 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34109 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MAYKSQHGVDDMVMLSKIANDSILDNLKKRYGGDVIYTYIGNVLISVNPF 50
51 KQIKNLYSERNLLEYRGKFRYELPPHAYAVADDMYRSMYAEGQSQCVIIS 100
101 GESGAGKTEAAKLIMQYIAAVSGKGADVSRVKDVILESNPLLEAFGNAKT 150
151 LRNNNSSRFGKYMEVQFNGIGDPEGGRVTNYLLEKSRVVYQTKGERNFHI 200
201 FYQLLSGANQQLKSELRLDTPDKFNYLSASGCYTVDGVDDSGEFQDVCKA 250
251 MKVIGLTDSEQKEVFRLVAAILYLGNVGFKNNAKDEAAIDQQSKKALENF 300
301 AFLMQTDVSSCEKALCFRTISTGTQGRSARVSTYACPQNSEGAYYSRDAL 350
351 AKALYSRLFDWIVGRVNSALGYKQNSQSLMIGILDIYGFEIFEKNGFEQM 400
401 VINYVNERLQQIFIELTLKTEQEEYFNEGIQWEQIDYFNNKICCDLIESK 450
451 KPAGILTILDDVCNFPKGDDQKFLDRLKESFSSHAHFQSAAQSSSSFTIK 500
501 HYAGDVEYCAEGFVDKNKDLLFNDLVELAACTTSKLIPQLFPEINCEKDK 550
551 RKPTTAGFKIKESIGALVKALSACTPHYIRCIKPNGNKRANDFDTSLVMH 600
601 QVKYLGLLENVRIRRAGYAYRQTYDKFFYRYRVCCKETWPNWTGGFESGV 650
651 ETILKSMDLEPKQYSKGKTKIFIRAPETVFNLEELRERKVFTYANKLQRF 700
701 FLRFTLMSYYYSIQKGAADSMKSNKERRRLSIERPYQGDYINYRENFELK 750
751 DIVKKNGNEKIMFTHAVNKYDRRSRCQRRVLLLSDTAIYFIATEKNKDKE 800
801 DRKKRPWIYVQKRRLLLAGITSVELSKLSDGFVVLKTMNEHDQIFECRRK 850
851 TEFLGTLIKAYKTGTLRINYNNSIGVAIKASKQGGKGKERIILFEKGIKP 900
901 GESVFKGTKVSTPSDGLPADTVPNLTPPESLPVVSIPIYKPAMNAKNAPQ 950
951 NSGGPASNVKPSAKALYDFDAESSMELSFKEGDILTVLDQSSGDWWDAEL 1000
1001 KGRRGKVPSNYLQLIKNAAPPRAGGPPVPTGNRAPTTTTTSGGSTRGGFN 1050
1051 NGPSTAPSGRGAAPPSSRGGMAPRGGSVAPPSSRGGIAPRGGIAPRGGMA 1100
1101 PRGGMAPRV 1109
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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