 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q2QI47 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MHYLALSPGFLCYTIKTLILAYLASVLVLAASQGVFPRLENVGAFRKVST 50
51 VPTHATCGFPGPSTFCRSPVAAEHVQLCTERLCIQDCPYRSASPLYTALL 100
101 EGLRSCIPADDGDLHPYSRSSSVSFMFGSHQNCPSLRAPRLAAELTLAVW 150
151 LKLEQGGTMCVIEKTVDGQIVFKVTISEKETMFYYRTVNGLQPPIKVMTP 200
201 GRILMKKWIHLTVQVHQTAISFFVDGLEENSTAFDTRTLHDSVTDSVSSV 250
251 IQVGQSLNGSEQFVGRMQDFRLYNVSLTNREILEVFSGDFPHLHIQPHCR 300
301 CPGSHPRVHPSVQQYCIPNGAGDTPEHRMSRLNPEAHPLSFINDDDVATS 350
351 WISHVFTNITQLYEGVAISIDLENGQYQVLKVITQFSSLQPVAIRIQRKK 400
401 ADSSPWEDWQYFARNCSVWGMKDNEDLENPNSVNCLQLPDFIPFSHGNVT 450
451 FDLLTSGQKHRPGYNDFYNSSVLQEFMRATQIRLHFHGQYYPAGHTVDWR 500
501 HQYYAVDEIIVSGRCQCHGHAETCDRTRRPYRCLCSPHSFTEGPQCDRCS 550
551 PLYNDKPFRSGDNVNAFNCKPCQCHGHASSCHYDASVDPFPLEHNRGGGG 600
601 VCDDCQHHTTGRHCESCQDYFYRPVGADPAAPDACKLCDCNRAGTRNGSL 650
651 HCDPIGGQCDCKRRVSGRQCLQCQDGFYDLQALDPDGCRPCNCNPSGTVD 700
701 GDITCHQNSGQCSCKANVIGLRCNRCNFGFKFLQSFNGDGCEPCQCNLHG 750
751 SVNQLCDPLSGQCACKKEAKGLKCDSCRENFYGLPWSACEVCDCSKAGSQ 800
801 PGTVCDTETGQCVCKPNVGGRQCSQCKAGYFNLYQNDSHLCLTCNCEKMG 850
851 TVNGSLRCDKSTGQCPCKLGVTGLRCHQCKPHRFNLTMDNPQGCQACDCD 900
901 SLGTLPGSMCDPISGQCLCLPHRQGRRCEQCQPGFYSSPSNATGCLPCLC 950
951 HTAGAVSHICNSVTGQCSCHDPSTTGRSCHQCQESYFRFDPLTGRCRPCH 1000
1001 CHVAGASNGTCDAVTGQCFCKEFVTGSKCDTCVPGASHLDVNNLLGCSKT 1050
1051 PSQQPPPRGWVQSSSTINVSWSPPECPNAHWLTYTLFRNGSEIYTTEDEH 1100
1101 PYYTQYFLDTSLSPHTAYSYYIETSNVHSSTRSIPVIYETKPEVSEGHLN 1150
1151 LTHIIPVGSDSITLTWTGLSNSSDPVAKYVLSCTPVDSTEPCVSYEGPET 1200
1201 SATIWNLVPFTQYCFSVQGCTNESCFYSLPIIVTTAQAPPQTQGPPTVWK 1250
1251 ISPTELRIEWSPPVDSNGIIISYELYMRRWLSTEESLVFESHGLVSSHSA 1300
1301 LQSVNPSKNLLQQPQASTFISGLEPHTEYAFRVLAVNMAGRVSSAWASER 1350
1351 TGESVPVFMAPPSVSPLSPHSLSVSWEKPAENFTRGEIIGYKISMVSEHF 1400
1401 PLHDVPVMCSKMVHFAKSQDQSYIVRGLEPYRTYSFTVSLCDSVGCVTSA 1450
1451 LGSGQTLAAAPAQLRPPMVTGVNSTTVHIRWLPPAGVNGPPPLYHLERKK 1500
1501 SSLPAATAAVTKGTRFVGHGYCRFPRTAHADFIGIKASFRTRVPEGLILL 1550
1551 ALSPGDQEEYFTLQLKNGRPYFLYNSQGTLVEVTPTDDPSQGYRDGEWHE 1600
1601 IIAVRHQAFGQITLDGQYTGSSSSLNGSSVTGGYTGLFVGGVPQGHSVLQ 1650
1651 KRLEIIQRGFVGCLKDVFIMKGYSPSGTWLPLDWQSSEEQVNVHPSWEGC 1700
