 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02440 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MAASELYTKYARVWIPDPEEVWKSAELLKDYKPGDKVLQLRLEEGKDLEY 50
51 CLDPKTKELPPLRNPDILVGENDLTALSYLHEPAVLHNLKVRFIDSKLIY 100
101 TYCGIVLVAINPYEQLPIYGEDIINAYSGQNMGDMDPHIFAVAEEAYKQM 150
151 ARDERNQSIIVSGESGAGKTVSAKYAMRYFATVSGSASEANVEEKVLASN 200
201 PIMESIGNAKTTRNDNSSRFGKYIEIGFDKRYRIIGANMRTYLLEKSRVV 250
251 FQAEEERNYHIFYQLCASAALPEFKTLRLGNANYFHYTKQGGSPVIDGID 300
301 DAKEMVNTRQACTLLGISDSYQMGIFRILAGILHLGNVEFASRDSDSCAI 350
351 PPKHDPLTIFCDLMGVDYEEMAHWLCHRKLATATETYIKPISKLHAINAR 400
401 DALAKHIYANLFNWIVDHVNKALHSTVKQHSFIGVLDIYGFETFEINSFE 450
451 QFCINYANEKLQQQFNMHVFKLEQEEYMKEQIPWTLIDFYDNQPCINLIE 500
501 AKMGVLDLLDEECKMPKGSDDTWAQKLYNTHLNKCALFEKPRLSNKAFII 550
551 KHFADKVEYQCEGFLEKNKDTVYEEQIKVLKSSKKFKLLPELFQDEEKAI 600
601 SPTSATPSGRVPLSRTPVKPAKARPGQTSKEHKKTVGHQFRNSLHLLMET 650
651 LNATTPHYVRCIKPNDFKFPFTFDEKRAVQQLRACGVLETIRISAAGFPS 700
701 RWTYQEFFSRYRVLMKQKDVLSDRKQTCKNVLEKLILDKDKYQFGKTKIF 750
751 FRAGQVAYLEKIRADKLRAACIRIQKTIRGWLMRKKYMRMRRAAITIQRY 800
801 VRGHQARCYATFLRRTRAAIIIQKFQRMYVVRKRYQCMRDATIALQALLR 850
851 GYLVRNKYQMMLREHKSIIIQKHVRGWLARVHYHRTLKAIVYLQCCYRRM 900
901 MAKRELKKLKIEARSVERYKKLHIGLENKIMQLQRKIDEQNKEYKSLLEK 950
951 MNNLEITYSTETEKLRSDVERLRMSEEEAKNATNRVLSLQEEIAKLRKEL 1000
1001 HQTQTEKKTIEEWADKYKHETEQLVSELKEQNTLLKTEKEELNRRIHDQA 1050
1051 KEITETMEKKLVEETKQLELDLNDERLRYQNLLNEFSRLEERYDDLKDEM 1100
1101 NLMVSIPKPGHKRTDSTHSSNESEYTFSSEITEAEDLPLRMEEPSEKKAP 1150
1151 LDMSLFLKLQKRVTELEQEKQSLQDELDRKEEQALRAKAKEEERPPIRGA 1200
1201 ELEYESLKRQELESENKKLKNELNELQKALTETRAPEVTAPGAPAYRVLL 1250
1251 DQLTSVSEELEVRKEEVLILRSQLVSQKEAIQPKEDKNTMTDSTILLEDV 1300
1301 QKMKDKGEIAQAYIGLKETNRLLESQLQSQKKSHENELESLRGEIQSLKE 1350
1351 ENNRQQQLLAQNLQLPPEARIEASLQHEITRLTNENLDLMEQLEKQDKTV 1400
1401 RKLKKQLKVFAKKIGELEVGQMENISPGQIIDEPIRPVNIPRKEKDFQGM 1450
1451 LEYKKEDEQKLVKNLILELKPRGVAVNLIPGLPAYILFMCVRHADYLNDD 1500
1501 QKVRSLLTSTINGIKKVLKKRGDDFETVSFWLSNTCRFLHCLKQYSGEEG 1550
1551 FMKHNTPRQNEHCLTNFDLAEYRQVLSDLAIQIYQQLVRVLENILQPMIV 1600
1601 SGMLEHETIQGVSGVKPTGLRKRTSSIADEGTYTLDSIIRQLNSFHSVMC 1650
1651 QHGMDPELIKQVVKQMFYIIGAVTLNNLLLRKDMCSWSKGMQIRYNVSQL 1700
1701 EEWLRDKNLMNSGAKETLEPLIQAAQLLQVKKKTDEDAEAICSMCNALTT 1750
1751 AQIVKVLNLYTPVNEFEERVLVSFIRTIQLRLRDRKDSPQLLMDAKHIFP 1800
1801 VTFPFNPSSLALETIQIPASLGLGFISRV 1829
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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