 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34092 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MSKKVQAKQGTDDLVMLPKVSEDEICENLKKRYMNDFIYTNIGPVLISVN 50
51 PFRNLNNSGPDFIEAYRGKHAQEVPPHVYQLAESAYRAMKNDQENQCVII 100
101 SGESGAGKTEAAKLIMGYVSAISGSTEKVEYVKHVILESNPLLEAFGNAK 150
151 TLRNNNSSRFGKYFEIQFDKAGDPVGGKIYNYLLEKSRVVYQNPGERNFH 200
201 IFYQLLAGASAQEKRDYVLSSPESYYYLNQSQCYTVDGINDVSDYAEVRQ 250
251 AMDTIGLTAQEQSDIIRIVACVLHIGNIYFIEDDKGNAAIYDPNALELAA 300
301 SMLCIDSATLQNAILFRVINTGGAGGAGNRRSTYNVPQNVEQANGTRDAL 350
351 ARTIYDRMFSWLVERVNQSLSYYKSPYQNVIGILDIFGFEIFEKNGFEQF 400
401 CINFVNEKLQQFFIELTLKAEQEEYVREGIKWEPIKYFNNQIVCDLIEGK 450
451 SPPGIFSLLDDICSTLHAQSTGTDQKFLEKMAGIYDGHLHWRGMTGAFAI 500
501 KHYAGEVTYEAEGFSDKNKDTLFFDLIEAIQCSKMPFLASLFNEDTGSLQ 550
551 KKRPTTAGFKIKTSAGELMKALSQCTPHYIRCIKPNETKKAKDWENSRVK 600
601 HQVQYLGLLENVRVRRAGFAYRNTFDKVLKRYKKLSSKTWGIWGEWKGDA 650
651 IEGCKTIFQDMNLEAGQWQLGKTKVFIRHPETVFLLEEALDKKDFDCTAK 700
701 IQKAFRNWKAKKHSLEQRAQIAHMFKDKKERQRNSIDRKFTSDYIDFENQ 750
751 FGLQEAMQNAHKKERVVFADTVIKIDRRAKQKNYEMVLTDQALYFVEKSI 800
801 KKKVLVHTLIRRVGLREIKGVSISTLSDNVIVFHLPEHDQVIENDKKTEI 850
851 IIVLVEYFKAIGGGSLNVQFSDRINYTLKKGEQKEISFQKSEQCPTLVVK 900
901 KGGKGLIGTIASGLPSSTDSTPKNYNPNSMSQASSRPAPQQSAGRGRGMP 950
951 QGAGQPQPQQPQQQQRPMPQPQQGGGARPMPQPQQGGGARPMGAPQQGGA 1000
1001 PQQGAGRQLPQPTQQGGAPGGRGAPMGRGAPGGGPAGAGGRPLPTVAKPA 1050
1051 PQPSRPTAKALYDYDASSTDELSFKEGDIIFIVQKDNGGWTQGELKSGQK 1100
1101 GWAPTNYLQYN 1111
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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