 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NYQ7 from www.uniprot.org...
The NucPred score for your sequence is 0.64 (see score help below)
1 MMARRPPWRGLGGRSTPILLLLLLSLFPLSQEELGGGGHQGWDPGLAATT 50
51 GPRAHIGGGALALCPESSGVREDGGPGLGVREPIFVGLRGRRQSARNSRG 100
101 PPEQPNEELGIEHGVQPLGSRERETGQGPGSVLYWRPEVSSCGRTGPLQR 150
151 GSLSPGALSSGVPGSGNSSPLPSDFLIRHHGPKPVSSQRNAGTGSRKRVG 200
201 TARCCGELWATGSKGQGERATTSGAERTAPRRNCLPGASGSGPELDSAPR 250
251 TARTAPASGSAPRESRTAPEPAPKRMRSRGLFRCRFLPQRPGPRPPGLPA 300
301 RPEARKVTSANRARFRRAANRHPQFPQYNYQTLVPENEAAGTAVLRVVAQ 350
351 DPDAGEAGRLVYSLAALMNSRSLELFSIDPQSGLIRTAAALDRESMERHY 400
401 LRVTAQDHGSPRLSATTMVAVTVADRNDHSPVFEQAQYRETLRENVEEGY 450
451 PILQLRATDGDAPPNANLRYRFVGPPAARAAAAAAFEIDPRSGLISTSGR 500
501 VDREHMESYELVVEASDQGQEPGPRSATVRVHITVLDENDNAPQFSEKRY 550
551 VAQVREDVRPHTVVLRVTATDRDKDANGLVHYNIISGNSRGHFAIDSLTG 600
601 EIQVVAPLDFEAEREYALRIRAQDAGRPPLSNNTGLASIQVVDINDHIPI 650
651 FVSTPFQVSVLENAPLGHSVIHIQAVDADHGENARLEYSLTGVAPDTPFV 700
701 INSATGWVSVSGPLDRESVEHYFFGVEARDHGSPPLSASASVTVTVLDVN 750
751 DNRPEFTMKEYHLRLNEDAAVGTSVVSVTAVDRDANSAISYQITGGNTRN 800
801 RFAISTQGGVGLVTLALPLDYKQERYFKLVLTASDRALHDHCYVHINITD 850
851 ANTHRPVFQSAHYSVSVNEDRPMGSTIVVISASDDDVGENARITYLLEDN 900
901 LPQFRIDADSGAITLQAPLDYEDQVTYTLAITARDNGIPQKADTTYVEVM 950
951 VNDVNDNAPQFVASHYTGLVSEDAPPFTSVLQISATDRDAHANGRVQYTF 1000
1001 QNGEDGDGDFTIEPTSGIVRTVRRLDREAVSVYELTAYAVDRGVPPLRTP 1050
1051 VSIQVMVQDVNDNAPVFPAEEFEVRVKENSIVGSVVAQITAVDPDEGPNA 1100
1101 HIMYQIVEGNIPELFQMDIFSGELTALIDLDYEARQEYVIVVQATSAPLV 1150
1151 SRATVHVRLVDQNDNSPVLNNFQILFNNYVSNRSDTFPSGIIGRIPAYDP 1200
1201 DVSDHLFYSFERGNELQLLVVNQTSGELRLSRKLDNNRPLVASMLVTVTD 1250
1251 GLHSVTAQCVLRVVIITEELLANSLTVRLENMWQERFLSPLLGRFLEGVA 1300
1301 AVLATPAEDVFIFNIQNDTDVGGTVLNVSFSALAPRGAGAGAAGPWFSSE 1350
1351 ELQEQLYVRRAALAARSLLDVLPFDDNVCLREPCENYMKCVSVLRFDSSA 1400
1401 PFLASASTLFRPIQPIAGLRCRCPPGFTGDFCETELDLCYSNPCRNGGAC 1450
1451 ARREGGYTCVCRPRFTGEDCELDTEAGRCVPGVCRNGGTCTDAPNGGFRC 1500
1501 QCPAGGAFEGPRCEVAARSFPPSSFVMFRGLRQRFHLTLSLSFATVQQSG 1550
1551 LLFYNGRLNEKHDFLALELVAGQVRLTYSTGESNTVVSPTVPGGLSDGQW 1600
1601 HTVHLRYYNKPRTDALGGAQGPSKDKVAVLSVDDCDVAVALQFGAEIGNY 1650
