 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P377 from www.uniprot.org...
The NucPred score for your sequence is 0.50 (see score help below)
1 MDYKKGEHSPSSSIQILSDDATINSNYAYGEQLQSNDQYNNIQHPAPSFA 50
51 NPFIHEQDDSYSDILEEEPDEDAYDSPERPSSTEEFISQDESNISAGSSF 100
101 MFPYNRGHPLSKRHDSIMVDEFGHEYIVEGDSIASADEAIDYDALYASWT 150
151 AETKAPILAIDIENIYIELAMKFGFQWDNMRNMFDYLMVMLDSRASRMTP 200
201 QEALLTLHADYIGGPQSNFKKWYFACKMDQFDLKSGVLSFISRDPSTQVP 250
251 YKDMSSCEALWISRMDELSNYERIEQLALYLLCWGEANNVRFMPECLCFI 300
301 YKVAYDYLISPSFKEQKNPAPKDYFLDNCITPLYNLMHDQQYEIRDQKYV 350
351 RKEKDHASIIGYDDINQMFWYSKGLKALLLSDGSRIMDADVASRYFLLAD 400
401 IQWQRVCYKSFRESRTWLHFLHNFSRIWILHISVFWYFTVYNSPTIYTPN 450
451 FHYLEGTQPARAAKWCAPALAGAVASFISFLALILEAYFVPRNNPGAQPV 500
501 IPRLIFVSILIALNIVPAAFIFGFSNATQQHYRSREIVGYVHFFFSIGCV 550
551 AYQSFIPLPFLLGPRFKFRSSSRKYLANSYFTNDIASLPWGRTLLSAALW 600
601 ITVFIAKFVESYYFLTLSVRDPIRFLQRMKPYDCYDFMIGASLCSHQPKF 650
651 LLSLVYLTDLVLFFLDTYLWYMLISTMFSIAYSFYMGSAIWTPWRVIFSN 700
701 LPRRIYFTLLAYKDLSTEFKPKIYVGQIWNSIMISMYREHLLSLEHLKGL 750
751 LYQQVGSEYFGKQTFQSPKFFMEAAKGLNKWDAFFRRNSEAERRISFFAQ 800
801 SLGGKIPDAVPVPKMPSFTVLIPHYGEKILLSLREIIREQDPMSRITLLE 850
851 YLKQLYPNDWDNFVQDTKLMAGDVGVEETKSDVKSEKGKKQGTVKEDLPF 900
901 YCIGFKSTAPEYTLRTRIWASLRSQTLYRTASGMMNYSRALKLLYRVEQP 950
951 NLLDDCDGNFERLEHQLEQMAYRKFRLCISMQRYAKFNRDEYENAEFLLR 1000
1001 AHPELQIAYLDQDPSEDGEEPKVYATLINGFCPFENGRRLPKYRIRLSGN 1050
1051 PILGDGKADNQNMALPFVRGEYLQLIDANQDNYIEECMKIRNVLSEFEEM 1100
1101 DCATLTPYTKKGNARHPVAMLGAREYVFSENSGILGDVAAGKEQTFGTLF 1150
1151 SRSLALIGGKLHYGHPDFLNTIFMTTRGGVSKAQKGLHVNEDIYAGMTAL 1200
1201 QRGGRIKHCDYFQCGKGRDLGFGTIINFTTKIGTGMGEQSLSREYFYLGT 1250
1251 QLPFFRMLSFYYAHAGFHLNNVFIMISMQLLMLVFVNLGAMYHTVEICDY 1300
1301 QAGAAINASLYPPGCYMLKPVLDWIRRCIISIFIVFFISFLPLVVHDLLE 1350
1351 KGVIRAVARLCKQIFSLSPMFEVFVTQNYANSIFTNLTYGGARYIATGRG 1400
1401 LATTRVPFSVLYSLYTGSSIYLGSRLIMMLLFGTMTVWTTHYVYFWVTMF 1450
1451 ALVICPFIYNPHQFSFVDFFVDYREFLRWLSRGNTKGHAHSWIGFCRLAR 1500
1501 TRITGVNRKVKGSPSNKLTMDMPRAGLRNVIFTEVFLPACFAFFTICAYT 1550
1551 FMNSQPGLEDKSRAVNGFIRIWIMAALPIAISTAALLILLMFSCMLGPLL 1600
1601 RKCSKRYGAVLAALAHAVSVFGLVFTFEALWFLEAWSFSKTVLGCIVIFA 1650
1651 IHRLVFKLVVVFLLPREVASGENNYSWWDGHWFGRKGIPYMPIQFIREFM 1700
1701 CKVVEMNLFAMDFILSHCILFSLTPILCIPFIDIPHSVLLFWLHPSRQIR 1750
1751 PPIYTRKQNQLRRRTFYRYSLLFFALLCTFVAMIVVPLVLDQKLSYQFKF 1800
1801 ENSVKFFRLMQPSLGVLPNTNKNTSE 1826
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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