1701 PTNLEEGVQFLGAGFLELPSDTFHAAKDFEISLKFQTDQLNGLLLFIHNT 1750
1751 EGPDFLAVELKRGLLSFKFNSSLVFTRVDLRLGLADCDGKWNTVSIKKEG 1800
1801 SVVSVRVNALKKSTSQAGGQPLLVNSPVYLGGIPRELQDAYRHLTLEPGF 1850
1851 RGCVKEVAFARGVVVNLASVSSRAVRVNQDGCLSSDSTVNCGGNDSILVY 1900
1901 RGSQQSVYESGLQPFTEYLYRVTASHEGGAVSSDWSRGRTLGTAPQSVPT 1950
1951 PSRAQSINGSSVEVAWNEPAVVKGVLEKYVLKAYSEDSSQPRVPSASTEL 2000
2001 HDTSTHSGVLIGLHPFHSYTVTLTACSRAGCTESSQALSISTPQEAPQEV 2050
2051 QAPVAVALPNSLSFFWSLPRQANGIITQYSLYVDGRLVYTGKGQNYTVTD 2100
2101 LRVFAAYEIIVGACTQAGCTNSSQVILHTAQLPPEQVDPPGLTVLDSRTI 2150
2151 HVRWKQPRQLNGILERYILYILNPIHNSTMWSVVYNSTEKLQAHVLHHLS 2200
2201 PGGLYLIRLRVCTGGGCTTSEPSQALMEETIPEGVPAPRAHSYSPDSFNI 2250
2251 SWTEPEYPNGVITTYELYLDDTLIHNSSGLSCHAYGFDPGSLHTFQVQAC 2300
2301 TAKGCALGPLVGNRTLEAPPEGVVNVLVKPEGSREAHVRWDAPAHPNGRL 2350
2351 TYSVHFTGSFYADQAGDNYTLLSGTKTIRGIEGSRLWVLVDGLVPCSHYM 2400
2401 VQVNASNSRGSVLSDPVSVEMPPGAPDGLLSPRLAAAAPTSLQVVWSTPA 2450
2451 RNNAPGSPRYQLQMRPGPSTHGRLELFPIPSASLSYEVTGLQPFTVYEFR 2500
2501 LVATNGFGTAYSAWTPLMTTEDKPGPIDAPILINVKARMLSVIWRQPAKC 2550
2551 NGAITHYNIYLHGRLYLTVSGRVTNYTVVPLHPYKAYHFQVEACTSQGCS 2600
2601 KSPSSETVWTLPGNPEGIPSPQLFPYTPTSIIVTWQPSAHLDLLVENVTI 2650
2651 ERRVKGKKEVRNLVTLARSQAMKFIDNDPALRPWTRYEYRVLGSTLDGGT 2700
2701 NSSAWVEVTTRPCRPSGVQPPTVRVLAPDTVEVSWKAPLMQNGDILSYEI 2750
2751 RMPEPLIKMTNMSSIMLSHLVKHLIPFTNYSVTIVACSGGNGYLAGCTES 2800
2801 PPTLATTHPAPPQELAPLSVILLSESDVGISWQPPSKPNGPNLRYELLRC 2850
2851 KIQQPLASNPPEDLNLWHNIYSGTRWFYKDKGLSRFTTYEYKLFVHNSVG 2900
2901 FTPSREVTVTTLAGSPERGATVTASILNHTAIDVRWKKPTFQDLQGDVEY 2950
2951 YTLFWSSGTSEESLKIFPDVDFHVIGQLSPNVEYQVFLLVFNGVHAINST 3000
3001 VVHVTMWEEEPQGMLPPEVVIINSTAVRVIWTSPSNPNAVVTESSVYVNN 3050
3051 KLYKTGTDAPGSFVLEDLSPFTIYDIQVEVCTKDACVKSNGTQVSTAEDT 3100
3101 PSDISIPVIRGITSRSLQIDWTTPANPNGIILGYDVLRKTWRPCSETQKL 3150
3151 TDKPRDELCKAVKCQYPGKVCGHTCYSPGTKVCCDGLLYDPQPGYSCCED 3200
3201 KYIALSPNATGVCCGGRMWEAQPDHQCCSGHYARILPGEICCPDERHNRV 3250
3251 SVGFGDACCGTMPYATSGSQVCCAGRLQDGYRQQCCGGEMVSQDFQCCGG 3300
3301 GEEGMVYSYLPGMLCCGQDYVNMSETICCSASSGESKAHVRKDDPTPVKC 3350
3351 CGTELSPESQRCCDGVGYNPLKYVCSDEISAGMAMKETRVCATICPATMK 3400
3401 ATAHCGRCDFNATTHICTVMRGPLNPTGKKAVEGLCSAAEEIVHSGDVNT 3450
3451 HSFIDRDLKPSTVYEYRISAWNSYGRGFSQSVRASTREDVPEGVKAPRWA 3500
