 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O95248 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MARLADYFVLVAFGPHPRGSGEGQGQILQRFPEKDWEDNPFPQGIELFCQ 50
51 PSGWQLCPERNPPTFFVAVLTDINSERHYCACLTFWEPAEPSQQETTRVE 100
101 DATEREEEGDEGGQTHLSPTAPAPSAQLFAPKTLVLVSRLDHTEVFRNSL 150
151 GLIYAIHVEGLNVCLENVIGNLLTCTVPLAGGSQRTISLGAGDRQVIQTP 200
201 LADSLPVSRCSVALLFRQLGITNVLSLFCAALTEHKVLFLSRSYQRLADA 250
251 CRGLLALLFPLRYSFTYVPILPAQLLEVLSTPTPFIIGVNAAFQAETQEL 300
301 LDVIVADLDGGTVTIPECVHIPPLPEPLQSQTHSVLSMVLDPELELADLA 350
351 FPPPTTSTSSLKMQDKELRAVFLRLFAQLLQGYRWCLHVVRIHPEPVIRF 400
401 HKAAFLGQRGLVEDDFLMKVLEGMAFAGFVSERGVPYRPTDLFDELVAHE 450
451 VARMRADENHPQRVLRHVQELAEQLYKNENPYPAVAMHKVQRPGESSHLR 500
501 RVPRPFPRLDEGTVQWIVDQAAAKMQGAPPAVKAERRTTVPSGPPMTAIL 550
551 ERCSGLHVNSARRLEVVRNCISYVFEGKMLEAKKLLPAVLRALKGRAARR 600
601 CLAQELHLHVQQNRAVLDHQQFDFVVRMMNCCLQDCTSLDEHGIAAALLP 650
651 LVTAFCRKLSPGVTQFAYSCVQEHVVWSTPQFWEAMFYGDVQTHIRALYL 700
701 EPTEDLAPAQEVGEAPSQEDERSALDVASEQRRLWPTLSREKQQELVQKE 750
751 ESTVFSQAIHYANRMSYLLLPLDSSKSRLLRERAGLGDLESASNSLVTNS 800
801 MAGSVAESYDTESGFEDAETCDVAGAVVRFINRFVDKVCTESGVTSDHLK 850
851 GLHVMVPDIVQMHIETLEAVQRESRRLPPIQKPKLLRPRLLPGEECVLDG 900
901 LRVYLLPDGREEGAGGSAGGPALLPAEGAVFLTTYRVIFTGMPTDPLVGE 950
951 QVVVRSFPVAALTKEKRISVQTPVDQLLQDGLQLRSCTFQLLKMAFDEEV 1000
1001 GSDSAELFRKQLHKLRYPPDIRATFAFTLGSAHTPGRPPRVTKDKGPSLR 1050
1051 TLSRNLVKNAKKTIGRQHVTRKKYNPPSWEHRGQPPPEDQEDEISVSEEL 1100
1101 EPSTLTPSSALKPSDRMTMSSLVERACCRDYQRLGLGTLSSSLSRAKSEP 1150
1151 FRISPVNRMYAICRSYPGLLIVPQSVQDNALQRVSRCYRQNRFPVVCWRS 1200
1201 GRSKAVLLRSGGLHGKGVVGLFKAQNAPSPGQSQADSSSLEQEKYLQAVV 1250
1251 SSMPRYADASGRNTLSGFSSAHMGSHGKWGSVRTSGRSSGLGTDVGSRLA 1300
1301 GRDALAPPQANGGPPDPGFLRPQRAALYILGDKAQLKGVRSDPLQQWELV 1350
1351 PIEVFEARQVKASFKKLLKACVPGCPAAEPSPASFLRSLEDSEWLIQIHK 1400
1401 LLQVSVLVVELLDSGSSVLVGLEDGWDITTQVVSLVQLLSDPFYRTLEGF 1450
1451 RLLVEKEWLSFGHRFSHRGAHTLAGQSSGFTPVFLQFLDCVHQVHLQFPM 1500
1501 EFEFSQFYLKFLGYHHVSRRFRTFLLDSDYERIELGLLYEEKGERRGQVP 1550
1551 CRSVWEYVDRLSKRTPVFHNYMYAPEDAEVLRPYSNVSNLKVWDFYTEET 1600
1601 LAEGPPYDWELAQGPPEPPEEERSDGGAPQSRRRVVWPCYDSCPRAQPDA 1650
1651 ISRLLEELQRLETELGQPAERWKDTWDRVKAAQRLEGRPDGRGTPSSLLV 1700
1701 STAPHHRRSLGVYLQEGPVGSTLSLSLDSDQSSGSTTSGSRQAARRSTST 1750
1751 LYSQFQTAESENRSYEGTLYKKGAFMKPWKARWFVLDKTKHQLRYYDHRV 1800
1801 DTECKGVIDLAEVEAVAPGTPTMGAPKTVDEKAFFDVKTTRRVYNFCAQD 1850
1851 VPSAQQWVDRIQSCLSDA 1868
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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