 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JI55 from www.uniprot.org...
The NucPred score for your sequence is 0.87 (see score help below)
1 DGHNLISLLEVLSGDSLPREKGRMRFHKLQNVQIALDYLRHRQVKLVNIR 50
51 NDDIADGNPKLTLGLIWTIILHFQISDIQVSGQSEDMTAKEKLLLWSQRM 100
101 VEGYQGLRCDNFTTSWRDGRLFNAIIHRHKPMLIDMNKVYRQTNLENLDQ 150
151 AFSVAERDLGVTRLLDPEDVDVPQPDEKSIITYVSSLYDAMPRVPGAQNG 200
201 VRANELQLRWQEYRELVLLLLQWIRHHTAAFEERKFPSSFEEIEILWCQF 250
251 LKFKETELPAKEADKSRSKGIYQSLEGAVQAGQLQIPPGFHPLDVEKEWG 300
301 KLHVAILEREKQLRGEFERLECLQRIVSKLQMEAGLCEEQLNQADSLLQS 350
351 DIRLLASGKVAQRAGEVERDLDKADGMIRLLFNDVQTLKDGRHPQGEQMY 400
401 RRVYRLHERLVAIRTEYNLRLKAGVAAPVTQVTVQSTQRRPELEDSTLRY 450
451 LQDLLAWVEENQRRIDSAEWGVDLPSVEAQLGSHRGMHQSIEEFRAKIER 500
501 ARNDESQLSPATRGAYRECLGRLDLQYAKLLNSSKARLRSLESLHGFVAA 550
551 ATKELMWLNEKEEEEVGFDWSDRNTNMAAKKESYSALMRELEMKEKKIKE 600
601 TQNTGDRLLREDHHARPTVESFQAALQTQWSWMLQLCCCIEAHLKENTAY 650
651 FQFFSDVREAEEQLQKLQETMRREYSCDRSITVTRLEDLLQDAQDEREQL 700
701 NEYKGHLSGLAKRAKAIVQLKPRNPAHPVRGHVPLMAVCDYKQVEVTVHK 750
751 GDQCQLVGPAQPSHWKVLRGPSSEAAVPSVCFLVPPPNQEAQEAVARLEA 800
801 QHQALVTLWHQLHVDMKSLLAWQNLSRDIQLIRSWSLVTFRTLKPEEQRQ 850
851 ALRNLELHYQAFLRDSQDAGGFGPEDRLVAEREYGSCSRHYQQLLQSLEQ 900
901 GEQEESRCQRCISELKDIRLQLEACETRTVHRLRLPLDKEPARECAQRIA 950
951 EQQKAQAEVEGLGKGVARLSAEAEKVLALPEPSPAAPTLRSELELTLGKL 1000
1001 EQVRSLSAIYLEKLKTISLVIRSTQGAEEVLKAHEEQLKEAQAVPATLQE 1050
1051 LEATKASLKKLRAQAEAQQPVFDTLRDELRGAQEVGERLQQRHGERDVEV 1100
1101 ERWRERVNQLLERWQAVLAQIDVRQRELEQLGRQLRYYRESADPLSSWLQ 1150
1151 DAKRRQEQIQAVPIPNSQAAREQLRQEKALLEEIERHGEKVEECQKFAKQ 1200
1201 YINAIKDYELQLVTYKAQLEPVASPAKKPKVQSGSESVIQEYVDLRTRYS 1250
1251 ELTTLTSQYIKFISETLRRMEEEERLAEQQRAEERERLAEGEAALEKQRQ 1300
1301 LAEAHAQAKAQAELEAQELQRRMQEEVARREEAAVNAQQQKRSIQEELQH 1350
1351 LRQSSEAEIQAKAQQVEAAERSRMRIEEEIRVVRLQLETTERQRGGAEGE 1400
1401 LQALRARAEEAEAQKRQAQEEAERLRRQVQDESQRKRQAEAELALRVKAQ 1450
