 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10077 from www.uniprot.org...
The NucPred score for your sequence is 0.87 (see score help below)
1 MFDLDKNTNIESNHVKIGNKNTTRRLIIKSSKNSVRIAYAPPEKHFVDVT 50
51 DRFLLPETETQNLKTRLGIFELEPLPPNGLVCCVLPNGELIQPNDFVLVN 100
101 SPFPGEPFQIARIISFEKSRPCVSTNLYDSVRLNWYFRPRDIQRHLTDTR 150
151 LLFASMHSDIYNIGSVQEKCTVKHRSQIENLDEYKSQAKSYYFDRLFDQN 200
201 INKVFDVVPVTQVKNAPDDVLEDLFKNYDFIVTEYGKGRALLNEPSNCKV 250
251 CKKWCAFDFSVQCADCKKYYHMDCVVPPLLKKPPHGFGWTCATCSFATQR 300
301 KKSTFQKENANVDANHATENNLEGQATQKSVSILKGHNKALSNVSLQEDH 350
351 GKRRNLKSLRSSRNLHQQSRKSLDENKPNSFSNVSKLKRLPWNMRYLDLK 400
401 SDLTVEKKSDIYPSRARISISPMLPTSSEDNLHPLQPLTTADEEMDLDLK 450
451 SDERFKVDIPTFFERWPFLKDLPLKGYLFPLCEPNLQSAMLLVPITYSDA 500
501 LLDDYLCSCWNLWKKLRLPVSAFVFLELTITALYETKLSPAAAFEKLKSW 550
551 MPGFGDPKNCTGKRVDEHKINSLVKEFGVSLQCFVEKLKFEYSLKEIFFS 600
601 FLSWASSPKGLNTFKKLSDSSLSTTTTDSHGLPTCCYDIGMYDLQKILKL 650
651 KKTPICRWCHSKRSSEWFVAPPIEESSPKDKSKIVALCQRCGYVWRYYGY 700
701 PLQQATPSDLRNCDFEPVKKRKADWDHLSNHDNEVKKENNRIRNASSLME 750
751 NPRVSTKTFDNFTLTHDSTINVKADTVKRARQNNIKNKDDVNFSEDRKKC 800
801 CALCGIVGTEGLLVCFKCGTCVHERCYVCDDYAENEQMLVSASHLSGRTT 850
851 RNSASPGIVSGKKSYAKKDQVLSWACLSCRSNDNLGQNNDNHCVLCLQSA 900
901 SHSLMKKTVEGNWVHLICASWTPDVYVPAEESEPVCGIAQLPPNRWEKKC 950
951 EVCGNSFGVCVSSPNSGLTSHVTCAEKANWYLGFEFVKQDQSPFSMLSNL 1000
1001 KSLSFFGNVTEINTNKCMINSWTSLRPVLFGPSEQLPRNFLLRNDIVPNT 1050
1051 NNSAWSEYIRNLYPKAYIYLLQYTIAVCKPTIAPTNVACCCSKCNSTMSP 1100
1101 FWWPGNICQACHCLRVE 1117
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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