 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q23868 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MKFHFSSNKLLLISGLILLVLVIGIKLDIFLSSKSTEYIKFNNNNNHFKR 50
51 DKRILLHNEIIDTNNKPSIKNNQIFINDNDNNENISPILLRILNNNNNNN 100
101 NNNGDDKSIYKKNHYIVQFKDRINDETREQLKEFLIGTDIVLDEQPYQSH 150
151 IVHYIPHDSFLVLMTQEQSVLLSSKEWVSWIGEFEPSNKIHLNYNEKSIG 200
201 LPVYIILSDSTNSLIQRWENTLNSILKSYNSKVKLTLINQKKLKSIVYCN 250
251 DESPSPSCSLINSEKLVYQWISEQSESNFIERSEKFQTANRLSPKVVFGT 300
301 KDTLVNNDRVDIPLRGKGQILSIADTGLDGSHCFFSDSKYPIPLNSVNLN 350
351 HRKVVTYITTSTSDDSDKVDGHGTHICGSAAGTPEDSSVNISSFSGLATD 400
401 AKIAFFDLASGSSSLTPPSDLKQLYQPLYDAGARVHCDSWGSVSVEGYTG 450
451 SYSSDTASIDDFLFTHPDFIILRAAGNNEQYLSLLTQSTAKNVITVGAHQ 500
501 TIHENYLTDGPNYINYQSSVDINQELICDFDSRYCNYTTAQCCLESNATT 550
551 GLASCCPTLLRKSVIDAANTQPLLYNENNICSFSSKGPTHDGRMKPDLVA 600
601 PGEYITSARSNGANTTDQCGDGSLPNTNALLAISGTSMATSFAAAATTIL 650
651 RQYLVDGYYPTGSIVESNKLQPTGSLLKALMINNAQLLNGTFQLITSSSI 700
701 TYPSNQVFENFAGASLVQGWGAIRMSNWLHVVNNNNSNNNNKTSDGITKF 750
751 VGIGGLDLRLVKPNQWKEESLSTGQNTSYCFTYKPSSSSSNSGNNIPRVV 800
801 ATLVWTDPPSYAGAKFNLVNNLDLTMIYYRDNGSTIFYSNQGGSSFLGLA 850
851 PTQDTLNNVEGIVHNPTEPMTYRFMVAGTNVPMGPQNFSFVFHGENGEFE 900
901 WADKCPQCSPGQVLACPVENGIGTQLCTSDLQWSSCLIQSCDNNYNYNSI 950
951 KKRCDKFLSYNYIIMIVAGGTMLLIIFILLLIKYQEYKESKKDSFRRFDD 1000
1001 GTGIFVRPKDKDAKVSLADLYNLISPFIIELAISTACSLVSTAASILQPF 1050
1051 YIGQIINDIPTAKHIGDFKSQFILIFILAVIEFVFSTISSWISGIVNEKM 1100
1101 VMRLQNKVFRALIAQDMGFFQKNNAAILMNVLISDTPMLRSALTGILLSI 1150
1151 ATGLCKFVGSLVFIFTISWKLSLVFFATVPVLAFITQIQSQFTKRLTRAL 1200
1201 LFYNSKASQHGQESMVNMHVVTNYCRQDKEIIKYTDNLNNVFQTARRLII 1250
1251 NNTLASSIKWLMVESLAFIILYYGAYLAIQKQFTVGLLISFCLYIGYVID 1300
1301 STTTLFGVYVSYVQSQASATRVFMILRSAPRKRTTLEEEEADRNNGLGGG 1350
1351 GGNNNNNDDDDDDDNYNGKGNDNGKGSDDPNNNNNNTTLENIDDNGELTT 1400
1401 VQPVELTKKQLKKQKEKEQKEYFKRTGISVVEMSLIPSSYIELTDCKGEI 1450
1451 EFKNVSFRYPTRPDVQVLHNINMKFETGKCYGLVGPSGSGKSTLLELISK 1500
1501 FYPLRDGGNIIMDDIDIANIRPNNLRGFVTCVHQNPYLFDASIKDNIGYA 1550
1551 LDNPTIEEVIEAAKLAYAHEFIKDLPQQYDTVLGSAGSLLSGGQKKRIAI 1600
1601 ARAICAKRKIMLLDEITAELDPESEEAITQSIKVLTQGHTVVMVAHKVAA 1650
1651 VRDCDKIFVLDKGYLVEEGTHDELMERKGKYHRMFNNEKDEEELLNNLGL 1700
1701 PSNNETNNENNNENNNNNNNNNNNNNNNNDDNNNQVVGEQV 1741
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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