 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9SMH3 from www.uniprot.org...
The NucPred score for your sequence is 0.59 (see score help below)
1 MDRRLEWVKEKACLGLGVEPNLFEAAIANPESRARVTAFLDGTVTSSALL 50
51 FALEEATIYVEEYQEVLAEEQAPEAEDGEGEEHDGQEPGEAGGEGAEGST 100
101 APGDSGDGQPEDAPAAAAEANGANPEDEAAAPADGAADGAAGEGGEEGDG 150
151 AEGDEPPAPPAPKYVRRVISVPKVVSKLNVALGSLPEELSVFPVFYFILN 200
201 RSGHVAAEELDSAVEFGLLSEGPSLRILEQMLSSVFVPILVQMSGGDVAS 250
251 GGVLMQSMTDNSHRELLGNMQKFHSQVTQALQQLTGDVTLQLPDFPLEDM 300
301 DRAAADTDLVMQLEQYMAEWSQVLASVLQRESQKHPTGKGPLAEIEFWRE 350
351 RNAVLSSLYEQLNLPQVKRMILVVEKGSDDRNLMAGFKSQLGELTKLATE 400
401 ARDNVKFLTTLERHFKNIATGPLGGILDTLPPMMNALRMVWIISRHYSDD 450
451 QRMGSLFQRIAREIGDRVEAAVDLRHIFRMTSADAVELLKVCKSVLEHWL 500
501 QTYMAMREKIELSGRDARWEFPKQLLFARTNYMAEICTDLIEMVEIVDDF 550
551 FRFLGPELKTVTGDTAGIDRVVHRVVAMYEPIESISFNVFDYGNQHEWKA 600
601 AKQQFYADNEDIKEATRELIDTSFRKLRSAEGACELLQSFKSIKSKGAIQ 650
651 KQVMNKFNDILEQFAREIEQTADIFERNKDAPPVTKNQPPVAGAIKWVRS 700
701 LLERLKRTMAKLLSTEEEIIRTTELGQAVESKFKSFARSVMLTEKKWFSS 750
751 WSDSINGVAMQHLKQTIFRRSAATNRVEVNFHPDLVRLIRETRYLDRMGF 800
801 PIPEIALNVALQEDKFLQWLEGLNSMLFKYYESIDQLTPVERELMERKLE 850
851 ELESCLQPGFTILNWNSLGITEFIGTCDKAIATFQQLVKQVQKNSGIIEQ 900
901 VVYAIAGAQLVTEPEEGAEVMDLQEFYEDIERQRLAALESLVKKYRTISP 950
951 LLGKIEEVVAGTNSGKSPALSSYYSFWERAIFNALNTMVLCAMTKLQDMI 1000
1001 EQRSKHAEGGRKPPLFKVTVSLQSVDVVVQPPMTEVNKALGRLVRSLVES 1050
1051 TKAFVRWMDGTCVETPEQRGATDDDEPIVFTFYWDVAANPQVIKTMLNLN 1100
1101 QSIQRAITSVNKYAESWRRHQALWKTDKNSVLDKFKARDPSAAQFEDKLS 1150
1151 KYAKMATEISAQAKDFDQDFIRVSCHALASSVCDEAQAWVRAIAQTMREL 1200
1201 DAVTESQLRDKIAKYQTALHRPPDTLEELKQVLNTVNTIRGESMVMELRY 1250
1251 ADLEERYRTRLLYATNPEEESQCAHELASASQVRALWTELLNEAEAVDWS 1300
1301 LEETKKKFSETTRSQVSDFAAITAELWEKFRTTGPGLPTVELASGLDELH 1350
1351 KYESNLADALRQREQLVLAEKLFGMEITAYPELAQLESEIRKLAQVYGVY 1400
1401 AEHAEAVRQYGGQLWSELDVGKMMAGTEAILTKLRKLKSLKLLPVYELVE 1450
1451 KEIQGFYNSLPLMKELKSEALRKRHWTRLMEVTGQEFDMDPKTFTLGNMF 1500
