 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q03660 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MDKEIYCGSVPVSYFDPFDLFESLRPEFQQILPLDNIHWKAFDGTVRTVN 50
51 RLPIELIPEGRGEADKSNDEQPFIRFLIVNCISIDQYRAKVRPLVRQWLP 100
101 NLESVSSSTGEKMIYKPIILLYANSEVVDSNLFKSVSLMEKFGKDFPHVQ 150
151 TLEVRSVYRSPKERQEFWNQFSQKIKASVLSIFQKRLTHLQHSLANLQKG 200
201 NNFEEQLLTREKLYELYVVFNILEDASLELQKIKKEILRRNMNMPDGKLQ 250
251 VPFESSSKSDESLGSIIIEGTLDKFQLHKYFFIRRLRLLKLEDQTLTAFV 300
301 GAFQLIKNFIESISIEYRKSVRLLEFKHYFITSMLSYFEFENVSNPLLCE 350
351 IKAELLMLKRDNWVQGVMATSGYRLMDKNYPNSDVKYKFDLLKETFVDET 400
401 VFQENFLTLTKEILSLFNKCEGKRQRIVDILSIEIGLLYYQGKKYEEAVS 450
451 LFLSCYEYYTQTNWNSIGLKILQVFIDSLSHCPKLDVLQIDGESVSASAV 500
501 LTNAFLNILKLCKDNDSKEIWWKKFMDLQMKNNIHLMYPLDGLFEVTLNS 550
551 KVHLARANVSAIEVNLKSYGFPEDISTKTMRLSLKNMGGDVIVFGASDFL 600
601 LKKGENKLILECRDIMYGEFSLLSFEIIVEGITFVKEFPENQDEFIVVPE 650
651 IYCKESTKVLVKQAHNLNLGEYALELKSVQSDALESLQVEVEVQKNIGNM 700
701 KNLPVSFSMDEIQARKRYNTPFENVRLEYYLLDQITAFDLIIKTSFTKKN 750
751 DQGTFGETKKVRIQCYLQLSVSVEDIFKKDIFFFKFLLNSSVREEPVILY 800
801 SSELSAPDTRNDYNIRGDYIATTPALITFDGNESFINCYEITANNNFDSK 850
851 DIFNLKVRYNTLKEQLDCFITDAVLIEGDVEWFILFEKWKTFWELEILKK 900
901 LKYDYDAFKENRIIRLLKTSIDLNKTKSKIRNLCIEKAVLDKILICLNKV 950
951 SRGIAVCNTDMDEYVRNLVPKQLTVPVQLPGFEQFFHVQFEQMETSHDAL 1000
1001 HDTIATIGNSLSYTVIVENLSGQWGQDVIDDGGYIFEILSSNEWLIHGQK 1050
1051 RCAIKEKRKEFEVHLIPLKKGYLNFPRVEITNINGKSCRVDHSNAFESIL 1100
1101 IF 1102
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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