3501 RTGKHEDVIFLQWEEPMQSNGPIIHYILFRDGRERFQGTALSFTDTQGIQ 3550
3551 PLQEYSYQLKACTAAGCAVSCKVVAATTQRSPENVPPPNITAQSSETLHL 3600
3601 SWSVPEKMKDAIKAYQLWLDGKGLIYTDTSDRRQHTVTGLQPYTNYSFTL 3650
3651 AVCTSVGCTSSEPCVGQTLQAAPQGVWVTPRHIIINSTTVELYWNPPERP 3700
3701 NGLISQYQLRRNGSLLLVGGRDNQSFTDSNLEPGSRYIYKLEARTGGGSS 3750
3751 WSEDYLVQMPLWTPEDIHPPCNVTVLGSDSIFVAWPTPGNLLPKIPVEYS 3800
3801 ILLSGGSVTLLVFSVRHRQSAHLKNLSPFTQYEIRIQACQNGGCGVSPGT 3850
3851 YVRTLEAAPVGLMPPLLKALGSSCIEVKWMPPTRPNGIITSYVVHRRPAD 3900
3901 TEEESLLFVWSEGALEFTDDTGTLRPFTLYEYRVRAWNSQGAVDSPWSTI 3950
3951 QTLEAPPRGLPAPRVQATSAHSAMLNWTEPEAPNGLISQYHVIYQERPDA 4000
4001 AAPGSSTVHAFTVTGTSRQAHLFGLEPFTTYHIGVVAVNSAGKVSSPWTL 4050
4051 IKTLESAPSGLMNFTVEQREKGRALLLQWSEPVKTNGVIKAYNIFNDGVL 4100
4101 EYSGLGRQFLFRRLAPFTLYTLILEACTTAGCAHSVPQPLWTEEAPPDTQ 4150
4151 MAPTIQSVGPTNVRLHWSQPASPNGKIIHYEVIRRRSEEEDWGNTTWQAD 4200
4201 GNTVFTEYNTQGNAWVYNDTGLQPWRQYAYRICAWNSAGHTCSSWNVVRT 4250
4251 LQAPPDGLSPPEISYVSMSPLQLLISWLPPRHSNGVIQGYRLQRDGVLPA 4300
4301 LNFNASTFSYMDSQLLPFSTYSYAILACTGGGCCTSEPTNITTPEVPPSE 4350
4351 VSPPVLWDISAHQMNVSWSPPSIPNGKIVKYLLQCDGEEHLAGQGLSFLL 4400
4401 SNLQPSTQYNISLVACTSGGCTASRTTSAWTKEAPPENMDPPTLHITGPE 4450
4451 SIEITWTPPRNPHGLIRSYELRRDGAIVYVGLETRYHDFTLAPGVEYSYS 4500
4501 VTATNSRGSVLSPLVKGQTSPSAPSGLQPPKLHSGDALELLADWDPPVRT 4550
4551 NGKIINYTLFVREMFEGKTRAMSINTTHSSFGTRSLTVKHLKPFHRYEVR 4600
4601 VQACTALGCTSSEWTSTQTSEVPPLRQPAPHLEVQTATGGFQPIVAVWWA 4650
4651 GPLQPNGKIICFELYRRQVAAWPGTSSSLLIYNGSFRSFMDSELLPFTEY 4700
4701 EYQVWAVNSAGKAASNWTRCRTGPAPPEGLQAPTFHTVSSTRAVVNISVP 4750
4751 SRPNGNISLFRVFSNSSGTHVTLSEGTATQQTLHDLSPFTTYTIGVEACT 4800
4801 CFNCCSRGPTAELRTHPAPPSGLSPPQVQTLGSRMASVHWTPPLLPNGVI 4850
4851 HSYELQLQRACPPDSAPRCPPSHTERKYWGPGHRASLAGLQPNTAYGVQV 4900
4901 VAYNEAGSTASGWTNFSTKKEMPQYQALFSVDSNASMVWVDWSGTFLLNG 4950
4951 HLKEYVVTDGGRRVYSGLDTTLYIPRMVDKIFFFQVTCTTDIGSVKTPLV 5000
5001 QYDAATGSGLVLTTPGEKKGAGTKSTEFYSELWFIMVMAVVGLILLAIFL 5050
5051 SLILQRKIHKEPCIRERPPLVPLQKRMTPLSVYPPGETHVGLADTRLPRS 5100
5101 GTPMSIRSSQSVSVLRIPSQSQLSHAYSQSSLHRSVSQLMDMADKKVVTE 5150
5151 DSLWETIMGHSSGLYVDEEELMNAIKGFSSVTKEHTAFTDTHL 5193
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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