 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P22276 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MVAATKRRKTHIHKHVKDEAFDDLLKPVYKGKKLTDEINTAQDKWHLLPA 50
51 FLKVKGLVKQHLDSFNYFVDTDLKKIIKANQLILSDVDPEFYLKYVDIRV 100
101 GKKSSSSTKDYLTPPHECRLRDMTYSAPIYVDIEYTRGRNIIMHKDVEIG 150
151 RMPIMLRSNKCILYDADESKMAKLNECPLDPGGYFIVNGTEKVILVQEQL 200
201 SKNRIIVEADEKKGIVQASVTSSTHERKSKTYVITKNGKIYLKHNSIAEE 250
251 IPIAIVLKACGILSDLEIMQLVCGNDSSYQDIFAVNLEESSKLDIYTQQQ 300
301 ALEYIGAKVKTMRRQKLTILQEGIEAIATTVIAHLTVEALDFREKALYIA 350
351 MMTRRVVMAMYNPKMIDDRDYVGNKRLELAGQLISLLFEDLFKKFNNDFK 400
401 LSIDKVLKKPNRAMEYDALLSINVHSNNITSGLNRAISTGNWSLKRFKME 450
451 RAGVTHVLSRLSYISALGMMTRISSQFEKSRKVSGPRALQPSQFGMLCTA 500
501 DTPEGEACGLVKNLALMTHITTDDEEEPIKKLCYVLGVEDITLIDSASLH 550
551 LNYGVYLNGTLIGSIRFPTKFVTQFRHLRRTGKVSEFISIYSNSHQMAVH 600
601 IATDGGRICRPLIIVSDGQSRVKDIHLRKLLDGELDFDDFLKLGLVEYLD 650
651 VNEENDSYIALYEKDIVPSMTHLEIEPFTILGAVAGLIPYPHHNQSPRNT 700
701 YQCAMGKQAIGAIAYNQFKRIDTLLYLMTYPQQPMVKTKTIELIDYDKLP 750
751 AGQNATVAVMSYSGYDIEDALVLNKSSIDRGFGRCETRRKTTTVLKRYAN 800
801 HTQDIIGGMRVDENGDPIWQHQSLGPDGLGEVGMKVQSGQIYINKSVPTN 850
851 SADAPNPNNVNVQTQYREAPVIYRGPEPSHIDQVMMSVSDNDQALIKVLL 900
901 RQNRRPELGDKFSSRHGQKGVCGIIVKQEDMPFNDQGIVPDIIMNPHGFP 950
951 SRMTVGKMIELISGKAGVLNGTLEYGTCFGGSKLEDMSKILVDQGFNYSG 1000
1001 KDMLYSGITGECLQAYIFFGPIYYQKLKHMVLDKMHARARGPRAVLTRQP 1050
1051 TEGRSRDGGLRLGEMERDCVIAYGASQLLLERLMISSDAFEVDVCDKCGL 1100
1101 MGYSGWCTTCKSAENIIKMTIPYAAKLLFQELLSMNIAPRLRLEDIFQQ 1149
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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