 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O60266 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MPRNQGFSEPEYSAEYSAEYSVSLPSDPDRGVGRTHEISVRNSGSCLCLP 50
51 RFMRLTFVPESLENLYQTYFKRQRHETLLVLVVFAALFDCYVVVMCAVVF 100
101 SSDKLASLAVAGIGLVLDIILFVLCKKGLLPDRVTRRVLPYVLWLLITAQ 150
151 IFSYLGLNFARAHAASDTVGWQVFFVFSFFITLPLSLSPIVIISVVSCVV 200
201 HTLVLGVTVAQQQQEELKGMQLLREILANVFLYLCAIAVGIMSYYMADRK 250
251 HRKAFLEARQSLEVKMNLEEQSQQQENLMLSILPKHVADEMLKDMKKDES 300
301 QKDQQQFNTMYMYRHENVSILFADIVGFTQLSSACSAQELVKLLNELFAR 350
351 FDKLAAKYHQLRIKILGDCYYCICGLPDYREDHAVCSILMGLAMVEAISY 400
401 VREKTKTGVDMRVGVHTGTVLGGVLGQKRWQYDVWSTDVTVANKMEAGGI 450
451 PGRVHISQSTMDCLKGEFDVEPGDGGSRCDYLEEKGIETYLIIASKPEVK 500
501 KTATQNGLNGSALPNGAPASSKSSSPALIETKEPNGSAHSSGSTSEKPEE 550
551 QDAQADNPSFPNPRRRLRLQDLADRVVDASEDEHELNQLLNEALLERESA 600
601 QVVKKRNTFLLSMRFMDPEMETRYSVEKEKQSGAAFSCSCVVLLCTALVE 650
651 ILIDPWLMTNYVTFMVGEILLLILTICSLAAIFPRAFPKKLVAFSTWIDR 700
701 TRWARNTWAMLAIFILVMANVVDMLSCLQYYTGPSNATAGMETEGSCLEN 750
751 PKYYNYVAVLSLIATIMLVQVSHMVKLTLMLLVAGAVATINLYAWRPVFD 800
801 EYDHKRFREHDLPMVALEQMQGFNPGLNGTDRLPLVPSKYSMTVMVFLMM 850
851 LSFYYFSRHVEKLARTLFLWKIEVHDQKERVYEMRRWNEALVTNMLPEHV 900
901 ARHFLGSKKRDEELYSQTYDEIGVMFASLPNFADFYTEESINNGGIECLR 950
951 FLNEIISDFDSLLDNPKFRVITKIKTIGSTYMAASGVTPDVNTNGFASSN 1000
1001 KEDKSERERWQHLADLADFALAMKDTLTNINNQSFNNFMLRIGMNKGGVL 1050
1051 AGVIGARKPHYDIWGNTVNVASRMESTGVMGNIQVVEETQVILREYGFRF 1100
1101 VRRGPIFVKGKGELLTFFLKGRDKLATFPNGPSVTLPHQVVDNS 1144
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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