1651 SCAAAGVQTSSKKSLDLTGPLLLGGVPNLPENFPVSHKDFIGCMRDLHID 1700
1701 GRRVDMAAFVANNGTMAGCQAKLHFCDSGPCKNSGFCSERWGSFSCDCPV 1750
1751 GFGGKDCQLTMAHPHHFRGNGTLSWNFGSDMAVSVPWYLGLAFRTRATQG 1800
1801 VLMQVQAGPHSTLLCQLDRGLLSVTVTRGSGRASHLLLDQVTVSDGRWHD 1850
1851 LRLELQEEPGGRRGHHVLMVSLDFSLFQDTMAVGSELQGLKVKQLHVGGL 1900
1901 PPGSAEEAPQGLVGCIQGVWLGSTPSGSPALLPPSHRVNAEPGCVVTNAC 1950
1951 ASGPCPPHADCRDLWQTFSCTCQPGYYGPGCVDACLLNPCQNQGSCRHLP 2000
2001 GAPHGYTCDCVGGYFGHHCEHRMDQQCPRGWWGSPTCGPCNCDVHKGFDP 2050
2051 NCNKTNGQCHCKEFHYRPRGSDSCLPCDCYPVGSTSRSCAPHSGQCPCRP 2100
2101 GALGRQCNSCDSPFAEVTASGCRVLYDACPKSLRSGVWWPQTKFGVLATV 2150
2151 PCPRGALGAAVRLCDEAQGWLEPDLFNCTSPAFRELSLLLDGLELNKTAL 2200
2201 DTMEAKKLAQRLREVTGHTDHYFSQDVRVTARLLAHLLAFESHQQGFGLT 2250
2251 ATQDAHFNENLLWAGSALLAPETGDLWAALGQRAPGGSPGSAGLVRHLEE 2300
2301 YAATLARNMELTYLNPMGLVTPNIMLSIDRMEHPSSPRGARRYPRYHSNL 2350
2351 FRGQDAWDPHTHVLLPSQSPRPSPSEVLPTSSSIENSTTSSVVPPPAPPE 2400
2401 PEPGISIIILLVYRTLGGLLPAQFQAERRGARLPQNPVMNSPVVSVAVFH 2450
2451 GRNFLRGILESPISLEFRLLQTANRSKAICVQWDPPGLAEQHGVWTARDC 2500
2501 ELVHRNGSHARCRCSRTGTFGVLMDASPRERLEGDLELLAVFTHVVVAVS 2550
2551 VAALVLTAAILLSLRSLKSNVRGIHANVAAALGVAELLFLLGIHRTHNQL 2600
2601 VCTAVAILLHYFFLSTFAWLFVQGLHLYRMQVEPRNVDRGAMRFYHALGW 2650
2651 GVPAVLLGLAVGLDPEGYGNPDFCWISVHEPLIWSFAGPVVLVIVMNGTM 2700
2701 FLLAARTSCSTGQREAKKTSALTLRSSFLLLLLVSASWLFGLLAVNHSIL 2750
2751 AFHYLHAGLCGLQGLAVLLLFCVLNADARAAWMPACLGRKAAPEEARPAP 2800
2801 GLGPGAYNNTALFEESGLIRITLGASTVSSVSSARSGRTQDQDSQRGRSY 2850
2851 LRDNVLVRHGSAADHTDHSLQAHAGPTDLDVAMFHRDAGADSDSDSDLSL 2900
2901 EEERSLSIPSSESEDNGRTRGRFQRPLCRAAQSERLLTHPKDVDGNDLLS 2950
2951 YWPALGECEAAPCALQTWGSERRLGLDTSKDAANNNQPDPALTSGDETSL 3000
3001 GRAQRQRKGILKNRLQYPLVPQTRGAPELSWCRAATLGHRAVPAASYGRI 3050
3051 YAGGGTGSLSQPASRYSSREQLDLLLRRQLSRERLEEAPAPVLRPLSRPG 3100
3101 SQECMDAAPGRLEPKDRGSTLPRRQPPRDYPGAMAGRFGSRDALDLGAPR 3150
3151 EWLSTLPPPRRTRDLDPQPPPLPLSPQRQLSRDPLLPSRPLDSLSRSSNS 3200
3201 REQLDQVPSRHPSREALGPLPQLLRAREDSVSGPSHGPSTEQLDILSSIL 3250
3251 ASFNSSALSSVQSSSTPLGPHTTATPSATASVLGPSTPRSATSHSISELS 3300
3301 PDSEVPRSEGHS 3312
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.) |
Go back to the NucPred Home Page.