 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q12019 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MSQDRILLDLDVVNQRLILFNSAFPSDAIEAPFHFSNKESTSENLDNLAG 50
51 TILHSRSITGHVFLYKHIFLEIVARWIKDSKKKDYVLVIEKLASIITIFP 100
101 VAMPLIEDYLDKENDHFITILQNPSTQKDSDMFKILLAYYRLLYHNKEVF 150
151 ARFIQPDILYQLVDLLTKEQENQVVIFLALKVLSLYLDMGEKTLNDMLDT 200
201 YIKSRDSLLGHFEGDSGIDYSFLELNEAKRCANFSKLPSVPECFTIEKKS 250
251 SYFIIEPQDLSTKVASICGVIVPKVHTIHDKVFYPLTFVPTHKTVSSLRQ 300
301 LGRKIQNSTPIMLIGKAGSGKTFLINELSKYMGCHDSIVKIHLGEQTDAK 350
351 LLIGTYTSGDKPGTFEWRAGVLATAVKEGRWVLIEDIDKAPTDVLSILLS 400
401 LLEKRELTIPSRGETVKAANGFQLISTVRINEDHQKDSSNKIYNLNMIGM 450
451 RIWNVIELEEPSEEDLTHILAQKFPILTNLIPKLIDSYKNVKSIYMNTKF 500
501 ISLNKGAHTRVVSVRDLIKLCERLDILFKNNGINKPDQLIQSSVYDSIFS 550
551 EAADCFAGAIGEFKALEPIIQAIGESLDIASSRISLFLTQHVPTLENLDD 600
601 SIKIGRAVLLKEKLNIQKKSMNSTLFAFTNHSLRLMEQISVCIQMTEPVL 650
651 LVGETGTGKTTVVQQLAKMLAKKLTVINVSQQTETGDLLGGYKPVNSKTV 700
701 AVPIQENFETLFNATFSLKKNEKFHKMLHRCFNKNQWKNVVKLWNEAYKM 750
751 AQSILKITNTENENENAKKKKRRLNTHEKKLLLDKWADFNDSVKKFEAQS 800
801 SSIENSFVFNFVEGSLVKTIRAGEWLLLDEVNLATADTLESISDLLTEPD 850
851 SRSILLSEKGDAEPIKAHPDFRIFACMNPATDVGKRDLPMGIRSRFTEIY 900
901 VHSPERDITDLLSIIDKYIGKYSVSDEWVGNDIAELYLEAKKLSDNNTIV 950
951 DGSNQKPHFSIRTLTRTLLYVTDIIHIYGLRRSLYDGFCMSFLTLLDQKS 1000
1001 EAILKPVIEKFTLGRLKNVKSIMSQTPPSPGPDYVQFKHYWMKKGPNTIQ 1050
1051 EQAHYIITPFVEKNMMNLVRATSGKRFPVLIQGPTSSGKTSMIKYLADIT 1100
1101 GHKFVRINNHEHTDLQEYLGTYVTDDTGKLSFKEGVLVEALRKGYWIVLD 1150
1151 ELNLAPTDVLEALNRLLDDNRELFIPETQEVVHPHPDFLLFATQNPPGIY 1200
1201 GGRKILSRAFRNRFLELHFDDIPQDELEIILRERCQIAPSYAKKIVEVYR 1250
1251 QLSIERSASRLFEQKNSFATLRDLFRWALRDAVGYEQLAASGYMLLAERC 1300
1301 RTPQEKVTVKKTLEKVMKVKLDMDQYYASLEDKSLEAIGSVTWTKGMRRL 1350
1351 SVLVSSCLKNKEPVLLVGETGCGKTTICQLLAQFMGRELITLNAHQNTET 1400
1401 GDILGAQRPVRNRSEIQYKLIKSLKTALNIANDQDVDLKELLQLYSKSDN 1450
1451 KNIAEDVQLEIQKLRDSLNVLFEWSDGPLIQAMRTGNFFLLDEISLADDS 1500
1501 VLERLNSVLEPERSLLLAEQGSSDSLVTASENFQFFATMNPGGDYGKKEL 1550
1551 SPALRNRFTEIWVPSMEDFNDVNMIVSSRLLEDLKDLANPIVKFSEWFGK 1600
1601 KLGGGNATSGVISLRDILAWVEFINKVFPKIQNKSTALIQGASMVFIDAL 1650
