 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9U943 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MGTPPHIWFLLILAISSGGLSAAVGGCNHQCTPQSSVFQYQKGQTYTYSF 50
51 EGTTLTSLPGTQGEPVRLKLKATADLSVADDCNKVLRLRGVTVSGPDSKN 100
101 YANLKDLEAHPVLANFKGSSINKQLCSEDGDNQSSLNIKRAILSLLQTPN 150
151 TKSSTASEVDVFGICPTNVRHSQRGDVTVISKTRNLNRYASRENLIQETL 200
201 STRFTGQSDLHATPFLDADLHVEQQIKGGLIVSATSRESYLFRPFSNQGN 250
251 GAKTIVETKLTLTSQNAQPAPPLASFTVPKSIVFEAPHALASVPGGSSAI 300
301 TAALHAAESSTKDGVTVDAAEKFRTLVSVLRQSSTTDILKVYNDVKAGAG 350
351 FSNKHSARNLLLDALFRTSTGDAVEVIARLLKTKEITANHWYLSLAFIQH 400
401 ASLKSVVSISSLLDQKNLPTEAFLGIGSFIGRYCREHNCENVAEFDEVLN 450
451 KFSKHLSGSTTSKAGENRAIAALKALGNIRHLNNALGEKVKQLALDKSLP 500
501 PRVRVAALEVIQSDPCRKNIKQAALQILRDQVEDSELRIKAYLAVVECPC 550
551 DNVVKTISNLLENEPIIQVGSFVVSHLKNLQASTDPSKAEAKEKLGQLKP 600
601 KKIFSSDIRKYSQNYELSYAIDAINAGASVESNVIFSQSSYLPRSVSLNL 650
651 TADVFGHSYNVFEIAARTENLDHIIESFLGPKGYIETEDDDKFVDEVEEK 700
701 TKSLYNRITERFEKTFRQKRSVSKDAVDNIRQQAYKSLLPSQRDRSLDVD 750
751 LSLKTFGSELAWFNYDGKHEQKSSERVVDEIFDAIDEGLKKSKKFNYDFE 800
801 PHFTFLDSELSYPTNLGFPLKLAIDGSIAARLKLNGEVDVRSILRQPENA 850
851 AFRLEFVPSAAVELTGKLLVDAYVVEGGLKLDYNVHSSTGINVAVHNLND 900
901 LGIDIKVGLPVKKQDIIDVKTDVLTTVKERGHPETSTPLHFNLKGNDYKQ 950
951 YRGCFDQLSPVSGLTFCGNVSVPWVSPTQAAAFYPLNGPSHLSVSIEADD 1000
1001 VSEYHFRAEIKKDESAFKSAAVLFDTPGSSADRKVLLLVEKKEKPHQGIT 1050
1051 AHLKSGWKEIVAEGLLIDDNNEKSVSAKLVIESDEYSIKGGVKISGNPSR 1100
1101 QVYKPILEYKAPAKDSGAKVKKSHKTSEGITVDGAVVVERTSDKGKYTFQ 1150
1151 DLSLKTPKGTFVINGQLDIVPRNYAFDLKLSVDKNELLLNGHLNYAEPKS 1200
1201 IDVALEVTSPQFPDYGSGFQLINKRGDDYSDTKIILACGRDLKSDGSRLI 1250
1251 LEHFIKGKYETPDTFNLETKGEVLGTGHKIFGKFDIDSKPKHLEYDLKLG 1300
1301 FDENEVTSDLVAKRDIKSPDDYELKFSAKILDNSIRIESSREVKPKDDSA 1350
1351 FLNTLVVLSGKKYEFQVDVKLAAEDEYHTSLKAESNLKIEGKTSVRLITD 1400
1401 FTTDAQTVNGHVKVSNEGEDFFELIYKLNRGSGNPSGNAKLFVKNYLDGA 1450
1451 ATFKYNNGVGSGTLQIDVLKLHRKIKATGDLTLSGSQRSAAIDLYWDADR 1500
1501 DQSKQLLFKTENDVKEKSIDSKNTLKILDKLTTLNFKGSLSGAIDDGEVE 1550
1551 GQGELILPVGTYLGVKFGRALHLTQADTKVGLHLQAEGRESASSTQPVWK 1600
1601 SDFLLESALTRDSFVGEAKLLFETKGKDDLKLFLSGKSLPQGEKKLISGQ 1650
1651 FSGQGSLIGGRTSVIKLNSEIDETFIAYNLNSECNQGYRANIVGKINRGY 1700
