 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q07660 from www.uniprot.org...
The NucPred score for your sequence is 0.81 (see score help below)
1 MKRYERDRSPTPDPDIVKGSYSQTSLRSLHELNYKNPAGISGLSFAGSPQ 50
51 QSVASLSQMRLENLVKDKHWEEVEDFGLEELRDGFFDAAFTKPDSKARSP 100
101 NSDIDDDNGAARKKLQSGFTKLSEYVWTAIYRPIIHFPRDIRKNGVSIFK 150
151 FFIAYFIAIVICVIRPSGRWIGHEFRYFLPIAVLIHHPVRNIGVQLEMTI 200
201 SSIIGASFGLGWSALAWYISTATKPTANYQGGILFQSLTMALLFAIWLRS 250
251 VYRRFFYFTTSFSISIIFTHTVRLASSKFDLKWQIFWDFGISYLFGLLLS 300
301 LLVCVCVSPHSGNAELMEHYNKCLQTTKTFLMALVDTELIKSKEQIYLAQ 350
351 VKMVKTLNIDLSQGFRDFVNQLTISRFDLQSLKSLRNSLTAMETSLRVLP 400
401 IAPKIFNDDELKKMYEELEKYRSDSATLSKEASASPQSSGIPTRENTPSA 450
451 FKPIGPGLLKNEIYINALKASFSKSIFNLILEMIFVLENLSRVLKKYESP 500
501 NQKNNLDECVKILSHSHSKLKRKIYKLDVCYRDFVNSSFFSQELLNDEES 550
551 VDIFLFLRYLRNSARQLVTVIHDCQVLGENIHWRIALPSYPLSRALTRLP 600
601 KQCVLDEGAGNVLHYFEAKRDVDEIFERVYNTYTSRHKYNKGEEEALRLD 650
651 SQGGDEKSQNRKNHTISIRAIDHNDFNFHTTQNPWRFKLWKLSRILSGDE 700
701 CKWTLKITFCMIFLCLPTWLPESYHWYQEFHCWWAPLTFYLLAHRRYSGN 750
751 WALVMRRLICGIVGIFWGWAANQSRHFGSPYVVCTFAGLIVVPFSINFLV 800
801 YRNTKSSFTALMCFTIIALEPYSKPNRHYNLTTAGIWKSTWVTGLALIIG 850
851 ILVSIPINWIVWPFRARTELRDSMSSLLAHLGQSYQTVADRYLYRDADDA 900
901 PTDLTFAFSHIREVRLTQSLEAIRELLKKARHEPIIISNFNPEKYASLIN 950
951 SCQFLLSKIIEARISGAFFEIWDQDFDIETTRALLSLRRDSVSSVIFVFY 1000
1001 ILSNCFRSKNKIPRYLPNPIMSRKKLYHFIKKFSEMKDQSHSNLNSGGNS 1050
1051 MEKNLFKKIYQQKASSSGQQQLPLPSVANSSEIDSEKMHWTEVHGIAFAR 1100
1101 AFTDISEALFQVESCAKDILGEENF 1125
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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