 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O42632 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MANVEETVANVQRKIDREKALINAANAMRQSTNNPAVLSRLDGQIKDGRR 50
51 NIDYFESKLRDLDLQRTTSGVDNMSLQPGRSPTNPLTPPPKDNWDGYAQQ 100
101 DQGGYGGPQSQYSQLSGGEALQPPRAPFAAPPPATANKRPNYSKLDLIKY 150
151 DTPHLGPRIQLMLTQLEFKLSVEKQYKDGIEKMVRLYQMEGDRKSKQDAE 200
201 AKRIESNQKIQLLKHSLKRYEDLHVDIDGDGDDNDSLDIPSQRKPLSGQL 250
251 SLRIHAVADVDHAATGRFSRGPETFVNIKVEDTIKGRTKPTRNDRWTDEV 300
301 HDFSIDKANEIEITVYDKTGDHLLPIGMLWVRISDIAEEMRRKKLETELA 350
351 NSGWVAADKMGGHTGIQPDMQFQPPPGQSPAGGPGGGPTPAGVRPPGAPQ 400
401 PQTGPIMIDDWFSLEPVGRIHLTMSFIKNTGNKQPFDLGLGRKGAVRQRK 450
451 EDVVEQYGHKFVQQQFYNVMRCALCAEFLKYAAGMQCSDCNYTCHKKCYP 500
501 KVVTKCITQSNAETDPDEAKLNHRIPHRFENFSNMGANWCCHCGYVLPLG 550
551 RKQTKKCNECGLTCHAHCVHFVPDFCGMSMEVANQILMEIKRTRRGQSSS 600
601 GPGMSQRTLRPQSAAKPLPPTQPQSPGQPGQESPTQSDRYSYGKEQMSPP 650
651 PGPPRQPSYPSSATSVDAARASYSTTGTASTGAPTSPTSGSRPPSGPRTQ 700
701 SSVAAAAAAMNKATQPGYGRSNTDYSPQSGRSSGSGYPTEQRMSQQQAPQ 750
751 PAAYDPSVYANIPSYPPSHQQPAPVPSYPTKSSYPLPQPPPPQSPPQHPQ 800
801 QPTPSQQKMPEQQALTQQPPSAVGEVPGKKVTPAANTQGTGKRIGLDHFN 850
851 FLAVLGKGNFGKVMLAETKTTKQLYAIKVLKKEFIIENDEVESTRSEKRV 900
901 FLIANKERHPFLLNLHACFQTETRVYFVMEYISGGDLMLHIQRGQFGTKR 950
951 AQFYAAEVCLALKYFHENGVIYRDLKLDNIMLTLDGHIKVADYGLCKEEM 1000
1001 WYGSTTSTFCGTPEFMAPEILLDKKYGRAVDWWAFGVLIYQMLLQQSPFR 1050
1051 GEDEDEIYDAILADEPLYPIHMPRDSVSILQKLLTREPEMRLGSGPTDAQ 1100
1101 EIMSHAFFRNINWDDIYHKRVQPPFIPQITSPTDTSNFDTEFTSVTPVLT 1150
1151 PVQSVLSQAMQEEFRGFSYSADFA 1174
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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