 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9QZZ4 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MADEEKKAKKGKKGKKAPEPEKPKRSLKGTSRLFMGFRDRTPKISKKGQF 50
51 RSASAFFWGLHTGPQKTKRKKKARTVLKSTSKLMTQMRVGKKKRAMKGKK 100
101 PSFMVIRFPGRRGYGRLRPRAQSLSKASTAINWLTKKFLLKKAEESGSEQ 150
151 ATVDAWLQRSSSRVGSRKLPFPSGAEILRHGGRLRRFPRSHSIYSSGEPV 200
201 GFLPFEDEAPFRHAGSRKSLYGLEGFQDLGEYYDYHREGDDYYDQQSLYH 250
251 YEEQEPYLAEFGGYSPAWPPYDDYGYPPGDPYNYYHPDYYGDTLYPGYAY 300
301 GYGYGYDDFEPPYAPPSGYSSPYSYHDSFESEAYPYSYYLDPYATHHMPY 350
351 PPYDFPYDTPYDIPYFDPYGVPYAEGVYGGGAEAIYPPGMPYVYPEEPAF 400
401 MYPWVPPPIMSPHNPYAHPMDDIAELEEPEETGEERQSTSFRLPSAAFFE 450
451 QQGMDKPARSKLSLIRKFRLFPRPQVKLFGKEKLEVPLPPSLDIPLPLGD 500
501 AEGEEEEEEMPPVPTMPYTHPYWSFLTPRQRNLQRALSAFGARQGLGFGP 550
551 EFGHPTPRPATSLARFLKKTLSEKKPIPRLRGSQKARGGRPPVREAAYKR 600
601 FGYKLAGMDPDRPNTPIVLRRSQPQARNNNNSHGPPSPRPAPRALTHWSA 650
651 LISPPMPAPSPSPASPLTPPFSPTFSRPPRLASPYGSLRQHPPPWAAPAH 700
701 VPFPPQANWWGFAEPPGTSPEVAPDLLAFPVPRPSFRASRSRSRRAAYGF 750
751 PSPSLIGSRRRPHLPSPQPSLRSLPGQGYHSPLGPLSPQLSLRRGPFQPP 800
801 FPPPPRRPQSLREAFSLRRASGRLGPPRSPVLGSPRPPSPPPLLKHGPRH 850
851 RSLNLPSRLPRTWRRLSEPPTRAVKPWVHRAYPPPPSAGPWGASTGALEQ 900
901 QENQREAEDSETPWTVPPLAPSWDVDMPPTQRPPSPWPEGIGSLRGFSRP 950
951 PPVPENPLLEHTSPSCEPQSEDRVSNLTGIFLGQHHDPGPGQLTKSADPS 1000
1001 LEKPEEVVTLGDPQPPAEPEALNPTPPNKNVVSERKVLRLSASYPLVTCK 1050
1051 QARATWPQWHRWKTVSRTPAPLAPTRAPGPLLKAGEQPRAEPGRFAVVMP 1100
1101 QVRGVSSFRPKGPAPVQPPEHPDQDPEQGPAPQACSLRWPRLWPPTDAHC 1150
1151 LWSRIRTYSSQSHLRGHGGDCHKSLWKKTRPQSWQNKMHSIRNLPSMRSR 1200
1201 EQHREDGVEDMTQLEDLQETTVLANLKTRFERNLIYTYIGSILVSVNPYR 1250
1251 MFAIYGPEQVQQYSGRALGENPPHLFAIANLAFAKMLDAKQNQCVIISGE 1300
1301 SGSGKTEATKLILRCLAAMNQRRDVMQQIKILEATPLLEAFGNAKTVRND 1350
1351 NSSRFGKFVEIFLEGGVICGAITSQYLLEKSRIVFQAKNERNYHIFYELL 1400
1401 AGLPAQLRQAFSLQEAETYYYLNQGGNCEIAGKSDADDFRRLLAAMEVLG 1450
1451 FTSEDQDSIFRILASILHLGNVYFEKHETDAQEVASVVSAREIQAVAELL 1500
1501 QVSPEGLQKAITFKVTETIREKIFTPLTVESAVDARDAIAKVLYALLFGW 1550
1551 LITRVNALVSPKQDTLSIAILDIYGFEDLSFNSFEQLCINYANENLQYLF 1600
1601 NKIVFQEEQEEYIREQMDWREIAFADNQPCINLISLKPYGILRILDDQCC 1650
1651 FPQATDHTFLQKCHYHHGANPLYSKPKMPLPEFTIKHYAGKVTYQVHKFL 1700
1701 DKNHDQVRQDVLDLFVHSRTRVVAHLFSSHAAQTAPPRLGKSSSITRLYK 1750
