 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q39575 from www.uniprot.org...
The NucPred score for your sequence is 0.67 (see score help below)
1 MALDNRHRLIVGKLAEAFGLPENVIEKTLTQDKQAVNSFFTPAGPPSLVF 50
51 VYQVKEDKLKDGSVGPVDNKPTLHRIGPHERIHNSVYFTRLNPKGINEKT 100
101 LEADMGSGELSVLWALENFKAIVSDLYLPIMQEQQQWGKMSTEYLEDFLS 150
151 STAKFGSMLTEAVATVSGGVEPMPDPRYIDQYGDLRPAGITQAAGDDDTL 200
201 QEMEECLTEWCREAELLLNQTNKIKDGEERGPDTELEYWRTRMSNFNSIT 250
251 EHLKTKECKLVLGICSHAKTKAYLRWRGLDVQITDAANESKDNVKYLATL 300
301 EKSMEPMYQGRVTDITESLPALMTNVRMMYTIARFYSTAEHMTRLFTKIT 350
351 NQLVRRCKEQIMENGKIWDQDKVTLIGNMKVSVELANVYRQQYRLAKETL 400
401 AAQPKSKQFDFDEQAIFLKFDLSSKALHKLIDMFTTIHQFSSLEQHTHIE 450
451 GLDTMLKSLNNIIDDVKRKPYDLLDYSRNAFDTDFLEFNVQINDLELQLQ 500
501 GFVNASFEHITSTEHALSLLAQFQAIMQRETLQQDLENKYMVIFQNYAKD 550
551 LDAVQKLYEKNKYEPPVPRNAPPVAGNIMWARQLLRRIEAPMQLAQNKNL 600
601 LAAKESKKNIKTYNKVAKALIEFETLWHQAWIKSIEQCKAGLAAPLLVQH 650
651 PDTGKILVNFDKEIMQLVREAKYMQRFNIRCSSPSQMVLLQEEKFKFYHN 700
701 QLTHLVREYEHVLGRGATIKPLLRPHLDDMERKIAPGFAVLTWTSLNIDG 750
751 YLHRFKQGLARLEELVRKVVDLTENRVDSNLGAISSTLLVELPTDRSFTY 800
801 EGFVEQNRFQKKQAELLAIRNEEVRRAIEDLYTLVRNYPRENTEDVLDEK 850
851 EVSLLVRHYSKNMYNAIMQCTLNSLQAMKRRLGSKTTTGIFFMERPFFDV 900
901 DVELKVPSVCMNPTLEEIQAAINQCAKKVLTISKQLPAWGMDNVATYHEM 950
951 MRGDRRWVKAVLRLTGSVEGIKTQVGEYIRTFDKYDFLWKEDLQAAYDHF 1000
1001 MRSNPTLEAFEAELKKYMAIETEVTMINGVNNIGALSLETHPLKNSLKAE 1050
1051 AVSWKTQFAQNLHKQCSDDLKLDNYIRDTNSKFHRKIEDLEDVRNVMAVL 1100
1101 KEVREKESEIDNLIGPIEEMYGLLMRYEVRVPKEETTMVSDLRYGWKKLK 1150
1151 KVATEVSDNLTRLQVGFKRELIKEVKTFVVDAQMFRKDWEANAMVPGLDP 1200
1201 QEAVDRLRKFQQMFEVRKRKWENYSSGEELFGLPVTQYPELEQTEKEIQM 1250
1251 LDRLYSLYVAVITTIKGYGDYFWVDVVEKIDEMGEQVQQYQNQSKKLPKL 1300
1301 RDWPAYNACRKTIDDFLEMLPLFQALTHKSMRERHWKEVMRVTGHELNLA 1350
1351 EDHFKLQHLLDCNVLRYREDIEDLTGAAVKEEIIEVKLNQLKADWATANL 1400
1401 ALAEYKNRGPVILKPSDTSELMEKLEESQMTLGSMATNRYSAPFRDEVQA 1450
