| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P53046 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MNSNELDLRNKYFYEIFGKKRKSDTSTPTQLFSGSKVQTNINEISITNDE 50
51 DEDSTEDENKASLKDYTLGHDTGARYRIAPDCSSHQLKASPVLHISTNLN 100
101 SSPQSFTGDQISPTNKKISINDSTRQDKGNSCTTTSSPSQKRSNVLLPHV 150
151 RKHSSPSLLSFSKNSGSHMGDPNQLSTPPTPKSAGHTMELHSSFNGKHSS 200
201 SSTSSLFALESLKTQNRRSSNSSNHSSQYRRHTNQHQRHHSRSKSSPVSL 250
251 TEISMIKGTPLVYPALLSLIAIKFKQTIKLSTHKKMGLLYRDSFTGKQAI 300
301 DTLCLIIGSLDRNLGMLIGKSLEAQKLFHDVLYDHGVRDSVLEIYELSSE 350
351 SIFMAHQSQSSTSIANTFSSSSSSVNSLRTKTEIYGVFVPLTHCYSSTCS 400
401 LEKLCYSISCPNRLQQQANLHLKLGGGLKRNISLALDKEDDERISWTNSV 450
451 PKSVWESLSKQQIKRQEAIYELFTTEKKFVKSLEIIRDTFMKKLLETNII 500
501 PSDVRINFVKHVFAHINEIYSVNREFLKALAQRQSLSPICPGIADIFLQY 550
551 LPFFDPFLSYIASRPYAKYLIETQRSVNPNFARFDDEVSNSSLRHGIDSF 600
601 LSQGVSRPGRYSLLVREIIHFSDPVTDKDDLQMLMKVQDLLKDLMKRIDR 650
651 ASGAAQDRYDVKVLKQKILFKNEYVNLGLNNEKRKIKHEGLLSRKDVNKT 700
701 DASFSGDIQFYLLDNMLLFLKSKAVNKWHQHTVFQRPIPLPLLFICPAED 750
751 MPPIKRYVTENPNCSAGVLLPQYQTSNPKNAIVFAYYGTKQQYQVTLYAP 800
801 QPAGLQTLIEKVKQEQKRLLDETKHITFKQMVGQFFHSYINTNRVNDVLI 850
851 CHAGKILLVATNMGLFVLNYATSINQKPVHLLHKISISQISVLEEYKVMI 900
901 LLIDKKLYGCPLDVIDDAENADFLFRKNSKVLFKYVAMFKDGFCNGKRII 950
951 MIAHHFLHAVQLLIVNPLIFDFNSGNFKKNLKAGLVDFSVDSEPLSFSFL 1000
1001 ENKICIGCKKNIKILNVPEVCDKNGFKMRELLNLHDNKVLANMYKETFKV 1050
1051 VSMFPIKNSTFACFPELCFFLNKQGKREETKGCFHWEGEPEQFACSYPYI 1100
1101 VAINSNFIEIRHIENGELVRCVLGNKIRMLKSYAKKILYCYEDPQGFEII 1150
1151 ELLNF 1155
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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