1501 AMQLHKYAEEIGKITNAAVKELTIESEIRKLADVWREQRFELGKYMKGPE 1550
1551 DRGWVLRSTEDILVLLEDMGLNLQSMMASPFVRPFLTEVRAWEQKLSLIG 1600
1601 ECIEVWMHVQRKWMYLESIFVGSDDIRHQLPAEAKRFDNIDRQWQKIMND 1650
1651 TAKNTVVLDACMADGRLDLLKSLSEQLEVCQKSLSEYLDTKRCAFPRFYF 1700
1701 ISDDELLSILGTSDPTSVQEHMLKLFDNCAALVFGRGNKTITGMVSSEKE 1750
1751 GFEFRNVVPIEGAVELWMTNVEAEMRKTLYQITKEGIFFYAKTPRTKWIS 1800
1801 ENLGMVTLVGSQIWWTWETEDVFRRVRDGNKHSMKEFAAKLTGQLSELTS 1850
1851 MVRSDLSNEVRKKVNTLIIIDVHARDIIDTYVRDSIVDAREFAWESQLRF 1900
1901 YWDRQQDDILIRQCTGLFKYGYEYMGLNGRLVITALTDRCYMTLTTALTY 1950
1951 RLGGAPAGPAGTGKTETTKDLAKSMALLCVVFNCGEGLDYKAMGSIFSGL 2000
2001 VQCGAWGCFDEFNRIEAEVLSVVSSQIKNIQEALKNDLTRFQFEGKEISI 2050
2051 DPRTGIFITMNPGYAGRTELPDNLKALFRPVTMVVPDLEQICEIMLFSEG 2100
2101 FDSAKVLAKKMTVLYKLSREQLSKQHHYDFGLRALKSVLVMAGSLKRGAP 2150
2151 DMSEQLVLMRALRDMNLPKFIFDDVPLFLGLINDLFPGMDCPRVRYPQFN 2200
2201 DVVEADLADQGFKVLTEPSEQVDKVVQLYEVMMTRHTTMVVGQTGGGKTV 2250
2251 ILNTLARAQTKLGKKTHLYTINPKAISVAELYGVLDKDTRDWTDGLLSNI 2300
2301 FREMNKPLPAERDEARYLVFDGDVDAVWVENMNSVMDDNKLLTLPNGERI 2350
2351 RLQNHCKLLFEVFDLQYASPATISRCGMVYVDSRNLGYKPYIYTWLNSRA 2400
2401 KQAEVDILRGLFEKYAVPSVDWILEGIDGEELVRRPKQAVPVTNLNMITQ 2450
2451 LCNLLNATITDHPRMSDPQILEAIFIFCTIWSLGAAIVQRPESPDRDRFD 2500
2501 AFVKHIASMGLVDGERVAATQLPARSLYEYCFDTNEGVWKSWRSYLQPYE 2550
2551 PPADGAFAKILVPTVDVVRSTWLLNTVVAAGKPCLFVGESGTAKSVTIAN 2600
2601 YLAHLDSTINIVLNVNFSSRTSSLDVQRAIEDSTEKRTKDTYGPPMGKRL 2650
2651 LMFIDDLNMPRVDTYGTQQPIALLKLFIERKGLYDRGKELSWKNMKDVQV 2700
2701 VGAMGPPGGARNPVDPRFISLFSVFEIQFPSNENLRTIYQAILSRHLAKL 2750
2751 PTDEIRDQLGERLTDVTLELYNFIIDKLPPTPSRFHYIFNLRDLSRIYEG 2800
2801 LLLTVGDVFKTPEQFLRLWRNECLRVLHDRLISTDDKRVMTERLEALVQQ 2850
2851 KFPNLAAHTLASPVLFGDFKNVINELQGEGEVAPRMYDDLGDYNSIKPLF 2900
2901 EDVMTNFYNRKRKPMNLVFFEDALEHLTRIHRTLRLPQGNCLLVGVGGSG 2950
2951 KQSLSKLAAFTAGCEVFEITLTRGYDELAFREDLKRLYAMLGSDNKRVMF 3000
3001 LFTDAHVADEGFLELINNMLTSGMVPALYDGAEKDGLIGSVRAEVEKKGL 3050
3051 LATKESCWSYYVDKCRNNLHVVLAMSPVGETLRSRCRNFPGMVNNTVIDW 3100
