 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9V496 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MARMKYNIALIGILASVLLTIAVNAENACNLGCPKSDNGLLKYIPGNYYD 50
51 YSFDSILTIGASSDVPNDSDDTSLKVSGSAKIFAKGNCGYTLQLSSVKVT 100
101 NTKESVEKKILNSIQKPVQFTLVSGILEPQICSDSSDLDYSLNIKRAVVS 150
151 LLQSGIEAEHEVDVFGMCPTHTSTSKVGNANIITKARNLNSCSHREQINS 200
201 GLVSGKVNEKAGITSSLLLQANYIKESRIVNHLIENVQLTETYKFIGNTK 250
251 RNSDISAKVVTILKLKNPSGTKANSPGTGSTVRSLIFQRPETYTSKNINA 300
301 LKTILSDLVDSTGDYVKKETAKKFVEFIRLLRQSDSETLLELAAFPHPNK 350
351 VLARKVYLDGLFRTSTAESARVILKQLSKFDEKEKLLAILSLNIVKSVDK 400
401 ETLNQAASQLLPNAPKELYIAVGNLVAKYCLKNYCQGPEIDAISKKFSDG 450
451 LKHCKPNTKREEERIVYILKGLGNAKSLSGNTVAALSECASTGRSNRIRV 500
501 AALHAFSKVKCEETLQSKSLELLKNRNEDSELRIEAYLSAISCPNAEVAN 550
551 QISEIVNSETVNQVGGFISSNLKAIRDSTDVSRDQQKYHLANIRVTKTFP 600
601 VDYRRYSFNNEVSYKLESLGVGASTDYQIIYSQHGFLPRSSRINVTTEFF 650
651 GTNYNVFEASVRQENVEDVLEYYLGPKGLVNKDFDEIVKLIEVGNNGVAA 700
701 GGRARRSIVDDVSKISKKYKMYGVKNVQDLNLDVSLKLFGSELAFLSLGD 750
751 NIPSSLDDIINYFSTSFEKAKQELSSFEKQFSSHHLFLDTDLAYPTSIGV 800
801 PLELVAQGFAATKVDLAVSLDINAILEQNWQKAKYRLKFVPSVDINANVQ 850
851 IGFNAQVLSTGLRVVSSAHSATGSDITVAVISDGEGFNVDLELPREKLEL 900
901 INFNVDTELYVAEQDKQKAIALKGNKKNKNSQPSEICFNQLELVGLNICI 950
951 KSSTSLSEVQAGNGNVAERGLSVSEKFHLSRPFNFAVYLTTERKFTFKGI 1000
1001 HTQEAFSQKWKLDYSTPGSKVSHDTTVVYELGNKPKTFSRLSFDNSQCHF 1050
1051 AVEGGINNDKNELVVYGQYEQDKEIKKSKIGFSKNGNEYKPLIEIQDNNG 1100
1101 ISNSINGYHADGKIVVKKNSNNIERYNFENFQVSNSNNAHVAVNGWSDVG 1150
1151 TNSLTSELRISLDHQTFLIKENLKLENGLYEAGFFINDEHSPENIYGSSI 1200
1201 HLTIADQSYALKTNGKAAAWSIGSDGSFNFQKLADSNSARAGSLVENVEI 1250
1251 QYKNKQVGGIKIMSNFDVNKMDVDVEISREQKIGSIIVKYESNQRHAQDY 1300
1301 SLEASAKINKHSIDVISKCDFNGNVYVVDNSLVTSWGTLLSAKGEIGQRY 1350
1351 SAQDININIQGNVQISGKDKVTQWILKVIGTPDKTNSDFRISRDTSELIK 1400
1401 LTSESQHPQDKISFAKLNLIVKNQLTAKGEFRVAKNGKGDFTASIDTLKT 1450
1451 EPKHKLEIESKFHIQSPKYDIDASLTLDGKRKVHLKSENTIEKLKFSTKN 1500
1501 IGEANDKIIAFEANGSLKGELRGNGEIQGTFIFNAPDGRVIDGSINRKIS 1550
1551 TNAKSGLSQGNIDAQLSDTPFGSNKKRSISLIGKLDRLNTKTKEFSANSN 1600
1601 LVYTAFNGEKSEISYQIKQQPNGDAKNIDFSLKAYGNPLPQPFEIAFALG 1650
1651 DYSAQHAVVSITSKYGEIFSVSANGNYNNNQALEYGLQANIEIPKSTLKS 1700
