 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98195 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MADQIPLYPVRSAGAAASHRRAAYYSSAGPGPGADRRGRYQLEDESAHLD 50
51 EMPLMMSEEGFENDESDYHTLPRARITRRKRGLEWFVCGGWKFLCTSCCD 100
101 WLINVCQRKKELKARTVWLGCPEKCEEKHPRNSIKNQKYNVFTFIPGVLY 150
151 EQFKFFLNLYFLVVSCSQFVPALKIGYLYTYWAPLGFVLAVTIAREAIDE 200
201 FRRFQRDKEMNSQLYSKLTVRGKVQVKSSDIQVGDLIIVEKNQRIPSDMV 250
251 FLRTSEKAGSCFIRTDQLDGETDWKLKVAVSCTQRLPALGDLFSISAYVY 300
301 AQKPQLDIHSFEGTFTREDSDPPIHESLSIENTLWASTIVASGTVIGVVI 350
351 YTGKETRSVMNTSNPNNKVGLLDLELNQLTKALFLALVVLSVVMVTLQGF 400
401 AGPWYRNLFRFLLLFSYIIPISLRVNLDMGKAAYGWMIMKDENIPGTVVR 450
451 TSTIPEELGRLVYLLTDKTGTLTQNEMVFKRLHLGTVSYGTDTMDEIQSH 500
501 VLNSYLQVHSQPSGHNPSSAPLRRSQSSTPKVKKSVSSRIHEAVKAIALC 550
551 HNVTPVYEARAGITGETEFAEADQDFSDENRTYQASSPDEVALVRWTESV 600
601 GLTLVSRDLASMQLKTPSGQVLTYCILQMFPFTSESKRMGIIVRDESTAE 650
651 ITFYMKGADVAMSTIVQYNDWLEEECGNMAREGLRTLVVAKRTLTEEQYQ 700
701 DFESRYSQAKLSIHDRALKVAAVVESLEREMELLCLTGVEDQLQADVRPT 750
751 LEMLRNAGIKIWMLTGDKLETATCIAKSSHLVSRTQDIHVFRPVTSRGEA 800
801 HLELNAFRRKHDCALVISGDSLEVCLRYYEHELVELACQCPAVVCCRCSP 850
851 TQKAHIVTLLRQHTRKRTCAIGDGGNDVSMIQAADCGIGIEGKEGKQASL 900
901 AADFSITQFRHIGRLLMVHGRNSYKRSAALGQFVMHRGLIISTMQAVFSS 950
951 VFYFASVPLYQGFLMVGYATIYTMFPVFSLVLDQDVKPEMAILYPELYKD 1000
1001 LTKGRSLSFKTFLIWVLISIYQGGILMYGALLLFEDEFVHVVAISFTALI 1050
1051 LTELLMVALTIRTWHWLMVVAEFLSLGCYVASLAFLNEYFGIGRVSFGAF 1100
1101 LDVAFITTVTFLWKVSAITVVSCLPLYVLKYLKRKLSPPSYSKLSS 1146
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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