 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9TU53 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MSSPFLWSLIILLTFAESNGEAGGFELQRQKRSIDFQQPRMATERGNLVF 50
51 LVGSAQNIEFRTGSLGKIKLNEEDLGECLHQIQKNKEDITDLKRSAVNVP 100
101 QNISSQIHQLNSKLVDLERKFQSLQQTVDKKVCSSNPCQNGGTCLNLHDS 150
151 FFCICPSQWKGPLCSVDVNECQIYSGTPLGCQNGATCENTAGSYSCLCSP 200
201 ETHGPQCASKYDDCEGGSKALCVHGICEDLVRVKADEPKYNCICDAGWTS 250
251 PLNSSACVLDIDECNLQHAPCSPLVQCFNTQGSFYCGACPTGWQGNGYSC 300
301 QDIDECKINNGGCSVVPPVMCVNTLGSYHCQACPPGYQGDGRVCTVIDIC 350
351 SVNNGGCHPEASCSSVLGSLPLCTCLPGYTGNGYGPNGCAQLSDTCLSHP 400
401 CLNGQCIETVSGYLCKCESGWAGINCTENINECLSNPCFNGGTCVDGVNA 450
451 FSCECTRFWTGFLCQIPQQVCGGSLSGMDGSFSYMSPDVGYVHDVNCFWV 500
501 IRTEDRKVLRITFTFFQLESVNNCPHEFLQIHDGDSSAALQLGRFCGSVL 550
551 PHELLSSNNALYFHLYSEHFRSGRGFTIRWETQQPECGGILMGTYGSIKS 600
601 PGYPGNYPPGRDCVWQVVTSPDLLITFTFGTLSLEHHDDCSKDYLEIRDG 650
651 PLYQDPSLGKFCTTLSVPPLQTTGPFARVHFHSDNQINDQGFHITYLTSP 700
701 SDLHCGGNYTDPEGLLSSDLSGPFTHNRQCIYIIKQPLGEQIQVNFTHVE 750
751 LEGQSSCSQSHIEVRDDKILLGKVCDNETLPHIKSIRNHIWIRLKIDASL 800
801 VRASFRAVYQVACGGELTGEGVIRSPFYPNVYPGERICRWTIHQPQSQVV 850
851 ILNFTAFGIESSAHCDTDYIEIGSSSILGSPENKKYCGTDIPLFITSVYN 900
901 FLYVIFVKSSSTENHGFMAKFSAADLACGEILTESTGIIQSPGHPNIYPH 950
951 GINCTWHILVQPGHLIHLIFRKFHLEFHYNCTNDYLEVYDTGSNTYLGRY 1000
1001 CGKSIPPSLTSSTNSLKLIFVADSDLAYEGFLINYEATDASSACMEDYTE 1050
1051 NSGTFTSPNFPNNYPNNWKCIYRITVETSQQIALHFTNFALEEAIGGQCV 1100
1101 ADFVEIRDGGYETSPPLGTYCGSIPPPRIISHSNKLWLQFTSDFLGSGPG 1150
1151 FSAYWDGSLTGCGGNITTPTGVFTSPSYPMPYYHSSECYWLLKASHGSPF 1200
1201 ELEFEDFHLEHHPNCTLDYLAVYDGPSTSSHLLSQLCGNEKPPVIRSTGD 1250
1251 SMFLKFRTDEDQQGGGFLAKYQQTCRNVVIVNRNYGILESIHYPNPYSDN 1300
1301 QRCNWTIQATTGNTVNYTFLAFELENHINCSTDYLELYDGPRRMGRYCGA 1350
1351 DMPPTGSTTGSKLQVLFYTDGVGHQEKGFQMQWFIHGCGGELSGTTGSFS 1400
1401 SPGYPNTYPPNKECIWYITTAPGSSIQLTIHDFDVEYHARCNFDVLEVYG 1450
1451 GPDFHSPRITQLCSQRSSENPMQVSSTGNELAIRFKTDSSINGRGFNASW 1500
1501 QAVPGGCGGIFQAPNGEIHSPNYPSPYRGNTDCSWVIRVERNHRILLNFT 1550
1551 DFDLEPQDSCITAYDGLSSTTTRLASVCGRQQLTNPITSSGNSLFLRFQS 1600
1601 GPSRQGRGFRAQFNQVCGGHILTNSFDTISSPLFPAKYPNNQNCSWVIQA 1650
1651 QPPFNHITLSFDHFGLESSTTCTQDFLEILDGDYDDAPLRGRYCGHSMPH 1700
1701 PITSFSSALTLRFVSDSRVNSDGFHATYAASSSACGGTFHMAEGIFNSPG 1750
1751 YPEVYPSNVECVWNIVSSPGNRLQLSFITFQLEDSQDCSRDFVEVREGNA 1800
1801 TGHLVGRYCGNVLPLNYSSIVGHILWIRFVSDGSGSGTGFQATFTKIFGN 1850
