 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8TE73 from www.uniprot.org...
The NucPred score for your sequence is 0.87 (see score help below)
1 MFRIGRRQLWKHSVTRVLTQRLKGEKEAKRALLDARHNYLFAIVASCLDL 50
51 NKTEVEDAILEGNQIERIDQLFAVGGLRHLMFYYQDVEEAETGQLGSLGG 100
101 VNLVSGKIKKPKVFVTEGNDVALTGVCVFFIRTDPSKAITPDNIHQEVSF 150
151 NMLDAADGGLLNSVRRLLSDIFIPALRATSHGWGELEGLQDAANIRQEFL 200
201 SSLEGFVNVLSGAQESLKEKVNLRKCDILELKTLKEPTDYLTLANNPETL 250
251 GKIEDCMKVWIKQTEQVLAENNQLLKEADDVGPRAELEHWKKRLSKFNYL 300
301 LEQLKSPDVKAVLAVLAAAKSKLLKTWREMDIRITDATNEAKDNVKYLYT 350
351 LEKCCDPLYSSDPLSMMDAIPTLINAIKMIYSISHYYNTSEKITSLFVKV 400
401 TNQIISACKAYITNNGTASIWNQPQDVVEEKILSAIKLKQEYQLCFHKTK 450
451 QKLKQNPNAKQFDFSEMYIFGKFETFHRRLAKIIDIFTTLKTYSVLQDST 500
501 IEGLEDMATKYQGIVATIKKKEYNFLDQRKMDFDQDYEEFCKQTNDLHNE 550
551 LRKFMDVTFAKIQNTNQALRMLKKFERLNIPNLGIDDKYQLILENYGADI 600
601 DMISKLYTKQKYDPPLARNQPPIAGKILWARQLFHRIQQPMQLFQQHPAV 650
651 LSTAEAKPIIRSYNRMAKVLLEFEVLFHRAWLRQIEEIHVGLEASLLVKA 700
701 PGTGELFVNFDPQILILFRETECMAQMGLEVSPLATSLFQKRDRYKRNFS 750
751 NMKMMLAEYQRVKSKIPAAIEQLIVPHLAKVDEALQPGLAALTWTSLNIE 800
801 AYLENTFAKIKDLELLLDRVNDLIEFRIDAILEEMSSTPLCQLPQEEPLT 850
851 CEEFLQMTKDLCVNGAQILHFKSSLVEEAVNELVNMLLDVEVLSEEESEK 900
901 ISNENSVNYKNESSAKREEGNFDTLTSSINARANALLLTTVTRKKKETEM 950
951 LGEEARELLSHFNHQNMDALLKVTRNTLEAIRKRIHSSHTINFRDSNSAS 1000
1001 NMKQNSLPIFRASVTLAIPNIVMAPALEDVQQTLNKAVECIISVPKGVRQ 1050
1051 WSSELLSKKKIQERKMAALQSNEDSDSDVEMGENELQDTLEIASVNLPIP 1100
1101 VQTKNYYKNVSENKEIVKLVSVLSTIINSTKKEVITSMDCFKRYNHIWQK 1150
1151 GKEEAIKTFITQSPLLSEFESQILYFQNLEQEINAEPEYVCVGSIALYTA 1200
1201 DLKFALTAETKAWMVVIGRHCNKKYRSEMENIFMLIEEFNKKLNRPIKDL 1250
1251 DDIRIAMAALKEIREEQISIDFQVGPIEESYALLNRYGLLIAREEIDKVD 1300
1301 TLHYAWEKLLARAGEVQNKLVSLQPSFKKELISAVEVFLQDCHQFYLDYD 1350
1351 LNGPMASGLKPQEASDRLIMFQNQFDNIYRKYITYTGGEELFGLPATQYP 1400
1401 QLLEIKKQLNLLQKIYTLYNSVIETVNSYYDILWSEVNIEKINNELLEFQ 1450
1451 NRCRKLPRALKDWQAFLDLKKIIDDFSECCPLLEYMASKAMMERHWERIT 1500