1451 WSIKLSTVSEIIEQWLMVQSMWQYMEAVFSGGDIVKQLPQEAKRFLNIDK 1500
1501 NFMKIVSNALETQNVINTCFGNELMKNMLPHLHEQLEMCQKSLSAYLEQK 1550
1551 RAEFPRFTCVGPHLLEICRWAHDPPSVVPHFQSGLFDSLSNVTFDRIDKT 1600
1601 RMTEMFSQQNEKVEFERPVDAKGNIEVWLQRLVDGMEDTVKQIIKRAVRN 1650
1651 VAEMPLEDFVFGHPAQVSLLGIQFQWTAETQMALSSAKVDKTIMNKNMKK 1700
1701 VDALLRDMVNITVRLDLTKNQRTNLETCITVHMHQKESTEDLVKKKIKDP 1750
1751 TDFEWLKQVRFYWRDDKDTVIISICDVDFEYSFEYLGVKERLVITPLTDI 1800
1801 CYITLSQALGMFLGGAPAGPAGTGKTETTKDLGNTLGKYVVVFNCSDQFD 1850
1851 YTYMGKIYKGLAQSGLWGCFDEFNRINLDVLSVCAQQVYCICRTRERKKS 1900
1901 FQFTDGTTVSLDPRVGFFITMNPGYAGAQELPENLKALFRGVTMMVPNRQ 1950
1951 IIMKVKLAAAGYQENDILSKKFFVLYGLCEQQLSKQAHYDFGLRNILSVL 2000
2001 RTAGASKRQSPDKSEVFLMMRTVRDMNMSKFVAEDVPLFLSLIDDLFPGL 2050
2051 KADATRPDVNKDAEKVVLERGLQVHPTWMNKCIQLYETYLVRHGIMLVGP 2100
2101 SGSGKSAICECLAAALTELGTKHVIWRMNPKAITAPQMFGRRDDTTGDWT 2150
2151 DGIFAVLWRRAAKNKNQNTWIVLDGPVDAIWIENLNTVLDDNKVLTLANG 2200
2201 DRILMSAAMKAMFEPENLNNASPATVSRAGIIYVSDVELGWEPPVKSWLQ 2250
2251 KRDPTEACWARLFSKYIDRMLEFVRISLKPVMYNEQVSIVGTVMTLLNGY 2300
2301 LKSMKEAGTAMNDAKYERVFLYCMTWSLGGLLEMKERPLFDQELRTFAHN 2350
2351 MPPKEEDSDTIFEFLVNTTDAEWLHWRHCVPVWTYPKNEEKPQYAQLVIP 2400
2401 TLDSVRYGALLNLSYNVDKATLLVGGPGTAKTNTINQFISKFNAETTANK 2450
2451 TITFSSLTTPGIFQMSIEGAVEKRQGRTFGPPGGKQMCIFVDDISMPYIN 2500
2501 EWGHQVTNEIVRQLLEQGGMYSLEKPIGDMKFITDVRYVAAMNTPGGGKN 2550
2551 DIPNRLKRQFAIFNVPLPSVAAINGIFGKLVEGRFSRDVFCEEVVYVASK 2600
2601 LVPLTITLWNRIQTKMLPTPAKFHYLFNMRELSKVFQGVILATRDRFNLA 2650
2651 AGDSAVFGGNVASPEGYLLGLWIHECRRVFSDKLISYEDKNWVDKAVFDL 2700
2701 CRDNFSSDLVKQVEEPIYFVDFLREPAVMMRPVEIVTPHPSFYYSVPGGL 2750
2751 PEVRARVEGLQRKFNEESKVMKLELVLFTDCVTHLMRITRLLAWPGLGLL 2800
2801 VGVGGSGKQSLSRLSAYIAGPTFYITKTYNVSNLFEHIKGLYKIAGFKGQ 2850
2851 PVYFIFTDAEVKDEGFLEYINQILMTGEVAGLLTKEDQDMIVNDIRPVMK 2900
2901 HQAPGILDTYDNLYNFFLNRVRDNLHVVLCFSPVGAKFARRAQQFPGLIN 2950
2951 GCTIDWFCPGPKKRLTSVSGKFIDKFTMACPKEVKNQLELLMGHAHVFVT 3000