1701 SPVAVKQIENTLELLLPFDKLKQLKHTIIGTFSSQPESTPEFTVSNVIIW 1750
1751 NNENTLKLTGEAAGDEKEGRTKWDLILPKEEPRTLETTWSNAGDNKKAGS 1800
1801 LSFKWGGNKEAKVSTDIEFTSDNQPQILHLKATSPTEKFGIFDLALSLKK 1850
1851 NADPADKIDFELTVTADQKKTDVKGSLGLAPGVPIIDVVAVQPSGTSKVF 1900
1901 VDFLRKSDSELHGAIELQWVAFGGGHLTANGDIKLDIDDFYLKLDVDSPK 1950
1951 FNFNKWHLEAGQRAAKGSKRIVFTAKSAEKVLFSGSTNFHSKAENNKISY 2000
2001 SGNGQVRIGDKAHAFNFRSSRQNLIQDANKEIGVEYNLDFKIAGHGSLHN 2050
2051 ILKVTNKELHALGKQCSEGKPNCAVVEIKSKVSAADAKETTHDLVFLVDL 2100
2101 KSVGVDTGVAFTAETVRRGFWLIDEQASLTLSHNGETTYKYKGYLKESGS 2150
2151 GFTLTLPSRVIAAEVKLSSDVKPNHSKQQISASVWLDKTRLPNSFSSVSI 2200
2201 LLEEIEDKNTDKYVSQLRFTHPNLEKDLTVKGYVQIGLENKLFDSNLEID 2250
2251 IFKQKNQKISISSTVVEQKQNDVVKYLSTLDVKSKGHELDVTGRGEATVK 2300
2301 PSLIALQSVLKYKKDKRIKEFKNFQFEVSTEKLLVHVKVPNHHLLHIDAN 2350
2351 TKINDKHASGDASVHIIGLPTSVIHIEGENKGFPVVKGTISSEGTPNKLE 2400
2401 LIADLSDGLLVEADFISESGKKELFYTFLSGKKDSRKPEFRWSVENIQSA 2450
2451 LEPHKNDIQEVLNKLKEISDEAGNEITKESSRLADSLKAGLPNFRRFVNT 2500
2501 YETQLKALKEEIANDKVLKEISENWKEVIGDAAEVVSTLVNGILVTIDAL 2550
2551 LKTLNELAESVLDALKKSLPALKDSYKQAVDAIVGIAKSLTQSLVNILSS 2600
2601 AAEILKKHEADIKEYLSVLADLANDVGKFVTKITGVIYEGVVEFSKPIKE 2650
2651 KLDGLKFGVAIEFGKVVEQLQNLIVPQELLAFAQEIVSELKETTLTPEIQ 2700
2701 DLLQAIEKYLEKVSKKKDADVEKELKLIFEKAIDAVESVINFVVSEITGG 2750
2751 DHTKDLYDINIPTVLPSFIQLPRVFSVRFSPLIYLVSNGVPCLSDLLASY 2800
2801 RPSLRFDNIIPPYDATAILLNSHHFFTFDRRHLTFKGICSYILAQDVQDG 2850
2851 NFTIIANIEGGSLKSIIVSDQATTFELASDKSLLVNGRPTEYPADEGEFH 2900
2901 AWREYNRVGIQTKAGVKVTCETSIELCTFEINGFYFGKTRGLLGTINNEP 2950
2951 WDDFTKPDGQVASKANEFGNAWKVDAQCANVDGVDHHEHSIKVEECEEVF 3000
3001 SKASLLSPCSLFLDPAPYLEACSHIAHEATTKEEKQLAACRTAAAYVQAC 3050
3051 SVENVFVSVPPHCVHCSVNGDAAIDIGQSFSVKVPQKSADILIVLEQVTG 3100
3101 NAETVKDFVSPIVSQLTQELSSRGISDVWISLLGYGAPGQEYPHLYTSSG 3150
3151 GKLSYDGKQKNIQFGERKVLGPFPFDNFTESIDWLDEFTDQAFHLITTAD 3200
3201 TILDYPFRPGAAKSIIYVLDTSCETTLFLKHLPVKALKLKDAIGSPGIVL 3250
3251 HLVTNVDSVQSKNIVGFDTNHAYYNQEGKKRVVSEVTGNEKAALKISETA 3300
3301 CGQIALATSGTVFNKNNLKQTKKFVAQHIADSLTNVELTQDCKCLPVEGI 3350
3351 HTRAVCAVTGAREKEHLSVKGVKGTKGVKG 3380
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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