1851 DNIVGTHGKIASPLWPGRYPHNSNYQWIVNVNATQVIHGRILEIDIEGAQ 1900
1901 SCYYDKLRVYDGLGIHSRLIGTYCGTQTTSFSSSRNSLTFQFSSDSSITG 1950
1951 KGFLLEWFAVNASGGPLPTIATGACGGFLRTGDAPVFLFSPGWPESYSNS 2000
2001 ADCTWLIQAPDSTVELNILSLDIEAQRTCDYDKLVIRDGDSNLAPQLAVL 2050
2051 CGREIPGPIRSTGEYMFIRFTSDFSITGAGFNASFHKSCGGYLHADRGII 2100
2101 TSPQYPETYSPNLNCSWHVLVQSGLTIAVHFEQPFQIPSGDSSCSQGDYL 2150
2151 VLKNGPDIYSPPLGPYGRNGHFCGSRPSSTLFTSDNQMFVQFISDGSNGG 2200
2201 QGFKIKYEAKSLACGGNIYIHDVNSAGYVTSPGHPNNYPQHADCNWLIAA 2250
2251 PPGKLIRVQFEDQFNIEETPNCVSNYLELRDGVDSNAPLLAKLCGRSLPS 2300
2301 SQLSSGEVMYLRFRSDNSSTQVGFKIKYAIAQCGGRVTGQSGIIESSGYP 2350
2351 TLPYRDNSFCEWHLKGPSGHYLTIHFEDFHLQNSSGCEKDFVEIWENHTS 2400
2401 GNLLGRYCGNTIPDSIDTSSNVALVRFVTDGSVTASGFRLRFESSMEACG 2450
2451 GELQGPTGTFTSPNYPNPNPHGRVCEWRIMVQEGRRITLTFNNLRLEAHP 2500
2501 SCYSEHVTIFNGIRNNSPQLEKLCGSVNASSEIKSSGNTMKVVFFTDGSR 2550
2551 PFGGFSATYTSSEDAVCGGSLTHFPEGNFTSPGYNGVSNYSRNLNCEWTL 2600
2601 SNPNQGNSSIYIHFEDFYLESHQDCQFDVLEFRVGNADGPLMWRLCGPSK 2650
2651 PIVPLVIPYPEVWIHFVTNEHVEHVGFHAEYSFTDCGGIQLGESGVIASP 2700
2701 NYPASYDSLTHCSWLLEAPQGFTITLTFSDFDIEDHATCAWDSVSVRNGG 2750
2751 SPGSPIIGQYCGTSNPRTIQSGSNQLVVIFNSDHSVQNGGFYATWNTQTL 2800
2801 GCGGILHSDNGTIRSPHWPQNFPENSRCSWTVITHESKQLEISFDNNFRI 2850
2851 PSGDGQCQNSFVKVWAGTEEVAESLLATGCGNVAPGSILTPRNVFIAVFQ 2900
2901 SQETPAQGFSASFVSRCGGNFTNPSGYILSPNYPRQYDNNMNCTYIIEAD 2950
2951 PLSVVLLTFESFHLEARSAITGSCANDGVHIIRGSNLSSTPFATVCGNEI 3000
3001 LSPVTILGPVLLNFYSNAHTTDLGFKFNYKITSCGGVFNVSTGVIKSPAY 3050
3051 SYSDYPNNIYCLYTIVGRDDRVVQLKFSDFDVVPSTFCSQDYLAIYDGSN 3100
3101 ISDPLLGKFCGSNLPPNIKSSNHSMLLVFKTDSFQTARGWKITFQQTLGP 3150
3151 QQGCGGYLTGSDNTFASPDSDSNGRYDKNLNCVWFIIAPVNKLIKLTFNT 3200
3201 FALEAQSAMQRCIYDYVKLYDGDSENANLAGTFCGSTVPAPFISSGNFLT 3250
3251 VQFVSDVTLEREGFNATYTTVDMPCGGTYNATWTPQSISSPNSSNPEVPL 3300
3301 SMCMWFLEAPPHQQVKITVWALELHSQDCDQNYLEFRDSPESNGSPGPQI 3350
3351 CGRNASATPTFYSSRSTAIVIFKSEVLNRNSRVGFTYQIAGCNREYNKAF 3400
3401 GNLKSPGWPDNYDNNLDCTVILTAPQNHTISLFFHSFGIEDSSECTHDFL 3450
3451 EVRNGSDSSSPLFGTYCGTLLPDPIFSRNNKLYLRFKTDSATSNRGYEIV 3500
3501 WTSSPSGCGGTLYGDSGSFTSPGYPGTYPNNTDCEWAIIAPAGRPVTVTF 3550
3551 YFISIDDPGDCVQNYLILYDGPDANSPSFGPYCGADTNIAPFVASSHRVF 3600
3601 IKFHAEYAVYPSAIRLTWDS 3620
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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