1701 LEINSHGKVLKSLIGNENAAYNVEFFLDSKTSLGQYARVNTVWNGTANDG 1750
1751 SYDFEAQTNNMESPLKFNGKYHRKQTGNIKDGDLTGKQTYVLNAQYGAQY 1800
1801 VKMDASLGYGAEKVDIAYVIDSSFDSVKDIKVNIRTFKPLDDSTYVVTAL 1850
1851 FKQTDKSYGLDTTFYHSAHKKGVDIRLDLLKEKPIIISSIAELLGDRKGK 1900
1901 VLFEILNLADLDIKINSEASYVSIDEFYIIVNWSSKKLKLDGYELEARAQ 1950
1951 SKNIKIQLKNENGIIFSGTATYALKKELNKTIIDGQGKVQYQGKALSGNF 2000
2001 KLTRQHFDFGTDREVGFSYTFMGNLGSKNGLGTLKITNKEFNTKFSVCEE 2050
2051 KRQCTNLIVQSIVSIDEQKLDAVEHTTLIIVDLRDFGYPYEFELKSQNTR 2100
2101 QGLKYQYHLDSFIITGNNFKYQFTANVQPTSSTIKLALPKRQILFETTQK 2150
2151 IPADGSLFGRYEQTASFFIDKLQKPDDVARFSAIVDVTGTERVAFNANGK 2200
2201 LKFEHPTIRPLSISGQLNGDVNQQIASAEVIFDIFRLPEQKVVGNSELRN 2250
2251 SRSQNGFNIAYITTVKSAGLQFQYQINSNAAVDIEAHEYNIGLELNNGEI 2300
2301 DVKAISFLNKEKFEISLSESNKHIIYIVGDFSKQNHYAKLNTKVQILDKN 2350
2351 PIEITSEVQPNSAKIILKRQDFIDGTAEVKLGKEFKVDVIGSGKQLFNGR 2400
2401 VALDATNFLQTNYFINEDHLNGFWHIVESEINKDSEYISENIKERLKKSR 2450
2451 QVTDKIVKLAKEAGPDFSKLQGKLLDYKNDIVQELEADQSIAPIIDGIRT 2500
2501 LFKKIAGIVDDINKAISEILEKAQKSIVDIYDKLQALWKDSLLKAWEDFI 2550
2551 ITVQKLISTLKTEFIKICTQSFKDLLSALEKYGPALKNYGKAIGEIVKPI 2600
2601 NDAAQEVIKIVVNAAEGVTHEFKQYVASLPSFESIRNEFNDKVKVLKLFE 2650
2651 KATELTNSLFDQINILPQTPETSEFLQKLHDYLIAKLKQEHIDNEKYIEE 2700
2701 LGQLLIKAVRSIWVSIRSTYPGSSDHVIDFQSWIGSLTHSFDSLAVLPSI 2750
2751 LSFRSSILNCLLNENWDVVFNKKLLYSWIFFNDFELRGHVVDGKHIFTFD 2800
2801 GLNFAYPGNCKYILAQDSVDNNFTIIGQLTNGKLKSITLIDREGSYFEVA 2850
2851 DNLALKLNGNLVEYPQHLSGLHAWRRFYTIHLYSEYGVGIVCTSDLKVCH 2900
2901 ININGFYTSKTRGLLGNGNAEPYDDFLLIDGTLAENSAALGNDYGVGKCT 2950
2951 AIEFDNNQFKSSKRQEMCSELFGIESTLAFNFITLDSRPYRKACDIALAK 3000
3001 VAEKEKEATACTFALAYGSAVKQINKWVLLPPRCIKCAGPAGQHDFGDEF 3050
3051 TVKLPNNKVDVVFVVDINVTPGVLSNLIAPAINDIRESLRSRGFSDVQVG 3100
3101 VIVFEETKRYPALLTSDGGKINYKGNVADVKLAGIKSFCDNCVEQIITEK 3150
3151 RILDIYNSLKEIVKGIAPQADEKAFQLALDYPFRAGAAKSIIGVRSDSLE 3200
3201 YKNWWKFVRAQLTGSITKFDGALIHLIAPVKGLSLEGVLSEKLIGFNSRL 3250
3251 VATVDGKDSKKRTKLQFDNDMGIDFVLNNGGWVFATQNFEKLKASDQKKM 3300
3301 LNQITSSLADTLFKTEIVSDCRCLPIHGLHGQHKCVIKSSTFVANKKAKS 3350
3351 A 3351
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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