 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P36422 from www.uniprot.org...
The NucPred score for your sequence is 0.30 (see score help below)
1 MASKTTFKDLQEIPDFPKEEENILKFWDEINAFKQQLEKTKDCPPFTFYD 50
51 GPPFATGLPHYGNLLAGTIKDVVCRYASQNGKYVERRFGWDCHGLPVEYE 100
101 IDKKLGITNRQEVLKMGVDKYNAECRSIVMRYAQEWRSIVNRFGRWVDFD 150
151 NDYKTLDLKFMESVWWVFKQMFDKGLVYRGCKVMPYSNGCATVLSNFETQ 200
201 QNYKEVDDPSLFIAFKTAEDPKTKFIAWTTTPWTLPSNLALVINKDFDYV 250
251 KVLDAKTQEHYILAECRLPELYKKDKDGYKILEKFKGSELVGREYEPLFP 300
301 YFLSRKQDGCFRILAGDFVTADAGTGIVHCAPGFGDDDYKVSVANNIIKP 350
351 DDPPVPVDENGHFTNVVSDFAGVYIKEADKLIRKNLKERGLLLVDSSFKH 400
401 NYPFCWRSDTPLIYKAVHCWFIKVTALKDDLLANNKKAYWVPKFAQEGRF 450
451 NNWLQNVSDWCFSRSRFWGNPIPIWVSEDFEEVVCIGSVEELKKLTGATE 500
501 ITDLHKDFIDHLTIPSQKGKGVLRRIDEVFDCWFESGSMPYGQQHYPFSM 550
551 NEEEFSKRFPADFIGEGIDQTRGWFYTLNVISTALRNSNPYKNLIVNGIV 600
601 LAADGKKMSKSKKNYDDPLLIASKYSVDAIRLYMINSPLVRAEEMSFKSE 650
651 GVFAVKKDIFLPWYNAYKFLIQSITRWELATGKDYMFNEQLSVDTTKLTN 700
701 PTDRWIIISCQNLINYVRIEMEKYHLYNVVPRLIHFLENLTNWYIRLNRN 750
751 RLKGDYGLEEQETSLNVLFNVILNSTILMSPLVPFITESFYQNLRKVIPK 800
801 GSSYLEESIHFLRIPTPKQELLDEKIERNFERMQNIINFARTLREKRKVS 850
851 LKQPIMSLTVINQDQEFHDSLKDYIQYIEDEINTPSILHEINTANYVDLK 900
901 AIPNHKLLGQKLGKEYNKDLKAAAGNLSAKDIETLKTTGSIDLVGKKLLL 950
951 EDFTITQNYKKEYSSGDLELGGEGEVILLLNLAQDEKLKSKGLVREFTSN 1000
1001 VQKTKKKTGLKVDDNIVIYYDVTKAPKLNAAIQSDLEAVQKVLKKPLVPL 1050
1051 SEKNAALKIVENSLLTFQVDEESVTIEIAYA 1081
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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