 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O43861 from www.uniprot.org...
The NucPred score for your sequence is 0.55 (see score help below)
1 MADQIPLYPVRSAAAAAANRKRAAYYSAAGPRPGADRHSRYQLEDESAHL 50
51 DEMPLMMSEEGFENEESDYHTLPRARIMQRKRGLEWFVCDGWKFLCTSCC 100
101 GWLINICRRKKELKARTVWLGCPEKCEEKHPRNSIKNQKYNVFTFIPGVL 150
151 YEQFKFFLNLYFLVISCSQFVPALKIGYLYTYWAPLGFVLAVTMTREAID 200
201 EFRRFQRDKEVNSQLYSKLTVRGKVQVKSSDIQVGDLIIVEKNQRIPSDM 250
251 VFLRTSEKAGSCFIRTDQLDGETDWKLKVAVSCTQQLPALGDLFSISAYV 300
301 YAQKPQMDIHSFEGTFTREDSDPPIHESLSIENTLWASTIVASGTVIGVV 350
351 IYTGKETRSVMNTSNPKNKVGLLDLELNRLTKALFLALVALSIVMVTLQG 400
401 FVGPWYRNLFRFLLLFSYIIPISLRVNLDMGKAVYGWMMMKDENIPGTVV 450
451 RTSTIPEELGRLVYLLTDKTGTLTQNEMIFKRLHLGTVSYGADTMDEIQS 500
501 HVRDSYSQMQSQAGGNNTGSTPLRKAQSSAPKVRKSVSSRIHEAVKAIVL 550
551 CHNVTPVYESRAGVTEETEFAEADQDFSDENRTYQASSPDEVALVQWTES 600
601 VGLTLVSRDLTSMQLKTPSGQVLSFCILQLFPFTSESKRMGVIVRDESTA 650
651 EITFYMKGADVAMSPIVQYNDWLEEECGNMAREGLRTLVVAKKALTEEQY 700
701 QDFESRYTQAKLSMHDRSLKVAAVVESLEREMELLCLTGVEDQLQADVRP 750
751 TLEMLRNAGIKIWMLTGDKLETATCIAKSSHLVSRTQDIHIFRQVTSRGE 800
801 AHLELNAFRRKHDCALVISGDSLEVCLKYYEHEFVELACQCPAVVCCRCS 850
851 PTQKARIVTLLQQHTGRRTCAIGDGGNDVSMIQAADCGIGIEGKEGKQAS 900
901 LAADFSITQFRHIGRLLMVHGRNSYKRSAALGQFVMHRGLIISTMQAVFS 950
951 SVFYFASVPLYQGFLMVGYATIYTMFPVFSLVLDQDVKPEMAMLYPELYK 1000
1001 DLTKGRSLSFKTFLIWVLISIYQGGILMYGALVLFESEFVHVVAISFTAL 1050
1051 ILTELLMVALTVRTWHWLMVVAEFLSLGCYVSSLAFLNEYFGIGRVSFGA 1100
1101 FLDVAFITTVTFLWKVSAITVVSCLPLYVLKYLRRKLSPPSYCKLAS 1147
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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