 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9C0E2 from www.uniprot.org...
The NucPred score for your sequence is 0.53 (see score help below)
1 MMAAALGPPEVIAQLENAAKVLMAPPSMVNNEQRQHAEHIFLSFRKSKSP 50
51 FAVCKHILETSKVDYVLFQAATAIMEAVVREWILLEKGSIESLRTFLLTY 100
101 VLQRPNLQKYVREQILLAVAVIVKRGSLDKSIDCKSIFHEVSQLISSGNP 150
151 TVQTLACSILTALLSEFSSSSKTSNIGLSMEFHGNCKRVFQEEDLRQIFM 200
201 LTVEVLQEFSRRENLNAQMSSVFQRYLALANQVLSWNFLPPNLGRHYIAM 250
251 FESSQNVLLKPTESWRETLLDSRVMELFFTVHRKIREDSDMAQDSLQCLA 300
301 QLASLHGPIFPDEGSQVDYLAHFIEGLLNTINGIEIEDSEAVGISSIISN 350
351 LITVFPRNVLTAIPSELFSSFVNCLTHLTCSFGRSAALEEVLDKDDMVYM 400
401 EAYDKLLESWLTLVQDDKHFHKGFFTQHAVQVFNSYIQCHLAAPDGTRNL 450
451 TANGVASREEEEISELQEDDRDQFSDQLASVGMLGRIAAEHCIPLLTSLL 500
501 EERVTRLHGQLQRHQQQLLASPGSSTVDNKMLDDLYEDIHWLILVTGYLL 550
551 ADDTQGETPLIPPEIMEYSIKHSSEVDINTTLQILGSPGEKASSIPGYNR 600
601 TDSVIRLLSAILRVSEVESRAIRADLTHLLSPQMGKDIVWFLKRWAKTYL 650
651 LVDEKLYDQISLPFSTAFGADTEGSQWIIGYLLQKVISNLSVWSSEQDLA 700
701 NDTVQLLVTLVERRERANLVIQCENWWNLAKQFASRSPPLNFLSSPVQRT 750
751 LMKALVLGGFAHMDTETKQQYWTEVLQPLQQRFLRVINQENFQQMCQQEE 800
801 VKQEITATLEALCGIAEATQIDNVAILFNFLMDFLTNCIGLMEVYKNTPE 850
851 TVNLIIEVFVEVAHKQICYLGESKAMNLYEACLTLLQVYSKNNLGRQRID 900
901 VTAEEEQYQDLLLIMELLTNLLSKEFIDFSDTDEVFRGHEPGQAANRSVS 950
951 AADVVLYGVNLILPLMSQDLLKFPTLCNQYYKLITFICEIFPEKIPQLPE 1000
1001 DLFKSLMYSLELGMTSMSSEVCQLCLEALTPLAEQCAKAQETDSPLFLAT 1050
1051 RHFLKLVFDMLVLQKHNTEMTTAAGEAFYTLVCLHQAEYSELVETLLSSQ 1100
1101 QDPVIYQRLADAFNKLTASSTPPTLDRKQKMAFLKSLEEFMANVGGLLCV 1150
1151 K 1151
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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