1451 AEAAQEKQRALQALEELRLQAEEAERRLRQAQAERARQVQVALETAQRSA 1500
1501 EVELQSKRASFAEKTAQLERTLQEEHVTVTQLREKAERRAQQQAEAERAR 1550
1551 EEAERELERWQLKANEALRLRLQAEEVAQQRSLAQADAEKQKEEAEREAR 1600
1601 RRGKAEEQAVRQRELAEQELEKQRQLAEGTAQQRLAAEQELIRLRAETEQ 1650
1651 GEQQRQLLEEELARLQREATAATHKRQELEAELAKVRAEMEVLLASKARA 1700
1701 EEESRSTSEKSKQRLEAEADRFRELAEEAARLRALAEEAKRQRQLAEEDA 1750
1751 ARQRAEAERVLTEKLAAISEATRLKTEAEIALKEKEAENERLRRLAEDEA 1800
1801 FQRRRLEEQAALHKADIEERLAQLRKASESELERQKGLVEDTLRQRRQVE 1850
1851 EEILALKVSFEKAAAGKAELELELGRIRSSAEDTMRSKEQAEQEAARQRQ 1900
1901 LAAEEEQRRREAEERVQKSLAAEEEAARQRKAALEEVERLKAKVEEARRL 1950
1951 RERAEQESARQLQLAQEAAQKRLQAEEKAHAFVVQQREEELQQTLQQEQS 2000
2001 MLERLRGEAEAARRAAEEAEEAREQAEREAAQSRKQVEEAERLKQSAEEQ 2050
2051 AQARAQAQAAAEKLRKEAEQEAARRAQAEQAALKQKQAADAEMEKHKKFA 2100
2101 EQTLRQKAQVEQELTTLRLQLEETDHQKSILDEELQRLKAEVTEAARQRS 2150
2151 QVEEELFSVRVQMEELGKLKARIEAENRALILRDKDNTQRFLEEEAEKMK 2200
2201 QVAEEAARLSVAAQEAARLRQLAEEDLAQQRALAEKMLKEKMQAVQEATR 2250
2251 LKAEAELLQQQKELAQEQARRLQEDKEQMAQQLVEETQGFQRTLEVERQR 2300
2301 QLEMSAEAERLKLRMAEMSRAQARAEEDAQRFRKQAEEIGEKLHRTELAT 2350
2351 QEKVTLVQTLEIQRQQSDHDAERLREAIAELEREKEKLKQEAKLLQLKSE 2400
2401 EMQTVQQEQILQETQALQKSFLSEKDSLLQRERFIEQEKAKLEQLFQDEV 2450
2451 AKAQQLREEQQRQQRQMEQEKQELVASMEEARRRQCEAEEAVRRKQEELQ 2500
2501 HLELQRQQQEKLLAEENQRLRERLQRLEEEHRAALAHSEEIAATQAAAAK 2550
2551 ALPNGRDALDGPSMEVEPEHAFEGLRQKVPALQLREAGILSAEELQRLEQ 2600
2601 GHTTVAELSQREDVRQYLQGRSSIAGLLLKPTDEKLSVYTALQRQLLSPG 2650
2651 TALILLEAQAASGFLLDPVRNRRLTVNEPVKEGVVGPELHHKLLSAERAV 2700
2701 TGYKDPYTGEQISLFQAMKKDLLVRDHAIRLLEAQIATGGIIDTVHSHRV 2750
2751 PVDVAYQRGYFDEEMSRILADPGDDTKGFFDPNTHENLTYLQLLERCVED 2800
2801 PETGLHLLPLTDKAAKGGELVYTDTEARDVFEKATVSAPFGKFQGKTVTI 2850
2851 WEIINSEYFTAEQRRDLLRQFRTGRITVEKIIKIVITVVEEQERKGQLCF 2900
2901 EGLRALVPAAELLESGVISHELYQQLQRGERSVREVAEADSVRRALRGAS 2950
2951 VIAGVWLEEAGQKLSIYEALKKDLLQPDVAVALLEAQAGTGHIIDPATSA 3000
