPASCVHKDTSIACCIHGMPTFPHWHRAYVVHMERALQTKR 100
101 RTSGLPYWDWTEPITQLPSLAADPVYIDSQGGKAHTNYWYRGNIDFLDKK 150
151 TNRAVDDRLFEKVKPGQHTHLMESVLDALEQDEFCKFEIQFELAHNAIHY 200
201 LVGGKHDYSMANLEYTAYDPIFFLHHSNVDRIFAIWQRLQELRNKDPKAM 250
251 DCAQELLHQKMEPFSWEDNDIPLTNEHSTPADLFDYCELHYDYDTLNLNG 300
301 MTPEELKTYLDERSSRARAFASFRLKGFGGSANVFVYVCIPDDNDRNDDH 350
351 CEKAGDFFVLGGPSEMKWQFYRPYLFDLSDTVHKMGMKLDGHYTVKAELF 400
401 SVNGTALPDDLLPHPVVVHHPEKGFTDPPVKHHQSANLLVRKNINDLTRE 450
451 EVLNLREAFHKFQEDRSVDGYQATAEYHGLPARCPRPDAKDRYACCVHGM 500
501 PIFPHWHRLFVTQVEDALVGRGATIGIPYWDWTEPMTHIPGLAGNKTYVD 550
551 SHGASHTNPFHSSVIAFEENAPHTKRQIDQRLFKPATFGHHTDLFNQILY 600
601 AFEQEDYCDFEVQFEITHNTIHAWTGGSEHFSMSSLHYTAFDPLFYFHHS 650
651 NVDRLWAVWQALQMRRHKPYRAHCAISLEHMHLKPFAFSSPLNNNEKTHA 700
701 NAMPNKIYDYENVLHYTYEDLTFGGISLENIEKMIHENQQEDRIYAGFLL 750
751 AGIRTSANVDIFIKTTDSVQHKAGTFAVLGGSKEMKWGFDRVFKFDITHV 800
801 LKDLDLTADGDFEVTVDITEVDGTKLASSLIPHASVIREHARVKFDKVPR 850
851 SRLIRKNVDRLSPEEMNELRKALALLKEDKSAGGFQQLGAFHGEPKWCPS 900
901 PEASKKFACCVHGMSVFPHWHRLLTVQSENALRRHGYDGALPYWDWTSPL 950
951 NHLPELADHEKYVDPEDGVEKHNPWFDGHIDTVDKTTTRSVQNKLFEQPE 1000
1001 FGHYTSIAKQVLLALEQDNFCDFEIQYEIAHNYIHALVGGAQPYGMASLR 1050
1051 YTAFDPLFYLHHSNTDRIWAIWQALQKYRGKPYNVANCAVTSMREPLQPF 1100
1101 GLSANINTDHVTKEHSVPFNVFDYKTNFNYEYDTLEFNGLSISQLNKKLE 1150
1151 AIKSQDRFFAGFLLSGFKKSSLVKFNICTDSSNCHPAGEFYLLGDENEMP 1200
1201 WAYDRVFKYDITEKLHDLKLHAEDHFYIDYEVFDLKPASLGKDLFKQPSV 1250
1251 IHEPRIGHHEGEVYQAEVTSANRIRKNIENLSLGELESLRAAFLEIENDG 1300
1301 TYESIAKFHGSPGLCQLNGNPISCCVHGMPTFPHWHRLYVVVVENALLKK 1350
1351 GSSVAVPYWDWTKRIEHLPHLISDATYYNSRQHHYETNPFHHGKITHENE 1400
1401 ITTRDPKDSLFHSDYFYEQVLYALEQDNFCDFEIQLEILHNALHSLLGGK 1450
1451 GKYSMSNLDYAAFDPVFFLHHATTDRIWAIWQDLQRFRKRPYREANCAIQ 1500
1501 LMHTPLQPFDKSDNNDEATKTHATPHDGFEYQNSFGYAYDNLELNHYSIP 1550
1551 QLDHMLQERKRHDRVFAGFLLHNIGTSADGHVFVCLPTGEHTKDCSHEAG 1600
1601 MFSILGGQTEMSFVFDRLYKLDITKALKKNGVHLQGDFDLEIEITAVNGS 1650
1651 HLDSHVIHSPTILFEAGTDSAHTDDGHTEPVMIRKDITQLDKRQQLSLVK 1700
1701 ALESMKADHSSDGFQAIASFHALPPLCPSPAASKRFACCVHGMATFPQWH 1750
1751 RLYTVQFQDSLRKHGAVVGLPYWDWTLPRSELPELLTVSTIHDPETGRDI 1800
