 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8BMQ3 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MARFVPSPPPNCLSYKSEGRLGEQDWQAHFKVPCCGVDPSQLESEEAEVD 50
51 VRERDTQRDREPKRARDLTLRDSCTDNSMQFGTRTTAAEPGFMGTWQNAD 100
101 TNLLFRMSQQVPLACAGRVLGADFCPNLEEPDQRLEVQAIRCTLVNCTCE 150
151 CFQPGKINLRTCDQCKHGWVAHALDKLSTQHLYHPTQVEIVQSNVVFDIS 200
201 SLMLYGTQAVPVRLKILLDRLFSVLKQEEVLHILHGLGWTLRDYVRGYIL 250
251 QDAAGKVLDRWAIMSREEEIITLQQFLRFGETKSIVELMAIQEKEGQAVA 300
301 VPSSKTDSDIRTFIESNNRTRSPSLLAHLENSNPSSIHHFENIPNSLAFL 350
351 LPFQYINPVSAPLLGLPPNGLLLEQPGLRLREPSISTQNEYNESSESEVS 400
401 PTPYKSDQTPNRNALTSITNVEPKTEPACVSPIQNSAPVSDLSKTEHPKS 450
451 SFRIHRMRRMGSASRKGRVFCNACGKTFYDKGTLKIHYNAVHLKIKHRCT 500
501 IEGCNMVFSSLRSRNRHSANPNPRLHMPMLRNNRDKDLIRATSGAATPVI 550
551 ASTKSNLTLTSPGRPPMGFTTPPLDPVLQNPLPSQLVFSGLKTVQPVPPF 600
601 YRSLLTPGEMVSPPTSLPTSPIIPTSGTIEQHPPPPSEPIVPAVMMGTHE 650
651 PSADLAPKKKPRKSSMPVKIEKEIIDTADEFDDEDDDPNDGGTVVNDMSH 700
701 DNHCHSQDEMSPGMSVKDFSKHNRTRCISRTEIRRADSMTSEDQEPERDY 750
751 ENESESSEPKLGEESMEGDEHLHSEVSEKVLMNSERPDENHSEPSHQDVI 800
801 KVKEEFTDPTYDMFYMSQYGLYNGGGASMAALHESFTSSLNYGSPQKFSP 850
851 EGDLCSSPDPKICYVCKKSFKSSYSVKLHYRNVHLKEMHVCTVAGCNAAF 900
901 PSRRSRDRHSANINLHRKLLTKELDDMSLDSSQPSLSKDLRDEFLMKIYG 950
951 AQHPLGLDGREDASSPAGTEDSHLNGYGRGMAEDYMVLDLSTTSSLQSSS 1000
1001 SVHSSRESDAGSDEGILLDDIDGASDSGESTHKAEAPTLPGSLGAEVSGS 1050
1051 LMFSSLSGSNGGIMCNICHKMYSNKGTLRVHYKTVHLREMHKCKVPGCNM 1100
1101 MFSSVRSRNRHSQNPNLHKNIPFTSID 1127
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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