3001 RLTVDEAVRAGLVGPELHEKLLSAEKAVTGYRDPYSGQSVSLFQALKKGL 3050
3051 IPREQGLRLLDAQLSTGGMVDPSKSHRVPLDVAYARGYLDKETNRALTSP 3100
3101 RNDARVYLDPSSQEPATYSQLQQRCRADQLTGLSLLPVSEKAVRARQEEV 3150
3151 YSELQARETLEKARVEVPVGGFKGRTMTVWELISSEYFTQEQRQELLRQF 3200
3201 RTGKVTVEKVIRIVITIVEEVETQRQERLSFSGLRAPVPASELLAAKILS 3250
3251 RAQFDQLKEGKTSVKDLSEVGSVRTLLQGSGCLAGVYLEDSKEKVTIYEA 3300
3301 MRRGLLRPSTATLLLEAQAATGFLVDPVRNQRLYVHEAVKAGVVGPELHE 3350
3351 KLLSAEKAVTGYKDPYSGTTISLFQAMKKGLVLREHAIRLLEAQIATGGI 3400
3401 IDPVHSHRLPVDVAYQRGYFDEEMSRILADPSDDTKGFFDPNTHENLTYL 3450
3451 QLLERCVEDPETGLRLLPLKGAEKTEVVETTQVYTEEETRRAFEETQIDI 3500
3501 PGGGSHGGSSMSLWEVMQSDMIPEDQRARLMADFQAGRVTKERMIIIIIE 3550
3551 IIEKTEIIRQQNLASYDYVRRRLTAEDLYEARIISLETYNLFREGTKSLR 3600
3601 EVLEMESAWRYLYGTGSVAGVYLPGSRQTLTIYQALKKGLLSAEVARLLL 3650
3651 EAQAATGFLLDPVKGERLTVDEAVRKGLVGPELHDRLLSAERAVTGYRDP 3700
3701 YTEQTISLFQAMKKELIPAEEALRLLDAQLATGGIVDPRLGFHLPLEVAY 3750
3751 QRGYLNKDTHDQLSEPSEVRSYVDPSTDERLSYTQLLKRCRRDDGSGQML 3800
3801 LPLSDARRLTFRGLRKQITVEELVRSQVMDEATALQLQEGLTSIEEVTKT 3850
3851 LQKFLEGTSCIAGVFVDATKERLSVYQAMKKGIIRPGTAFELLEAQAATG 3900
3901 YVIDPIKGLKLTVEEAVRMGIVGPEFKDRLLSAERAVTGYKDPYSGKLIS 3950
3951 LFQAMKKGLILKDHGIRLLEAQIATGGIIDPEESHRLPVEVAYKRGLFDE 4000
4001 EMNEILTDPSDDTKGFFDPNTEENLTYLQLMERCITDPQTGLRLLPLKEK 4050
4051 KRERKTSSKSSVRKRRVVIVDPETSKEMSVYEAYRKGLIDHQTYLELSEQ 4100
4101 ECEWEEITISSSDGVVKSMIIDRRSGRQYDIDDAITENLIDRSALDQYRA 4150
4151 GTLSITEFADMLSGNAGGFRSRSSSVGSSSSYPISPAVSRTQLASWSDPT 4200
4201 EETGPVAGILDTETLEKVSITEAMHRNLVDNITGQRLLEAQACTGGIIDP 4250
4251 STGERFPVTEAVNKGLVDKIMVDRINLAQKAFCGFEDPRTKTKMSAAQAL 4300
4301 KKGWLYYEAGQRFLEVQYLTGGLIEPDTPGRVPLDEALQRGTVDARTAQK 4350
4351 LRDVSAYSKYLTCPKTKLKISYKDALDRSMVEEGTGLRLLEAAAQSSKGY 4400
4401 YSPYSVSGSGSTTGSRTGSRTGSRAGSRRGSFDATGSGFSMTFSSSSYSS 4450
4451 SGYGRRYASGPPASLGGPESAVA 4473
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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