1801 PNPFIGSKIEFEGENVHTKRDINRDRLFQGSTKTHHNWFIEQALLALEQT 1850
1851 NYCDFEVQFEIMHNGVHTWVGGKEPYGIGHLHYASYDPLFYIHHSQTDRI 1900
1901 WAIWQSLQRFRGLSGSEANCAVNLMKTPLKPFSFGAPYNLNDHTHDFSKP 1950
1951 EDTFDYQKFGYIYDTLEFAGWSIRGIDHIVRNRQEHSRVFAGFLLEGFGT 2000
2001 SATVDFQVCRTAGDCEDAGYFTVLGGEKEMPWAFDRLYKYDITETLDKMN 2050
2051 LRHDEIFQIEVTITSYDGTVLDSGLIPTPSIIYDPAHHDISSHHLSLNKV 2100
2101 RHDLSTLSERDIGSLKYALSSLQADTSADGFAAIASFHGLPAKCNDSHNN 2150
2151 EVACCIHGMPTFPHWHRLYTLQFEQALRRHGSSVAVPYWDWTKPIHNIPH 2200
2201 LFTDKEYYDVWRNKVMPNPFARGYVPSHDTYTVRDVQEGLFHLTSTGEHS 2250
2251 ALLNQALLALEQHDYCDFAVQFEVMHNTIHYLVGGPQVYSLSSLHYASYD 2300
2301 PIFFIHHSFVDKVWAVWQALQEKRGLPSDRADCAVSLMTQNMRPFHYEIN 2350
2351 HNQFTKKHAVPNDVFKYELLGYRYDNLEIGGMNLHEIEKEIKDKQHHVRV 2400
2401 FAGFLLHGIRTSADVQFQICKTSEDCHHGGQIFVLGGTKEMAWAYNRLFK 2450
2451 YDITHALHDAHITPEDVFHPSEPFFIKVSVTAVNGTVLPASILHAPTIIY 2500
2501 EPGLDHHEDHHSSSMAGHGVRKEINTLTTAEVDNLKDAMRAVMADHGPNG 2550
2551 YQAIAAFHGNPPMCPMPDGKNYSCCTHGMATFPHWHRLYTKQMEDALTAH 2600
2601 GARVGLPYWDGTTAFTALPTFVTDEEDNPFHHGHIDYLGVDTTRSPRDKL 2650
2651 FNDPERGSESFFYRQVLLALEQTDFCQFEVQFEITHNAIHSWTGGLTPYG 2700
2701 MSTLEYTTYDPLFWLHHANTDRIWAIWQALQEYRGLPYDHANCEIQAMKR 2750
2751 PLRPFSDPINHNAFTHSNAKPTDVFEYSRFNFQYDNLRFHGMTIKKLEHE 2800
2801 LEKQKEEDRTFAAFLLHGIKKSADVSFDVCNHDGECHFAGTFAILGGEHE 2850
2851 MPWSFDRLFRYDITQVLKQMHLEYDSDFTFHMRIIDTSGKQLPSDLIKMP 2900
2901 TVEHSPGGKHHEKHHEDHHEDILVRKNIHSLSHHEAEELRDALYKLQNDE 2950
2951 SHGGYEHIAGFHGYPNLCPEKGDEKYPCCVHGMSIFPHWHRLHTIQFERA 3000
3001 LKKHGSHLGIPYWDWTQTISSLPTFFADSGNNNPFFKYHIRSINQDTVRD 3050
3051 VNEAIFQQTKFGEFSSIFYLALQALEEDNYCDFEVQYEILHNEVHALIGG 3100
3101 AEKYSMSTLEYSAFDPYFMIHHASLDKIWIIWQELQKRRVKPAHAGSCAG 3150
3151 DIMHVPLHPFNYESVNNDDFTRENSLPNAVVDSHRFNYKYDNLNLHGHNI 3200
3201 EELEEVLRSLRLKSRVFAGFVLSGIRTTAVVKVYIKSGTDSDDEYAGSFV 3250
3251 ILGGAKEMPWAYERLYRFDITETVHNLNLTDDHVKFRFDLKKYDHTELDA 3300
3301 SVLPAPIIVRRPNNAVFDIIEIPIGKDVNLPPKVVVKRGTKIMFMSVDEA 3350
3351 VTTPMLNLGSYTAMFKCKVPPFSFHAFELGKMYSVESGDYFMTASTTELC 3400
3401 NDNNLRIHVHVDDE 3414
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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