 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P17442 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MKFGKYLEARQLELAEYNSHFIDYKALKKLIKQLAIPTLKASSDLDLHLT 50
51 LDDIDEKIIHQRLQENKAAFFFKLERELEKVNGYYLARESDLRIKFNILH 100
101 SKYKDYKINGKLNSNQATSFKNLYAAFKKFQKDLRNLEQYVELNKTGFSK 150
151 ALKKWDKRSQSHDKDFYLATVVSIQPIFTRDGPLKLNDETLHILLELNDI 200
201 DNNNRRADIQSSTFTNDDDDDNNTSNNNKHNNNNNNNNNNNNNNNNNNIL 250
251 HNNYELTTSKISENQLEHLFQASSSSLDMEMEIENWYKEILNIATVKDVQ 300
301 RKHALLRNFRETKIFTYLLQNSSESFHKNVFSLLKECLTTLFLLLVASPL 350
351 DDNSLHIFYKSNQDHIDLSYCDEDDQVFSRKNVFHEAASCPEKSRLFILD 400
401 EALTTSKLSKETVQKLLNAQDIHSRVPLHYAAELGKLEFVHSLLITNLLE 450
451 DVDPIDSDSKTPLVLAITNNHIDVVRDLLTIGGANASPIEKPILDYSKNV 500
501 ISSTKVQFDPLNVACKFNNHDAAKLLLEIRSKQNADNAKNKSSQHLCQPL 550
551 FKKNSTGLCTLHIVAKIGGDPQLIQLLIRYGADPNEIDGFNKWTPIFYAV 600
601 RSGHSEVITELLKHNARLDIEDDNGHSPLFYALWESHVDVLNALLQRPLN 650
651 LPSAPLNEINSQSSTQRLNTIDLTPNDDKFDLDIQDSIPDFALPPPIIPL 700
701 RKYGHNFLEKKIFIKLKLRPGLESIKLTQDNGIIMSSSPGRITLSSNLPE 750
751 IIPRNVILPVRSGEINNFCKDISETNDEEDDDEISEDHDDGEIIFQVDSI 800
801 DDFSMDFEIFPSFGTRIIAKTTAMPFLFKKVAINSIATMNLPLFDTRLNN 850
851 IGSLTLDYQIIFPYPGNPLKIINYEPYWKSTGSDLMTSSKDGNFVTSSSL 900
901 NGSFISVLVCALNDETIVAAPKPYVEFKGTKILLNDLTKEQLEKVVDYDF 950
951 GKIDGSFDEVTLKQYLSSRVVPLRSLLEVIPGSAQLVIRVYFPTDKEIDT 1000
1001 IPIKISPFININQFIDKLLLIIFEHERFLRHSGSGSMRQIVFSSCNWEAC 1050
1051 SILNWKQPNFPVLLQMKNLLRDSTTGKFVGDTPNCLKELAVNPQKMSYLN 1100
1101 TELINIHTMVQFAMNNNLLGVTLPYEVLKICPSLARIIKQNGLLLIASVG 1150
1151 ENDQIPADGGYSGIYYACELLFENNIDM 1178
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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