 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P34616 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MTIRIFFSIFLLNHLIFFHLFNFTHQFSEETIKFSVSEDAKLNTIIGHLE 50
51 AEIGYTYRLSRGNSKIKFDEQTLELSVSSPLDRESENAIDMLIITSPPSI 100
101 IHILIDVLDVNDNSPIFPIDVQRVEIPETAPIGWRVQISGATDPDEGKNG 150
151 TIGKYELVDSLATVDTMSPFGIVQSDGFLFLEVTGKLDRETRDLYSMRLT 200
201 AIDQGVPELSSSCHLNILILDINDNPPNFGIRSLTLNWNGLPNTKLFSLN 250
251 ATDLDSNENSLLTYRILPSGPTSEMFSISDENILVTQNNTECLQRCEFVV 300
301 EARDSGVPPLSTTLNIVVNMEYGNEHEPNINIRFYPSDYPFIIVQPEDVN 350
351 GKTLAILSITDSDGPLGANSTIWIENGNEQSIFSLISRQSINILTVKHVE 400
401 NANQEQYILEFRANDGQSPADRITRKELKIFFKKYVKSTQIHVERESHVT 450
451 VEKDTVPGSFVAHVETNCTDMCSFELANSDVFKIDPFNGIIVTSSILPEG 500
501 VTSYHLPIRIHLPPPSTQLVEADVFVKVIQESVPKNLIRSSESPIHLKRA 550
551 YTFTTWQDVSLGTVIGRLPKAQIYSTIDTVSELGVFPDGSVFVGKTITSD 600
601 FVTLPVTLVNRNTTQTSIITLIVKPLNQHSPICQITEIHVLENAPIGTIF 650
651 GRIQARDEDSGLSGVVSYKILTKSDDYDGIFHLDSTSGSLRSLKAFDAEK 700
701 KRSYTFEYEAKDLGTPSKTTNCPATIFIEDVNDNVPKFGSRYYTATISGK 750
751 SNETVAIVQANDNDVDVKNQKLQYHLLNYHDFFQLDKETGKVTTIQDVPM 800
801 TWQRLNISISAVNMDSERFLQSKTFLLVTVTSSSKLAVQLNSGNLIRIFK 850
851 NDKIGEKVGHLDIASSETVYWSTLDPRLHVDSSGNIILIRRNAKQASTGF 900
901 DIILTSENGEKTEKVNFEVEFVDSERSEDVEKVMDIVLNENTTEVSNLMN 950
951 DWKNWKISRVILENANNSGNNTFFLEHKKLWRTKNATVSNAYIILESEDQ 1000
1001 EGSPKSFKLLHVTTSPSPSSESSCISPAHLISPPSTVPLPSNCSNVKLQN 1050
1051 LKTSLQIHENNLLIPTQSELINHVDLVSTQNSDMKPFMMTLIKDYLSEDV 1100
1101 RFSTNNVLMLLSSIHPIGTSFGRVTAESGYRIRYYIVGTDKISIDADTGE 1150
1151 LILKERFYRNLNDILIVAVIPKGIAKAKITIEVIEDRLILPQSNFFIPSP 1200
1201 PSFNSKSKIGKIPIDRDDVTIDVIDEHFYVRNFEIFVKRHFIPNSNFYDL 1250
1251 KGTVKKGKLSAPISVTLFFGEKMKSREIRENELMFEIEENSPIGTVVGVV 1300
1301 PNSDTTKYRLVDPTCGLLIDQEGIIRTTTVFDRENTSLLKTKMIEPSENR 1350
1351 IWNLLIFIADVNDNKPKILNAPGRIIVYDDLNYKLEWEDLDAIASDVSFS 1400
1401 IVDGDVFGNLEIEDSGVISLNSIPNESFNATIRIYDNRPPFKVHFDDVTI 1450
1451 EFQVTQKLRAVTCEDAEFWMFFGNEDVGMLIASEIVTWRIVPQIGSDSFK 1500
1501 IDPITGIIQSTPNTKPTSDIAKLKIQAISYDGERVGFCDVKIHIDKAAFV 1550
1551 ENVVLSNGTFEFNISETADRFTEVGKIVILGAGLEGSVFRIQDNDYNFTI 1600
1601 SPFDGTIFTNSPLDFENIKTYRFNITAGKSTSQVIIHVTDENDEAPRFIT 1650
1651 GDVVNLKVLEELDTVSYPLIIGSSIAEDLDEGQNGLVTYSILSGNTSLFA 1700