1651 GTNNTAYLAENENDLKSLRTECIIQLLKLCGDDLELQQIETNEIIVTQDE 1700
1701 LQVGMFKIPRFPDAQSSSFNLTAPTTASNLVRVVRAMQVHKPILLEGSPG 1750
1751 VGKTSLITALANITGNKLTRINLSEQTDLVDLFGADAPGERSGEFLWHDA 1800
1801 PFLRAMKKGEWVLLDEMNLASQSVLEGLNACLDHRGEAYIPELDISFSCH 1850
1851 PNFLVFAAQNPQYQGGGRKGLPKSFVNRFSVVFIDMLTSDDLLLIAKHLY 1900
1901 PSIEPDIIAKMIKLMSTLEDQVCKRKLWGNSGSPWEFNLRDTLRWLKLLN 1950
1951 QYSICEDVDVFDFVDIIVKQRFRTISDKNKAQLLIEDIFGKFSTKENFFK 2000
2001 LTEDYVQINNEVALRNPHYRYPITQNLFPLECNVAVYESVLKAINNNWPL 2050
2051 VLVGPSNSGKTETIRFLASILGPRVDVFSMNSDIDSMDILGGYEQVDLTR 2100
2101 QISYITEELTNIVREIISMNMKLSPNATAIMEGLNLLKYLLNNIVTPEKF 2150
2151 QDFRNRFNRFFSHLEGHPLLKTMSMNIEKMTEIITKEASVKFEWFDGMLV 2200
2201 KAVEKGHWLILDNANLCSPSVLDRLNSLLEIDGSLLINECSQEDGQPRVL 2250
2251 KPHPNFRLFLTMDPKYGELSRAMRNRGVEIYIDELHSRSTAFDRLTLGFE 2300
2301 LGENIDFVSIDDGIKKIKLNEPDMSIPLKHYVPSYLSRPCIFAQVHDILL 2350
2351 LSDEEPIEESLAAVIPISHLGEVGKWANNVLNCTEYSEKKIAERLYVFIT 2400
2401 FLTDMGVLEKINNLYKPANLKFQKALGLHDKQLTEETVSLTLNEYVLPTV 2450
2451 SKYSDKIKSPESLYLLSSLRLLLNSLNALKLINEKSTHGKIDELTYIELS 2500
2501 AAAFNGRHLKNIPRIPIFCILYNILTVMSENLKTESLFCGSNQYQYYWDL 2550
2551 LVIVIAALETAVTKDEARLRVYKELIDSWIASVKSKSDIEITPFLNINLE 2600
2601 FTDVLQLSRGHSITLLWDIFRKNYPTTSNSWLAFEKLINLSEKFDKVRLL 2650
2651 QFSESYNSIKDLMDVFRLLNDDVLNNKLSEFNLLLSKLEDGINELELISN 2700
2701 KFLNKRKHYFADEFDNLIRYTFSVDTAELIKELAPASSLATQKLTKLITN 2750
2751 KYNYPPIFDVLWTEKNAKLTSFTSTIFSSQFLEDVVRKSNNLKSFSGNQI 2800
2801 KQSISDAELLLSSTIKCSPNLLKSQMEYYKNMLLSWLRKVIDIHVGGDCL 2850
2851 KLTLKELCSLIEEKTASETRVTFAEYIFPALDLAESSKSLEELGEAWITF 2900
2901 GTGLLLLFVPDSPYDPAIHDYVLYDLFLKTKTFSQNLMKSWRNVRKVISG 2950
2951 DEEIFTEKLINTISDDDAPQSPRVYRTGMSIDSLFDEWMAFLSSTMSSRQ 3000
3001 IKELVSSYKCNSDQSDRRLEMLQQNSAHFLNRLESGYSKFADLNDILAGY 3050
3051 IYSINFGFDLLKLQKSKDRASFQISPLWSMDPINISCAENVLSAYHELSR 3100
3101 FFKKGDMEDTSIEKVLMYFLTLFKFHKRDTNLLEIFEAALYTLYSRWSVR 3150
3151 RFRQEQEENEKSNMFKFNDNSDDYEADFRKLFPDYEDTALVTNEKDISSP 3200
3201 ENLDDIYFKLADTYISVFDKDHDANFSSELKSGAIITTILSEDLKNTRIE 3250
3251 ELKSGSLSAVINTLDAETQSFKNTEVFGNIDFYHDFSIPEFQKAGDIIET 3300