3101 FEPWPEQALTSVASVFLAEEALPEALRPQIVEHMVTVHQSVRTFSTRFLE 3150
3151 ELRRYNYVTPKNYLDFINNYKRALATNRRTIEDTVTRLSGGLEKLIQAAV 3200
3201 EVDAMQKELSQAQVVVAQATKECNELLEVISTNTVDVETKAKAAAIKEAQ 3250
3251 LKVDSEQIAIEKAEAEAALEEAIPALEEAAAALQDLSKDHITEIRSYAKP 3300
3301 PEQVQKVCECVVILRNIKDVSWLGAKSMMADGNFLRSLVEFDKDSLTDKQ 3350
3351 VKKVKEYFKDPKAPLTYDSLRAISTAGAGLLKWVLAMVNYNNVARTVEPK 3400
3401 RKKVAESEKNLRIAQKDLASTKLELQSLNDQLGKLRTQFEEKTAEQQDLK 3450
3451 AKADLMERRLIAASKLIAGLGSERERWTRDIADLESRRDRLIGDCLLTSS 3500
3501 FLSYTGAFTATYRHAMVYEMWQDDVKARGVPVTQPFRLEALLTSDVETTG 3550
3551 WASEGLPSDELSIQNGILTVRANRWPLCIDPQMQAVNWIKSREGKMLEGK 3600
3601 VKTFNDSDFLKQLELSIQYGFPFLFENLDEYIDPVIDPVLEKNLVPGDGK 3650
3651 FVIKLGDKEVEWDSNFRLYMTSKLSNPHYGPEISGKTMIINYGVTQQGLT 3700
3701 EQLLNVTLRHERSDLEEAREALIKQMSENKATLQALEDTLLRELSNAQGN 3750
3751 ILDNSELIATLESAKLKAVEIAEKLEASKVTAAEIEETRVRYSPAAKRGA 3800
3801 ILFFVIAGLSAITNMYEYSLASFLVVFNGSLHSSRRDASIEGRLRNIIDT 3850
3851 LTYDVYAYTCLGLFERHKLMFSFQMTCKILEGDTPLDPQLLDFFLKGNLS 3900
3901 LEKAARRKPFDWFPDAGWQDLMRLVELGQKKIGADGRMHALGSLANDVES 3950
3951 DEAAWRTWYDLEAPEEAELPCGYQSFLSDFEKLCLMRCLRMDRVTVGITR 4000
4001 FVIGVMGEKYVQPPVLEYRSIYKQSTETTPIVFVLSPGADPAFDVFKLGE 4050
4051 EMGFRPGAKLKYMALGQGMGPKAQELIETGATRGLWIMLQNCHLLPTWLK 4100
4101 TLEKILEKITKPHADFRLWLTTELTDRFPLGVLQRSLKVVTEPPNGLKLN 4150
4151 MRQSYSKITEEVLADCPHQAFRPLVYVLGFFHAVVQERRKYGKLGWNVPY 4200
4201 DFNETDFRISMALISTYLTKAWDAQDDLIPWGTLRYLIGEAMYGGRVSDS 4250
4251 YDRRILTTYLDEYLGDFLFDTFQPFRFYACKDYEIAIPQTGSRDTYLKAV 4300
4301 EALPLVQSPEAFGLNANADISYYTSATKAIWTDLVDLQPRTGGGGGGVAR 4350
4351 EEFIGGVARDIAAKIPEPFDLPQLRKELGTPSPTQVVLLQELERWNSVLG 4400
4401 VMVSSLRDLQRALSGEIGFSSRLEELASSLYNGKLPAMWARLNPATEKAL 4450
4451 GAWMLWFGRRYRQYKDWTEHGEPKVIWLSGLHIPETYIAALVQAACRDKG 4500
4501 WPLDKSTLYTKVTKFTDPYQVSERPKYGCYMSGLYLEGAAWDLEASQLRK 4550
4551 QDPKVLVNELPILQVIPIEANKLKLANTFRAPVYVTQARRNAMGVGLVFD 4600
4601 ADLASAEHSSHWVLQGVALVLNIDQ 4625
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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