3001 AACKEYFEKYRRYVYVTPKSYLSFLQGYKELYAKKWSFTKELAYQIEVAC 3050
3051 QKMFEPKADVNKMKAELAVKNQTAVSAKEAEALLKQISESTAIAEKEKQK 3100
3101 VAVIVDAVTKKASEIATVKDDAERDLAAAKPALDAALEALNSIKDGDIKN 3150
3151 LKALKKPPQIITRIFDCVLVLRMLPVTKAEYTDEKGRMVQVGNYPEAQKM 3200
3201 MNQMSFLQDLKDFAKEQINDETVELLEPYFMSEDFTFENAQKGSGNVAGL 3250
3251 CNWAESMAKYHNVAKVVEPKIAKLREAEAELKLATKEKNAAEERMAKVQA 3300
3301 KLDEMQAQFDAAMAHKQALEDDAAATQRKMDSANALIGALAGEEARWTAQ 3350
3351 SKEFDVQIQRLTGDCALASAFVSYLGPFNKEFRELLLNRDFYGDCMKLNV 3400
3401 PVTPHLQITKFLVDDSEVGEWNLQGLPTDELSIQNGIMVTRASRYPVLVD 3450
3451 PQGQGREWIKNREEANQLKTTQLNDKLFRNHLEECLAFGRPLLIENIEEE 3500
3501 LDPLLDPVLERRLVKKGKTWVVPLADKEVDFTETFRLFCTTRLPNPHFTP 3550
3551 ELSAKVTVVDFTVTMAGLEDQLLGKLISKEKKELEDQRQQLLEEVQSYKK 3600
3601 RIKQLEDDLLCRLSNSQGNLLDEHQELIDVLAVTKQTAQDVSEKLANASE 3650
3651 TNKRINEACEEYRPVAHRATLLYFLIAEFSVVNCMYQTSLAQFNQLYELA 3700
3701 IDRSEKANMPSKRIHNIIEYMTYEIYLYVQRGLFERHKIIFALMLTNKVL 3750
3751 TSAGKVKATDLDVFLKGGAALDINSVRKKPKDWIPDLVWLNIIALSAMDA 3800
3801 FRDIPDSVFRNDGLWRQWYDQEAPEMAKVPDYEDRLNKFERMCVVKTFRE 3850
3851 DRTLIAAADYIAEALGQRFVESVPLNMEKRPGRRAMAKCPLICLLSPGPD 3900
3901 PTKLIEDLAKKKKIKTLGVSMGQGQEVIARKHMAAASLEGHWVLLQNTHL 3950
3951 GLGYLTEVETFLVKEENVHEDFRLWITAEPHPQFPIGLLQMGIKITNEAP 4000
4001 VGIKAGLRASYQWVNQDMLDMVSRQEWRQLLFVMCFLHSVVQEPQFGPIG 4050
4051 WNVPYEFNQSDLSACVQFLQNHLSEMDAKKAPQPTWETVRYMISAIQYGS 4100
4101 RITDDFDKLLMDTFAEKYFLQPVLQPSYELFKDTRSSDGFSYRVPDSTDI 4150
4151 ETFGSYIETLPGTESPEIFGLHPNADITFRTLQVQESIVTILDTMPKGAG 4200
4201 SGSGLSREDVVDKICEDLLSKAPPLFDKEETKEKLKKLPGGPTLPLTVHL 4250
4251 RQEIDRLNIVTRLTTTTLKNLALAIAGTIAAERGLIDALDALFNARIPQQ 4300
4301 WLSKSWEASTLGNWFTGLLQRYDQLNKWLNLGRPKAYWMTGFFNPQGFLT 4350
4351 AMKQEVNRKHRDKWALDDVVMSSEVTHRPKDFESLKEGAPEGVYVYGLYL 4400
4401 DLRLDGRENRLMDSDPKKLFNPLPVLHVDGVLAKDKKRSGLYEAPKPYRV 4450
4451 KARKGLNFITTFSVRTEDDKSKWILPGVGILCSID 4485
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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