1501 TLTGHSLDVGNESFKLRNIMEAPLLKYKEEIEDICISAVKERDIEQKLKQ 1550
1551 VINEWDNKTFTFGSFKTRGELLLRGDSTSEIIANMEDSLMLLGSLLSNRY 1600
1601 NMPFKAQIQKWVQYLSNSTDIIESWMTVQNLWIYLEAVFVGGDIAKQLPK 1650
1651 EAKRFSNIDKSWVKIMTRAHEVPSVVQCCVGDETLGQLLPHLLDQLEICQ 1700
1701 KSLTGYLEKKRLCFPRFFFVSDPALLEILGQASDSHTIQAHLLNVFDNIK 1750
1751 SVKFHEKIYDRILSISSQEGETIELDKPVMAEGNVEVWLNSLLEESQSSL 1800
1801 HLVIRQAAANIQETGFQLTEFLSSFPAQVGLLGIQMIWTRDSEEALRNAK 1850
1851 FDKKIMQKTNQAFLELLNTLIDVTTRDLSSTERVKYETLITIHVHQRDIF 1900
1901 DDLCHMHIKSPMDFEWLKQCRFYFNEDSDKMMIHITDVAFIYQNEFLGCT 1950
1951 DRLVITPLTDRCYITLAQALGMSMGGAPAGPAGTGKTETTKDMGRCLGKY 2000
2001 VVVFNCSDQMDFRGLGRIFKGLAQSGSWGCFDEFNRIDLPVLSVAAQQIS 2050
2051 IILTCKKEHKKSFIFTDGDNVTMNPEFGLFLTMNPGYAGRQELPENLKIN 2100
2101 FRSVAMMVPDRQIIIRVKLASCGFIDNVVLARKFFTLYKLCEEQLSKQVH 2150
2151 YDFGLRNILSVLRTLGAAKRANPMDTESTIVMRVLRDMNLSKLIDEDEPL 2200
2201 FLSLIEDLFPNILLDKAGYPELEAAISRQVEEAGLINHPPWKLKVIQLFE 2250
2251 TQRVRHGMMTLGPSGAGKTTCIHTLMRAMTDCGKPHREMRMNPKAITAPQ 2300
2301 MFGRLDVATNDWTDGIFSTLWRKTLRAKKGEHIWIILDGPVDAIWIENLN 2350
2351 SVLDDNKTLTLANGDRIPMAPNCKIIFEPHNIDNASPATVSRNGMVFMSS 2400
2401 SILDWSPILEGFLKKRSPQEAEILRQLYTESFPDLYRFCIQNLEYKMEVL 2450
2451 EAFVITQSINMLQGLIPLKEQGGEVSQAHLGRLFVFALLWSAGAALELDG 2500
2501 RRRLELWLRSRPTGTLELPPPAGPGDTAFDYYVAPDGTWTHWNTRTQEYL 2550
2551 YPSDTTPEYGSILVPNVDNVRTDFLIQTIAKQGKAVLLIGEQGTAKTVII 2600
2601 KGFMSKYDPECHMIKSLNFSSATTPLMFQRTIESYVDKRMGTTYGPPAGK 2650
2651 KMTVFIDDVNMPIINEWGDQVTNEIVRQLMEQNGFYNLEKPGEFTSIVDI 2700
2701 QFLAAMIHPGGGRNDIPQRLKRQFSIFNCTLPSEASVDKIFGVIGVGHYC 2750
2751 TQRGFSEEVRDSVTKLVPLTRRLWQMTKIKMLPTPAKFHYVFNLRDLSRV 2800
2801 WQGMLNTTSEVIKEPNDLLKLWKHECKRVIADRFTVSSDVTWFDKALVSL 2850
2851 VEEEFGEEKKLLVDCGIDTYFVDFLRDAPEAAGETSEEADAETPKIYEPI 2900
2901 ESFSHLKERLNMFLQLYNESIRGAGMDMVFFADAMVHLVKISRVIRTPQG 2950
2951 NALLVGVGGSGKQSLTRLASFIAGYVSFQITLTRSYNTSNLMEDLKVLYR 3000
3001 TAGQQGKGITFIFTDNEIKDESFLEYMNNVLSSGEVSNLFARDEIDEINS 3050
3051 DLASVMKKEFPRCLPTNENLHDYFMSRVRQNLHIVLCFSPVGEKFRNRAL 3100
