 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q05022 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MVASTKRKRDEDFPLSREDSTKQPSTSSLVRNTEEVSFPRGGASALTPLE 50
51 LKQVANEAASDVLFGNESVKASEPASRPLKKKKTTKKSTSKDSEASSANS 100
101 DEARAGLIEHVNFKTLKNGSSLLGQISAITKQDLCITFTDGISGYVNLTH 150
151 ISEEFTSILEDLDEDMDSDTDAADEKKSKVEDAEYESSDDEDEKLDKSNE 200
201 LPNLRRYFHIGQWLRCSVIKNTSLEPSTKKSKKKRIELTIEPSSVNIYAD 250
251 EDLVKSTSIQCAVKSIEDHGATLDVGLPGFTGFIAKKDFGNFEKLLPGAV 300
301 FLGNITKKSDRSIVVNTDFSDKKNKITQISSIDAIIPGQIVDLLCESITK 350
351 NGIAGKVFGLVSGVVNVSHLRTFSEEDLKHKFVIGSSIRCRIIACLENKS 400
401 GDKVLILSNLPHILKLEDALRSTEGLDAFPIGYTFESCSIKGRDSEYLYL 450
451 ALDDDRLGKVHSSRVGEIENSENLSSRVLGYSPVDDIYQLSTDPKYLKLK 500
501 YLRTNDIPIGELLPSCEITSVSSSGIELKIFNGQFKASVPPLHISDTRLV 550
551 YPERKFKIGSKVKGRVISVNSRGNVHVTLKKSLVNIEDNELPLVSTYENA 600
601 KNIKEKNEKTLATIQVFKPNGCIISFFGGLSGFLPNSEISEVFVKRPEEH 650
651 LRLGQTVIVKLLDVDADRRRIIATCKVSNEQAAQQKDTIENIVPGRTIIT 700
701 VHVIEKTKDSVIVEIPDVGLRGVIYVGHLSDSRIEQNRAQLKKLRIGTEL 750
751 TGLVIDKDTRTRVFNMSLKSSLIKDAKKETLPLTYDDVKDLNKDVPMHAY 800
801 IKSISDKGLFVAFNGKFIGLVLPSYAVDSRDIDISKAFYINQSVTVYLLR 850
851 TDDKNQKFLLSLKAPKVKEEKKKVESNIEDPVDSSIKSWDDLSIGSIVKA 900
901 KIKSVKKNQLNVILAANLHGRVDIAEVFDTYEEITDKKQPLSNYKKDDVI 950
951 KVKIIGNHDVKSHKFLPITHKISKASVLELSMKPSELKSKEVHTKSLEEI 1000
1001 NIGQELTGFVNNSSGNHLWLTISPVLKARISLLDLADNDSNFSENIESVF 1050
1051 PLGSALQVKVASIDREHGFVNAIGKSHVDINMSTIKVGDELPGRVLKIAE 1100
1101 KYVLLDLGNKVTGISFITDALNDFSLTLKEAFEDKINNVIPTTVLSVDEQ 1150
1151 NKKIELSLRPATAKTRSIKSHEDLKQGEIVDGIVKNVNDKGIFVYLSRKV 1200
1201 EAFVPVSKLSDSYLKEWKKFYKPMQYVLGKVVTCDEDSRISLTLRESEIN 1250
1251 GDLKVLKTYSDIKAGDVFEGTIKSVTDFGVFVKLDNTVNVTGLAHITEIA 1300
1301 DKKPEDLSALFGVGDRVKAIVLKTNPEKKQISLSLKASHFSKEAELASTT 1350
1351 TTTTTVDQLEKEDEDEVMADAGFNDSDSESDIGDQNTEVADRKPETSSDG 1400
1401 LSLSAGFDWTASILDQAQEEEESDQDQEDFTENKKHKHKRRKENVVQDKT 1450
1451 IDINTRAPESVADFERLLIGNPNSSVVWMNYMAFQLQLSEIEKARELAER 1500
1501 ALKTINFREEAEKLNIWIAMLNLENTFGTEETLEEVFSRACQYMDSYTIH 1550
1551 TKLLGIYEISEKFDKAAELFKATAKKFGGEKVSIWVSWGDFLISHNEEQE 1600
1601 ARTILGNALKALPKRNHIEVVRKFAQLEFAKGDPERGRSLFEGLVADAPK 1650
1651 RIDLWNVYVDQEVKAKDKKKVEDLFERIITKKITRKQAKFFFNKWLQFEE 1700
1701 SEGDEKTIEYVKAKATEYVASHESQKADE 1729
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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