3301 VLKSVLKLLKQWPEHATLKELYRVSQEFLNYPIKTPLARQLQKIEQIYTY 3350
3351 LAEWEKYASSEVSLNNTVKLITDLIVSWRKLELRTWKGLFNSEDAKTRKS 3400
3401 IGKWWFYLYESIVISNFVSEKKETAPNATLLVSSLNLFFSKSTLGEFNAR 3450
3451 LDLVKAFYKHIQLIGLRSSKIAGLLHNTIKFYYQFKPLIDERITNGKKSL 3500
3501 EKEIDDIILLASWKDVNVDALKQSSRKSHNNLYKIVRKYRDLLNGDAKTI 3550
3551 IEAGLLYSNENKLKLPTLKQHFYEDPNLEASKNLVKEISTWSMRAAPLRN 3600
3601 IDTVASNMDSYLEKISSQEFPNFADLASDFYAEAERLRKETPNVYTKENK 3650
3651 KRLAYLKTQKSKLLGDALKELRRIGLKVNFREDIQKVQSSTTTILANIAP 3700
3701 FNNEYLNSSDAFFFKILDLLPKLRSAASNPSDDIPVAAIERGMALAQSLM 3750
3751 FSLITVRHPLSEFTNDYCKINGMMLDLEHFTCLKGDIVHSSLKANVDNVR 3800
3801 LFEKWLPSLLDYAAQTLSVISKYSATSEQQKILLDAKSTLSSFFVHFNSS 3850
3851 RIFDSSFIESYSRFELFINELLKKLENAKETGNAFVFDIIIEWIKANKGG 3900
3901 PIKKEQKRGPSVEDVEQAFRRTFTSIILSFQKVIGDGIESISETDDNWLS 3950
3951 ASFKKVMVNVKLLRSSVVSKNIETALSLLKDFDFTTTESIYVKSVISFTL 4000
4001 PVITRYYNAMTVVLERSRIYYTNTSRGMYILSTILHSLAKNGFCSPQPPS 4050
4051 EEVDDKNLQEGTGLGDGEGAQNNNKDVEQDEDLTEDAQNENKEQQDKDER 4100
4101 DDENEDDAVEMEGDMAGELEDLSNGEENDDEDTDSEEEELDEEIDDLNED 4150
4151 DPNAIDDKMWDDKASDNSKEKDTDQNLDGKNQEEDVQAAENDEQQRDNKE 4200
4201 GGDEDPNAPEDGDEEIENDENAEEENDVGEQEDEVKDEEGEDLEANVPEI 4250
4251 ETLDLPEDMNLDSEHEESDEDVDMSDGMPDDLNKEEVGNEDEEVKQESGI 4300
4301 ESDNENDEPGPEEDAGETETALDEEEGAEEDVDMTNDEGKEDEENGPEEQ 4350
4351 AMSDEEELKQDAAMEENKEKGGEQNTEGLDGVEEKADTEDIDQEAAVQQD 4400
4401 SGSKGAGADATDTQEQDDVGGSGTTQNTYEEDQEDVTKNNEESREEATAA 4450
4451 LKQLGDSMKEYHRRRQDIKEAQTNGEEDENLEKNNERPDEFEHVEGANTE 4500
4501 TDTQALGSATQDQLQTIDEDMAIDDDREEQEVDQKELVEDADDEKMDIDE 4550
4551 EEMLSDIDAHDANNDVDSKKSGFIGKRKSEEDFENELSNEHFSADQEDDS 4600
4601 EIQSLIENIEDNPPDASASLTPERSLEESRELWHKSEISTADLVSRLGEQ 4650
4651 LRLILEPTLATKLKGDYKTGKRLNMKRIIPYIASQFRKDKIWLRRTKPSK 4700
4701 RQYQIMIALDDSKSMSESKCVKLAFDSLCLVSKTLTQLEAGGLSIVKFGE 4750
4751 NIKEVHSFDQQFSNESGARAFQWFGFQETKTDVKKLVAESTKIFERARAM 4800
4801 VHNDQWQLEIVISDGICEDHETIQKLVRRARENKIMLVFVIIDGITSNES 4850
4851 ILDMSQVNYIPDQYGNPQLKITKYLDTFPFEFYVVVHDISELPEMLSLIL 4900
4901 RQYFTDLASS 4910
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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