 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q99996 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MEDEERQKKLEAGKAKLAQFRQRKAQSDGQSPSKKQKKKRKTSSSKHDVS 50
51 AHHDLNIDQSQCNEMYINSSQRVESTVIPESTIMRTLHSGEITSHEQGFS 100
101 VELESEISTTADDCSSEVNGCSFVMRTGKPTNLLREEEFGVDDSYSEQGA 150
151 QDSPTHLEMMESELAGKQHEIEELNRELEEMRVTYGTEGLQQLQEFEAAI 200
201 KQRDGIITQLTANLQQARREKDETMREFLELTEQSQKLQIQFQQLQASET 250
251 LRNSTHSSTAADLLQAKQQILTHQQQLEEQDHLLEDYQKKKEDFTMQISF 300
301 LQEKIKVYEMEQDKKVENSNKEEIQEKETIIEELNTKIIEEEKKTLELKD 350
351 KLTTADKLLGELQEQIVQKNQEIKNMKLELTNSKQKERQSSEEIKQLMGT 400
401 VEELQKRNHKDSQFETDIVQRMEQETQRKLEQLRAELDEMYGQQIVQMKQ 450
451 ELIRQHMAQMEEMKTRHKGEMENALRSYSNITVNEDQIKLMNVAINELNI 500
501 KLQDTNSQKEKLKEELGLILEEKCALQRQLEDLVEELSFSREQIQRARQT 550
551 IAEQESKLNEAHKSLSTVEDLKAEIVSASESRKELELKHEAEVTNYKIKL 600
601 EMLEKEKNAVLDRMAESQEAELERLRTQLLFSHEEELSKLKEDLEIEHRI 650
651 NIEKLKDNLGIHYKQQIDGLQNEMSQKIETMQFEKDNLITKQNQLILEIS 700
701 KLKDLQQSLVNSKSEEMTLQINELQKEIEILRQEEKEKGTLEQEVQELQL 750
751 KTELLEKQMKEKENDLQEKFAQLEAENSILKDEKKTLEDMLKIHTPVSQE 800
801 ERLIFLDSIKSKSKDSVWEKEIEILIEENEDLKQQCIQLNEEIEKQRNTF 850
851 SFAEKNFEVNYQELQEEYACLLKVKDDLEDSKNKQELEYKSKLKALNEEL 900
901 HLQRINPTTVKMKSSVFDEDKTFVAETLEMGEVVEKDTTELMEKLEVTKR 950
951 EKLELSQRLSDLSEQLKQKHGEISFLNEEVKSLKQEKEQVSLRCRELEII 1000
1001 INHNRAENVQSCDTQVSSLLDGVVTMTSRGAEGSVSKVNKSFGEESKIMV 1050
1051 EDKVSFENMTVGEESKQEQLILDHLPSVTKESSLRATQPSENDKLQKELN 1100
1101 VLKSEQNDLRLQMEAQRICLSLVYSTHVDQVREYMENEKDKALCSLKEEL 1150
1151 IFAQEEKIKELQKIHQLELQTMKTQETGDEGKPLHLLIGKLQKAVSEECS 1200
1201 YFLQTLCSVLGEYYTPALKCEVNAEDKENSGDYISENEDPELQDYRYEVQ 1250
1251 DFQENMHTLLNKVTEEYNKLLVLQTRLSKIWGQQTDGMKLEFGEENLPKE 1300
1301 ETEFLSIHSQMTNLEDIDVNHKSKLSSLQDLEKTKLEEQVQELESLISSL 1350
1351 QQQLKETEQNYEAEIHCLQKRLQAVSESTVPPSLPVDSVVITESDAQRTM 1400
1401 YPGSCVKKNIDGTIEFSGEFGVKEETNIVKLLEKQYQEQLEEEVAKVIVS 1450
1451 MSIAFAQQTELSRISGGKENTASSKQAHAVCQQEQHYFNEMKLSQDQIGF 1500
1501 QTFETVDVKFKEEFKPLSKELGEHGKEILLSNSDPHDIPESKDCVLTISE 1550
1551 EMFSKDKTFIVRQSIHDEISVSSMDASRQLMLNEEQLEDMRQELVRQYQE 1600
1601 HQQATELLRQAHMRQMERQREDQEQLQEEIKRLNRQLAQRSSIDNENLVS 1650
1651 ERERVLLEELEALKQLSLAGREKLCCELRNSSTQTQNGNENQGEVEEQTF 1700
1701 KEKELDRKPEDVPPEILSNERYALQKANNRLLKILLEVVKTTAAVEETIG 1750
1751 RHVLGILDRSSKSQSSASLIWRSEAEASVKSCVHEEHTRVTDESIPSYSG 1800
1801 SDMPRNDINMWSKVTEEGTELSQRLVRSGFAGTEIDPENEELMLNISSRL 1850
1851 QAAVEKLLEAISETSSQLEHAKVTQTELMRESFRQKQEATESLKCQEELR 1900
1901 ERLHEESRAREQLAVELSKAEGVIDGYADEKTLFERQIQEKTDIIDRLEQ 1950