3101 KFPALISGCTIDWFSRWPKDALVAVSEHFLTSYDIDCSLEIKKEVVQCMG 3150
3151 SFQDGVAEKCVDYFQRFRRSTHVTPKSYLSFIQGYKFIYGEKHVEVRTLA 3200
3201 NRMNTGLEKLKEASESVAALSKELEAKEKELQVANDKADMVLKEVTMKAQ 3250
3251 AAEKVKAEVQKVKDRAQAIVDSISKDKAIAEEKLEAAKPALEEAEAALQT 3300
3301 IRPSDIATVRTLGRPPHLIMRIMDCVLLLFQRKVSAVKIDLEKSCTMPSW 3350
3351 QESLKLMTAGNFLQNLQQFPKDTINEEVIEFLSPYFEMPDYNIETAKRVC 3400
3401 GNVAGLCSWTKAMASFFSINKEVLPLKANLVVQENRHLLAMQDLQKAQAE 3450
3451 LDDKQAELDVVQAEYEQAMTEKQTLLEDAERCRHKMQTASTLISGLAGEK 3500
3501 ERWTEQSQEFAAQTKRLVGDVLLATAFLSYSGPFNQEFRDLLLNDWRKEM 3550
3551 KARKIPFGKNLNLSEMLIDAPTISEWNLQGLPNDDLSIQNGIIVTKASRY 3600
3601 PLLIDPQTQGKIWIKNKESRNELQITSLNHKYFRNHLEDSLSLGRPLLIE 3650
3651 DVGEELDPALDNVLERNFIKTGSTFKVKVGDKEVDVLDGFRLYITTKLPN 3700
3701 PAYTPEISARTSIIDFTVTMKGLEDQLLGRVILTEKQELEKERTHLMEDV 3750
3751 TANKRRMKELEDNLLYRLTSTQGSLVEDESLIVVLSNTKRTAEEVTQKLE 3800
3801 ISAETEVQINSAREEYRPVATRGSILYFLITEMRLVNEMYQTSLRQFLGL 3850
3851 FDLSLARSVKSPITSKRIANIIEHMTYEVYKYAARGLYEEHKFLFTLLLT 3900
3901 LKIDIQRNRVKHEEFLTLIKGGASLDLKACPPKPSKWILDITWLNLVELS 3950
3951 KLRQFSDVLDQISRNEKMWKIWFDKENPEEEPLPNAYDKSLDCFRRLLLI 4000
4001 RSWCPDRTIAQARKYIVDSMGEKYAEGVILDLEKTWEESDPRTPLICLLS 4050
4051 MGSDPTDSIIALGKRLKIETRYVSMGQGQEVHARKLLQQTMANGGWALLQ 4100
4101 NCHLGLDFMDELMDIIIETELVHDAFRLWMTTEAHKQFPITLLQMSIKFA 4150
4151 NDPPQGLRAGLKRTYSGVSQDLLDVSSGSQWKPMLYAVAFLHSTVQERRK 4200
4201 FGALGWNIPYEFNQADFNATVQFIQNHLDDMDVKKGVSWTTIRYMIGEIQ 4250
4251 YGGRVTDDYDKRLLNTFAKVWFSENMFGPDFSFYQGYNIPKCSTVDNYLQ 4300
4301 YIQSLPAYDSPEVFGLHPNADITYQSKLAKDVLDTILGIQPKDTSGGGDE 4350
4351 TREAVVARLADDMLEKLPPDYVPFEVKERLQKMGPFQPMNIFLRQEIDRM 4400
4401 QRVLSLVRSTLTELKLAIDGTIIMSENLRDALDCMFDARIPAWWKKASWI 4450
4451 SSTLGFWFTELIERNSQFTSWVFNGRPHCFWMTGFFNPQGFLTAMRQEIT 4500
4501 RANKGWALDNMVLCNEVTKWMKDDISAPPTEGVYVYGLYLEGAGWDKRNM 4550
4551 KLIESKPKVLFELMPVIRIYAENNTLRDPRFYSCPIYKKPVRTDLNYIAA 4600
4601 VDLRTAQTPEHWVLRGVALLCDVK 4624
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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