1751 AHTVAAKFQQSLLDLVEKMERCNPLFVRCLKPNHKKEPGLFEPDVMMAQL 1800
1801 RYSGVLETVRIRKEGFPVRLPFQVFIDRYRCLVALKLNVPADGDMCVSLL 1850
1851 SRLCTVTPDMYRVGISKLFLKEHLHQLLESMRERVQNRAALTLQRYLRGF 1900
1901 FIQRHFRSLRRKIILLQSRARGFLARQRYQQMRQSLLKFRSLVHTYVNRR 1950
1951 RYLKLRAEQRRRAQEAWLREQEELSKREVVPVRHLEVPAEVAGLLQAAAG 2000
2001 LKLSSGPRVAVVRAPRLQAEPCVTLPLDINNYPMAKFIRCHFKEPSFGML 2050
2051 TVPLKMPLTRLPVEHHAEAISVFKLILRFMGDPHLHGTQEMILGNYIVHQ 2100
2101 GLVEPALRDEILAQLANQVWRNPNAYNSKRGWLLLAACLSGFAPSPHLDK 2150
2151 FLLKFVSDYGQNGFQAVCQHRLLQAMGSGAARTFPPTQLEWTAIQEKASM 2200
2201 ALDVSCFNGDQFSCPVHTWSTGEAVAGDILKHRGLADGWRGWTVAMKNGV 2250
2251 QWAELAGHDYVLDLVSDLELLRDFPRQKSYFIVGAEGPLAGRGDTRGVFG 2300
2301 NCWDSDEDTPTRPQPQDHVAKMPDLDGYCSHKEDGTNGETEAQRWTSNRQ 2350
2351 AVDSIGESTVPPRELDGYLDSLFDPVLACGDADLEKPTAIAYRMKGGGQP 2400
2401 GGGGGSTSEDTSRRPPEPKLKPIPGLDASTLALQQAFIHRQAVLLAREMT 2450
2451 LQALALQQQPLSATSRPQLPERPLAPEARPKTVVGTGPPAKPVLVRPTPQ 2500
2501 SWAPGSVAKAPKIPSKPVAVPILAQDWTAPESISASPELVRYSTLNSEHF 2550
2551 PQPTQQIRSIIKQYKQPPWAGHPEARRTDGGKVFRRPPDPHEEALMILKG 2600
2601 QKTQLAVVPGTQVSREAVAMVKPVTSAPRPCMGPTPVQPSRSLEPPEDPV 2650
2651 QTQLHRLVNPNFYGYQDIPWRIFLRKEVFYPKDNYSHPVQLDLLFRQILH 2700
2701 DTFSEACLRISEDERLQMKALFAQNQLDTQRPLVTESVKRAAISMARDSW 2750
2751 EIYFSRLFPAMGSVGTGVQILAVSHTGIKLLQMVKGSKEASRRLRVLCAY 2800
2801 SFADILFVTMPSQNMLEFNLSNEKLILFSARAQQVKTLVDTFILELKKDS 2850
2851 DYVVAVRNFLSEDPELLSFHKGDIIHLQSLEPTRVGYSAGCVVRKKLVYL 2900
2901 EELRRRGPDFGWRFGAVHGRVGRFPSELVQPAAAPDFLQLPAEPGRGRAA 2950
2951 AVAAAVASAAAAQEVGRRREGPPVRARSADSGEDSIALPPSTMLEFAQKY 3000
3001 FRDPRRRPRDGLKLKSKEDRESKTLEDVLCFTKVPIQESLIELSDSNLNK 3050
3051 MAVDMFVAVMRFMGDAPLKGQSELDVLCTLLKLCGDHEVMRDECYCQIVK 3100
3101 QITDNSSPKQDSCQRGWRLLYIMAAYYSCSEVFYPYLIRFLQHVSWTPGL 3150
3151 PFQGIAKACEQNLQKTLRFGGRLEFPSNMELRAMLAGRSSKRQLFLLPGG 3200
3201 LERHLKIKTCTVALDVIEGLCTEMALTRPEAFDEYVIFVVTNRGQHVCPL 3250
3251 SCRAYILDVASEMEQVDGGYTLWFRRVLWDQPLKFENELYVTMHYNQVLP 3300
3301 DYLKGLFSSVPARQPTEQQLQQVSKLASLQHRAKDHFYLPSVREVQEYIP 3350
3351 AQLYHTTAGDTWLNLVSQHRQQTQALSPHQARAQFLGLLSAFPLFGSSFF 3400
3401 FIQSCSNVLVPAPCILAVNHNGLNFLSTKTHELIVKIPLKEIQSTWTQQP 3450
3451 TANSSYPYVEISLGDVAAQRTMQLQLEQGLELCRVVAVHVESMLSAREER 3500
3501 LTLPPSEITLL 3511
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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