 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q39565 from www.uniprot.org...
The NucPred score for your sequence is 0.55 (see score help below)
1 MAEDEGMTAAAPSSSVKDIRFDYIRDRVCSCLKVPDSAYDKLVSGDGRTS 50
51 LVQFMEEAHTKRLLIMLDGKDLSATVKPPPKFKKKTVYFLKLQETKLDND 100
101 NIKKLVIHGEISENPLETLAAISQDVFMPVLTAPANQQGWPDVVAKEVTE 150
151 NLHKFVSNVFVTIGQMKGQTLLPLPPQNTVPTLQPEQSMHSLKDQDKIHI 200
201 LESAIVTWTKQIKNVLKADPDAPLKEPGAYPGPLTELNFWSERAANLNSI 250
251 HEQLTSEKTQKVVKVLELAKSTYYPAFQRLFREVEAAQQEANDNVKFLKP 300
301 LRKYLDKLNMMDDFPMLVDLFKPIMHTLMLIWKHSKSYNSSTRFVTLMQE 350
351 ICNDLIMQACKYVPGSDLIQMEPSEAVDKLRMTLRWLGTFKNYYFEYRAL 400
401 SMQDTPENPWKFQNNSLFARLDSFLERCHDMMDLMSTCMQFNRLERVEIG 450
451 GTKGKVLTNGVKAIHQDFTSAVEKFQQVTYDVMDVDAKQFDEDFFGFRVV 500
501 IKELERRLAAIIIQAFDDCTTIGTTFKLLDSFEGLLDREVIAHDLEKKHT 550
551 DLLHSYARDLKDVADLFHQYKDRPIVAKNSAPYSGAAYWVRGLMERIKDP 600
601 MDRLLTMNKMVLESELFREIQRTYDHLWEEMTEYRTRAVDAWCAQVAATS 650
651 DEKLNLPLLSLIEETADGIRVLGVNFDPALVRLLRETKYFLLLETSTQDK 700
701 NADRNAEKAAEGGEVEVVKKAPKLSVPDSAKDLFASADTFRQQISALDLI 750
751 CSIYNKVQRTILAVEKPLVQQKLDAVEQALNRGLAELNWKCAEIDTYIKE 800
801 CMELVKDVDLVLNTIKDNVKATQGILAMWEKNLMFERKDGKTYTFDELND 850
851 AFNQLIQQRHSEIRDAGKEITKLLSSSNRVLKVSKGAASWRAYVDYFSNI 900
901 VIDGFSAAIISTVRYLLSQIDPDILAKTESSPLLEIQVELVAPDIVWKPD 950
951 LGEGGAKPGLRDMIKKWLQSFLEIGQLMKRLDVGEGNYAKELEEDFEVYD 1000
1001 ALNQVMMVTLANESRCEDFKNQFAKFDYLWKQDLQATLQQFITDNGVTLP 1050
1051 DGTRDDPPLAKFEEQIVKYKNVASEIASFKDTMTMGYVKVNAKPLRQALS 1100
1101 TWASKWVYLFTHYLQEKVVNSITELYTFMDTSNSTLDLKVMGEGVEEEPE 1150
1151 YHPDQDPEEAAAKKAAEEEEKRKALYAIMACMRDIRRRTERGTDTMFEPL 1200
1201 KETVTALHTFGIQLSDTVLHQLDNAEFNWRTLKKKMLNRREQLAPLQQAE 1250
1251 AVEIRRKSDAFNERVEDFRTFFQRKAPFAVSGGELKLEQVKPAYKLLDEF 1300
1301 RSGSLEGYPSVLGIIAESKQLQEAQDLFELYQPGYLQLQRCSEELGHLKS 1350
1351 LWDTVGTVMFTFRDWYKTPWDKIDVDFLVEETKKLSKDIKMLNKAVRNYD 1400
1401 VYRMLEEAIKAVLTSLPLVQDLHHPAMRERHWKLLMQTTGKHFVMDDKFC 1450
1451 LGDLLALELHNYVDACSEIVDRAQKELNIEKQLKKIEDTWAGLSLAFSTY 1500