1701 VNSTTGDILSLIPLDREESSLHELLIEAKDAGIPSLSATSKILIHVGDIN 1750
1751 DNTPEFELSSYFIKISENSKIGSKIIRILATDKDKDAELQYSLESNDEIT 1800
1801 IPFRINVATGWITVAGKVNREENEEFRFFVKVTDGEKSSKVIVEIHVEDF 1850
1851 NDNHPMINDRNSDIFVPDPTRSVEIIHVINVHDLDKSDHLKFSLNNSNLN 1900
1901 LSENGEITLKSPLQTAVPVRVTVSDDAGHVAFMEYLFHPHSRKHFPVFVE 1950
1951 KLDTVSVREHDEQELAVFKANGDSIRYSIVSRCSDHLEMEKSTGILKTKS 2000
2001 SLDAEEYSECLVFIIATTYFDNKPLSTITKATIKIVDINDNSPRFDQQLY 2050
2051 RFNVTENSGPKLIGHVIARDIDRSSRVFYEIVGGDANHEFMVTESGQIES 2100
2101 VRDLDRETKSEYHLIVEAIDDGKPRRRGNTTVIVTVLDEDDNAPRFSRIF 2150
2151 HVEVPEDVRIGEPVIQLSASDADEHSNHRFELDGGGEGIPFRVDENTGMV 2200
2201 FVNDSLDFEKKQSYRIKVKLTDGAWLIETSLFVNVKDVNDNAPIFEKPEY 2250
2251 LFISEENSAEIGQFHASDMDSENNGKIRYSVTSPYFKIEPSTGVLSRFRQ 2300
2301 QLPQPLMSLKVTATDHGVPRLQKTVLAHLVDKSSFGKIKQRRIRETTKVG 2350
2351 DVIGKKIDSGATIFPLDVATVTRDGDVVLKKNATQFWILENDTIYEFVKT 2400
2401 DAMESTKNENITLNITSDISMNSDNFKVLRNGSLIVFGFSGNQAHLKIQC 2450
2451 DDGFWPKQDRKIINLVVNNLDADRNSFPLARQPTIRKSMSLPKTMILNIP 2500
2501 FDSPTGTIIWKNLENAVQYMENQKNVNFSNGSKNLILKTPLEETMQIDIF 2550
2551 GQNFERSALTITPNRSLMACPVFQKNFYFFESVANLDSKHPTEIHNFGWS 2600
2601 SDEIKGCQIDIFDKTHLFYQNGSSLIFLKPLLPGTYQFSLQIKSQSDSKI 2650
2651 RSACHVVVTVIPPTNLTTWNIPSVIFATRNYNIPNLFHLPSGYSLSSDQR 2700
2701 TFSLIGSGTGKNISKLSSGVYQVNVVGKDEKKEIVRILLDDVADDVTSKD 2750
2751 IEYHVVSSTLSNLKIPTPIDVECFPRTEENLYEITKDCRLLFNSDVINTT 2800
2801 IPVVTSPANSTWNLRIINESPETVKSLENNAVSLEIITQKSSIPRLITDL 2850
2851 RVTYSDMKIYCLGTWQTSEDIKYHITFVIVDRNGVVIEESEARQTLTSFL 2900
2901 KKHRPGYLDFVDFDKDPCDGVTCIQKNSTCQPTLVGDSASRLVSRSSSVI 2950
2951 FDLPLKKLTARCFCSSGIDCYDDTTNETIQKTQKINVITTCDDIDCGPRG 3000
3001 KCFMEESSQPICRCGQGFESMYSCERADDVFSMSTGGSVEISVRNGTSHL 3050
3051 LKCSENCDGRDIQKIEFDFRTVQLEKSELFRVDFGKQVALIELIGGSLTF 3100
3101 SITDAYARPIETRIEKRVNDGRWHRLLFQMSEDGRRISIQVNGRGKEVKS 3150
3151 RVPLQMLFTAKKIQLMTPAAFCFRRLLAQNQFVHPILNRNKFFEISSTGT 3200
3201 SRNECQFDSIQSGSGGFRLFSNFSNTTTLILLITLALISLIGFSVCLLAI 3250
3251 RRRWRQKSPGDQKQTERSNGWTGHVMPRRRGHINRSMVKSPDDDTYDVAT 3300
3301 VYGMKSTSTDDITHIYTSSSSRRYQPPTAPSYRRDGHINMAYL 3343
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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