1951 ELLCASNRLQELEAEQQQIQEERELLSRQKEAMKAEAGPVEQQLLQETEK 2000
2001 LMKEKLEVQCQAEKVRDDLQKQVKALEIDVEEQVSRFIELEQEKNTELMD 2050
2051 LRQQNQALEKQLEKMRKFLDEQAIDREHERDVFQQEIQKLEQQLKVVPRF 2100
2101 QPISEHQTREVEQLANHLKEKTDKCSELLLSKEQLQRDIQERNEEIEKLE 2150
2151 FRVRELEQALLVSADTFQKVEDRKHFGAVEAKPELSLEVQLQAERDAIDR 2200
2201 KEKEITNLEEQLEQFREELENKNEEVQQLHMQLEIQKKESTTRLQELEQE 2250
2251 NKLFKDDMEKLGLAIKESDAMSTQDQHVLFGKFAQIIQEKEVEIDQLNEQ 2300
2301 VTKLQQQLKITTDNKVIEEKNELIRDLETQIECLMSDQECVKRNREEEIE 2350
2351 QLNEVIEKLQQELANIGQKTSMNAHSLSEEADSLKHQLDVVIAEKLALEQ 2400
2401 QVETANEEMTFMKNVLKETNFKMNQLTQELFSLKRERESVEKIQSIPENS 2450
2451 VNVAIDHLSKDKPELEVVLTEDALKSLENQTYFKSFEENGKGSIINLETR 2500
2501 LLQLESTVSAKDLELTQCYKQIKDMQEQGQFETEMLQKKIVNLQKIVEEK 2550
2551 VAAALVSQIQLEAVQEYAKFCQDNQTISSEPERTNIQNLNQLREDELGSD 2600
2601 ISALTLRISELESQVVEMHTSLILEKEQVEIAEKNVLEKEKKLLELQKLL 2650
2651 EGNEKKQREKEKKRSPQDVEVLKTTTELFHSNEESGFFNELEALRAESVA 2700
2701 TKAELASYKEKAEKLQEELLVKETNMTSLQKDLSQVRDHLAEAKEKLSIL 2750
2751 EKEDETEVQESKKACMFEPLPIKLSKSIASQTDGTLKISSSNQTPQILVK 2800
2801 NAGIQINLQSECSSEEVTEIISQFTEKIEKMQELHAAEILDMESRHISET 2850
2851 ETLKREHYVAVQLLKEECGTLKAVIQCLRSKEGSSIPELAHSDAYQTREI 2900
2901 CSSDSGSDWGQGIYLTHSQGFDIASEGRGEESESATDSFPKKIKGLLRAV 2950
2951 HNEGMQVLSLTESPYSDGEDHSIQQVSEPWLEERKAYINTISSLKDLITK 3000
3001 MQLQREAEVYDSSQSHESFSDWRGELLLALQQVFLEERSVLLAAFRTELT 3050
3051 ALGTTDAVGLLNCLEQRIQEQGVEYQAAMECLQKADRRSLLSEIQALHAQ 3100
3101 MNGRKITLKREQESEKPSQELLEYNIQQKQSQMLEMQVELSSMKDRATEL 3150
3151 QEQLSSEKMVVAELKSELAQTKLELETTLKAQHKHLKELEAFRLEVKDKT 3200
3201 DEVHLLNDTLASEQKKSRELQWALEKEKAKLGRSEERDKEELEDLKFSLE 3250
3251 SQKQRNLQLNLLLEQQKQLLNESQQKIESQRMLYDAQLSEEQGRNLELQV 3300
3301 LLESEKVRIREMSSTLDRERELHAQLQSSDGTGQSRPPLPSEDLLKELQK 3350
3351 QLEEKHSRIVELLNETEKYKLDSLQTRQQMEKDRQVHRKTLQTEQEANTE 3400
3401 GQKKMHELQSKVEDLQRQLEEKRQQVYKLDLEGQRLQGIMQEFQKQELER 3450
3451 EEKRESRRILYQNLNEPTTWSLTSDRTRNWVLQQKIEGETKESNYAKLIE 3500
3501 MNGGGTGCNHELEMIRQKLQCVASKLQVLPQKASERLQFETADDEDFIWV 3550
3551 QENIDEIILQLQKLTGQQGEEPSLVSPSTSCGSLTERLLRQNAELTGHIS 3600
3601 QLTEEKNDLRNMVMKLEEQIRWYRQTGAGRDNSSRFSLNGGANIEAIIAS 3650
3651 EKEVWNREKLTLQKSLKRAEAEVYKLKAELRNDSLLQTLSPDSEHVTLKR 3700
3701 IYGKYLRAESFRKALIYQKKYLLLLLGGFQECEDATLALLARMGGQPAFT 3750
3751 DLEVITNRPKGFTRFRSAVRVSIAISRMKFLVRRWHRVTGSVSININRDG 3800
3801 FGLNQGAEKTDSFYHSSGGLELYGEPRHTTYRSRSDLDYIRSPLPFQNRY 3850
3851 PGTPADFNPGSLACSQLQNYDPDRALTDYITRLEALQRRLGTIQSGSTTQ 3900
3901 FHAGMRR 3907
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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