1501 QDSDVMALLVDDAVNEALEADNLQLQNLSGQKYVQSNPMFLETVSKWQNN 1550
1551 MGRVSAVLETWQNVQKKWQNLESIFIGSADIRVQLPEDSKRFDAVNADFQ 1600
1601 ELMRTAPDITNVVEACTLDGRQERLENMQSMLEQCEKALQEYLETKRVAF 1650
1651 PRFYFVSPADLLDILSKGSNPQLILRHLQKCFDNIDNLSFRKDERGDPTK 1700
1701 IATHMHSKEGEVVEFVEDCSCDGPVEVWLQNVVDSMKLALQVEFRKAIPT 1750
1751 YDELPRTKWIYVYSAQNTVVVSRTFFTQEINEAFDDLEEGNEEALKVELD 1800
1801 RQVQQLADLIDEINKEQTSLDRKKLITLCTIDVHSRDLVQKLIDERVEDQ 1850
1851 MCFQWQSQLRYIQSEKTKTCQVNICDAEIAYSYEYIGNCGCLCITPLTDR 1900
1901 CFITLTQAQRLVLGGAPAGPAGTGKTETTKDLARALGIQCYVFNCSDQMD 1950
1951 YKAMGHTYKGLAQTGAWGCFDEFNRIPVAVLSVCSTQYKTVLDAIRAKKE 2000
2001 RFTFEDADISLKSTVMAFITMNPGYPGRAELPESLKALFRPVSMVVPDLA 2050
2051 LICEIMLMAEGFQMSKILSRKFVILYKLCEDLLSKSRHYDWKLRAIKTTL 2100
2101 YVAGGMKRAAPELSEDKVLLRALRDFNLGKLTADDTSIFMGLLNDLFPKT 2150
2151 LELVPRALDKAFDEAAHKAATELGYQPDDQFLLKISHVRELFVVRWSVFL 2200
2201 LGAAGCGKTAVWRTLLRAQNSSGEKTIYQAVNPKAVTRNELYGYLHPATR 2250
2251 EWKEGLMSVTFRNMANNKTNKHQWIVLDGDIDAEWIESMNTVMDDNKMLT 2300
2301 LASNERIPLTPSMRLLLEINHMVHCSPATVSRGGVIFINADDVGWQPVVA 2350
2351 SWIDKLEAAEYRPLLTALFTKYVDPCLEHCRRNFKTVVPLPAVNQAMTIC 2400
2401 KILEGILPKETVRGAPPPDKKLLHYHFVFACVWAFGGCMLVDKVTDYRTQ 2450
2451 FSKWWVSEWKDVQFPEKGLVYDYYVDEQNCIMVPWEDRVTKFQYIPGDFT 2500
2501 SLFVPTVETTRLTYFLDSLVSNKHYAMFVGNTGTGKSAIMVNKLRNMDTE 2550
2551 TMSFYTINMNSLSEAPALQVILEQPLEKKSGVRYGPPGSRRMVYFVDDMN 2600
2601 MPLVDKYDTQSSIELLRQMVDYHGWYDKVKIQLKEIINCQMAACMNPTAG 2650
2651 SFNITPRMQRHFVTFAVQMPNAEITRAMYYQIIDGHFSSFDVDVAKMSNK 2700
2701 LVDATCELHRNVMHNFLPSAVKFHYQFNLRDLSNITQGLTRAIKEYYREP 2750
2751 VKVARLWVHECERVFRDRMINEADMAKFDEFRVAVTKKFFDDCGGMVAIE 2800
2801 ERPLIYASHASMTYTPEDVPVYNALSSYDVLRKTLEDKLREYNESNAVMD 2850
2851 LVLFQQAMEHVTRIARIIDLPRGNAMLVGVGGSGKQSLARLASYICGYEV 2900
2901 YQISVSSTYGINDFKENLLGLYRKAGTKGTPITFLMTDNQIVKEGFLVYI 2950
2951 NDLLSTGYIADLFTPEDKEAFTNAVRNEVKAAGILDSAENCWDFFIDKVR 3000
3001 KFLHIVLCFSPVGDKFRIRARQFPALVNCTMFDWFHGWPGEALVSVAQRF 3050
3051 LVDVPNMEEVVRENIAYHMAYAHQCVSEASERFKEAFRRYNYTTPKSYLE 3100
