 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q99104 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MAASELYTKFARVWIPDPEEVWKSAELLKDYKPGDKVLLLHLEEGKDLEY 50
51 RLDPKTGELPHLRNPDILVGENDLTALSYLHEPAVLHNLRVRFIDSKLIY 100
101 TYCGIVLVAINPYEQLPIYGEDIINAYSGQNMGDMDPHIFAVAEEAYKQM 150
151 ARDERNQSIIVSGESGAGKTVSAKYAMRYFATVSGSASEANVEEKVLASN 200
201 PIMESIGNAKTTRNDNSSRFGKYIEIGFDKRYRIIGANMRTYLLEKSRVV 250
251 FQAEEERNYHIFYQLCASAKLPEFKMLRLGNADSFHYTKQGGSPMIEGVD 300
301 DAKEMAHTRQACTLLGISESYQMGIFRILAGILHLGNVGFASRDSDSCTI 350
351 PPKHEPLTIFCDLMGVDYEEMCHWLCHRKLATATETYIKPISKLQATNAR 400
401 DALAKHIYAKLFNWIVDHVNQALHSAVKQHSFIGVLDIYGFETFEINSFE 450
451 QFCINYANEKLQQQFNMHVFKLEQEEYMKEQIPWTLIDFYDNQPCINLIE 500
501 SKLGILDLLDEECKMPKGTDDTWAQKLYNTHLNKCALFEKPRMSNKAFII 550
551 KHFADKVEYQCEGFLEKNKDTVFEEQIKVLKSSKFKMLPELFQDDEKAIS 600
601 PTSATSSGRTPLTRVPVKPTKGRPGQTAKEHKKTVGHQFRNSLHLLMETL 650
651 NATTPHYVRCIKPNDFKFPFTFDEKRAVQQLRACGVLETIRISAAGFPSR 700
701 WTYQEFFSRYRVLMKQKDVLGDRKQTCKNVLEKLILDKDKYQFGKTKIFF 750
751 RAGQVAYLEKLRADKLRAACIRIQKTIRGWLLRKRYLCMQRAAITVQRYV 800
801 RGYQARCYAKFLRRTKAATTIQKYWRMYVVRRRYKIRRAATIVIQSYLRG 850
851 YLTRNRYRKILREYKAVIIQKRVRGWLARTHYKRTMKAIVYLQCCFRRMM 900
901 AKRELKKLKIEARSVERYKKLHIGMENKIMQLQRKVDEQNKDYKCLMEKL 950
951 TNLEGVYNSETEKLRNDVERLQLSEEEAKVATGRVLSLQEEIAKLRKDLE 1000
1001 QTRSEKKSIEERADKYKQETDQLVSNLKEENTLLKQEKETLNHRIVEQAK 1050
1051 EMTETMERKLVEETKQLELDLNDERLRYQNLLNEFSRLEERYDDLKEEMT 1100
1101 LMLNVPKPGHKRTDSTHSSNESEYTFSSEFAETEDIAPRTEEPIEKKVPL 1150
1151 DMSLFLKLQKRVTELEQEKQLMQDELDRKEEQVFRSKAKEEERPQIRGAE 1200
1201 LEYESLKRQELESENKKLKNELNELRKALSEKSAPEVTAPGAPAYRVLME 1250
1251 QLTSVSEELDVRKEEVLILRSQLVSQKEAIQPKDDKNTMTDSTILLEDVQ 1300
1301 KMKDKGEIAQAYIGLKETNRLLESQLQSQKRSHENEAEALRGEIQSLKEE 1350
1351 NNRQQQLLAQNLQLPPEARIEASLQHEITRLTNENLYFEELYADDPKKYQ 1400
1401 SYRISLYKRMIDLMEQLEKQDKTVRKLKKQLKVFAKKIGELEVGQMENIS 1450
1451 PGQIIDEPIRPVNIPRKEKDFQGMLEYKREDEQKLVKNLILELKPRGVAV 1500
1501 NLIPGLPAYILFMCVRHADYLNDDQKVRSLLTSTINSIKKVLKKRGDDFE 1550
1551 TVSFWLSNTCRFLHCLKQYSGEEGFMKHNTSRQNEHCLTNFDLAEYRQVL 1600
1601 SDLAIQIYQQLVRVLENILQPMIVSGMLEHETIQGVSGVKPTGLRKRTSS 1650
1651 IADEGTYTLDSILRQLNSFHSVMCQHGMDPELIKQVVKQMFYIVGAITLN 1700
1701 NLLLRKDMCSWSKGMQIRYNVSQLEEWLRDKNLMNSGAKETLEPLIQAAQ 1750
1751 LLQVKKKTDDDAEAICSMCNALTTAQIVKVLNLYTPVNEFEERVSVSFIR 1800
1801 TIQMRLRDRKDSPQLLMDAKHIFPVTFPFNPSSLALETIQIPASLGLGFI 1850
1851 ARV 1853
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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