 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9QYF3 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MAASELYTKFARVWIPDPEEVWKSAELLKDYKPGDKVLLLHLEEGKDLEY 50
51 RLDPKTSELPHLRNPDILVGENDLTALSYLHEPAVLHNLRVRFIDSKLIY 100
101 TYCGIVLVAINPYEQLPIYGEDIINAYSGQNMGDMDPHIFAVAEEAYKQM 150
151 ARDERNQSIIVSGESGAGKTVSAKYAMRYFATVSGSASEANVEEKVLASN 200
201 PIMESIGNAKTTRNDNSSRFGKYIEIGFDKRYRIIGANMRTYLLEKSRVV 250
251 FQAEEERNYHIFYQLCASAKLPEFKMLRLGNADSFHYTKQGGSPMIEGVD 300
301 DAKEMAHTRQACTLLGISESYQMGIFRILAGILHLGNVGFASRDSDSCTI 350
351 PPKHEPLIIFCDLMGVDYEEMCHWLCHRKLATATETYIKPISKLQATNAR 400
401 DALAKHIYAKLFNWIVGHVNQALHSAVKQHSFIGVLDIYGFETFEINSFE 450
451 QFCINYANEKLQQQFNMHVFKLEQEEYMKEQIPWTLIDFYDNQPCINLIE 500
501 SKLGILDLLDEECKMPKGTDDTWAQKLYNTHLNKCALFEKPRMSNKAFII 550
551 KHFADKVEYQCEGFLEKNKDTVFEEQIKVLKSSKFKMLPELFQDDEKAIS 600
601 PTSATSSGRTPLTRVPVKPTKGRPGQTAKEHKKTVGLQFRNSLHLLMETL 650
651 NATTPHYVRCIKPNDFKFPFTFDEKRAVQQLRACGVLETIRISAAGFPSR 700
701 WTYQEFFSRYRVLMKQKDVLGDRKQTCQNVLEKLILDKDKYQFGKTKIFF 750
751 RAGQVAYLEKLRADKLRAACIRIQKTIRGWLLRKRYLCMQRAAITVQRYV 800
801 RGYQARCYAKFLRRTKAATTIQKYWRMYVVRRKYKIRRAATIVLQSYLRG 850
851 YLARNRYRKILREHKAVIIQKRVRGWLARTHYKRTMKAIIYLQCCFRRMM 900
901 AKRELKKLKIEARSVERYKKLHIGMENKIMQLQRKVDEQNKDYKCLMEKL 950
951 TNLEGVYNSETEKLRNDVERLQLSEEEAKVATGRVLSLQEEIAKLRKDLE 1000
1001 QTRSEKKSIEERADKYKQETEQLVSNLKEENTLLKQEKETLNHLMVEQAK 1050
1051 EMTETMERKLVEETKQLELDLNDERLRYQNLLNEFSRLEERYDDLKEEMT 1100
1101 LMLNVPKPGHKRTDSTHSSNESEYTFSSEFAETEDIAPRTEEPTEKKVPL 1150
1151 DMSLFLKLQKRVTELGQEKQLMQDELDRKEEQVLRSKAKGGERPQIRGAE 1200
1201 LGYESLKRQELESENKKLKNELNELRKALSEKSAPEVNAPGAPAYRVLME 1250
1251 QLTAVSEELDVRKEEVLILRSQLVSQKEAIQPKDDKNTMTDSTILLEDVQ 1300
1301 KMKDKGEIAQAYIGLKETNRLLESQLQSQKRSHENEAEALRGEIQSLKEE 1350
1351 NNRQQQLLAQNLQLPPEARIEASLQHEITRLTNENLDLMEQLEKQDKTVR 1400
1401 KLKKQLKVFAKKIGELEVGQMENISPGQIIDEPIRPVNIPRKGKDFQGML 1450
1451 EYKREDEQKLVKNLILELKPRGVAVNLISGLPAYILFMCVRHADYLDDDQ 1500
1501 KVRSLLTSTINSIKKVLKKRGDDFETVSFWLSNTCRFLHCLKQYSGEEGF 1550
1551 MKHNTSRQNEHCLTNFDLAEYRQVLSDLAIQIYQQLVRVLENILQPMIVS 1600
1601 GMLEHETIQGVSGVKPTGLRKRTSSIADEGTYTLDSILRQLNSFHSVMCQ 1650
1651 HGMDPELIKQVVKQMFYIVGAITLNNLLLRKDMCSWSKGMQIRYNVSQLE 1700
1701 EWLRDKNLMNSGAKETLEPLIQAAQLLQVKKKTDDDAEAICSMCNALTTA 1750
1751 QIVKVLNLYTPVNEFEERVSVSFIRTIQVRLRDRKDSPQLLMDAKHIFPV 1800
1801 TFPFNPSSLALETIQIPASLGLGFIARV 1828
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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