3101 LISLYKMLLQLKRDDLRRSKERLENGIDKIAQAAAQVTDLQRVLKEEQIV 3150
3151 VDEKKAQTDELIVSIGKEKAIVDQAVEAGREDEEAATALQTEVSAFQAEC 3200
3201 ERDLLEAEPIIAQAEAALNSLNKKELSELKSFGSPAAEIVQVAAACLVLT 3250
3251 CGGKIPKDRDWNAGKKMMADVNSFLSSLMNFDKDNVPVVCVEVVEKDYIS 3300
3301 NPGFTPDNIKGKSAACAGLCSWVINICKYFRIYQVVAPKRAALAEANKKL 3350
3351 DTANKKLKVIRDEVKRLQDRVALLEQSLMKATEDKNAAIAQADRTARKAQ 3400
3401 MAERLINGLSGENTRWGAEIKRLESLEGRLVGDVLIASAFVSYAGPFNMQ 3450
3451 FRKSLVDEKWLPDIIERQIPMTQGIRPLDLLTDDATKAKWANEGLPTDPL 3500
3501 SVENGAIMSNASRWALMIDPQLQGIKWIINKETNNGLVIIQQSQPKYIDQ 3550
3551 VINCIENGWPLLIENLPVDIDAVLDPVIGKMTIKKGRNIIMKIGDAEVQY 3600
3601 DSRFRLYLQTKLSNPHFKPEVAAQTTLVNFCVTEKGLEDQLLALVVDHER 3650
3651 PDLQEQAAGLVRSLNEYNITLVELENNLLFNLANATGNILENIELIEGLE 3700
3701 ETKRTAVEIEEKVKLAKQTEIQIAKAREVYRPVATRGSLTYFLIDNLNAL 3750
3751 DRVYHYSMANFVFVLKKGMDMTPGGKDESKVPLAERLNQEVDLDKRVELL 3800
3801 VETTCFVLIGYVAQGLFERHKLIVATQLCMQILRSRGELHYAKFEYLLRG 3850
3851 PKVMGADNPLHDWVSDSVWGSVQALKELDDYQGLPEDLIGSSKRWREWME 3900
3901 LERPEDEPLPGDWKRMQEFDKLLLFRALRPDRLTSAMGRFVTNMLGAKYV 3950
3951 TSQPYDLERSYQDASPGTPIFVFLSPGVDVAGSVEALGKKLGFTLDNGKY 4000
4001 ASVSLGQGQEPIAMDRLSAAHKNGGWVLLQNIHLTIDWTTNQLDKKVDKL 4050
4051 VEGAHPDFRLFLSAEPPPSLERGLPISLLQNSIKLTNEPARGLKANLRRA 4100
4101 WNNFNEEILESCAKQAEFRAIVFALCYFHAALLERKKFGVGNLPGARSGI 4150
4151 GWNMNYPFNTGDLLCCGQTANNYLENNVKVPWEDLRYNFGEIMYGGHIVE 4200
4201 DYDRRLAMCYLRKYVNEGLLDNMEFFPGFAMPPNTANHRQVLEFIDEVMP 4250
4251 PETPLAFGLHPNAEIGFKLREAESFCNSLVQLQPRESSGEGGMSAEERAK 4300
4301 LVLDEVVDKLPDIFDMEDVRSKINPDDPNMPFVMVAIQESERMNMLLAEM 4350
4351 KRSLLELDLGLKGDLTMTEPMERLLKALATDAVPGSWRNLAYPSLRPLGS 4400
4401 WLGNLLARHAQLVDWTAELSTPKAVWLSGLFNPQSFLTAVMQATARRNDW 4450
4451 PLDKTVIITEVTKKQPDQIEANSRDGAFIHGLTLEGARWDDKIGALDDSK 4500
4501 PKELFCPMPVILVRAVTQDKAEMKDVYKCPVYTTEARFREEVFEAQLKSK 4550
4551 HTEIKWVLAGVCLFLDVV 4568
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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