 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P38811 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MSLTEQIEQFASRFRDDDATLQSRYSTLSELYDIMELLNSPEDYHFFLQA 50
51 VIPLLLNQLKEVPISYDAHSPEQKLRNSMLDIFNRCLMNQTFQPYAMEVL 100
101 EFLLSVLPKENEENGILCMKVLTTLFKSFKSILQDKLDSFIRIIIQIYKN 150
151 TPNLINQTFYEAGKAEQGDLDSPKEPQADELLDEFSKNDEEKDFPSKQSS 200
201 TEPRFENSTSSNGLRSSMFSFKILSECPITMVTLYSSYKQLTSTSLPEFT 250
251 PLIMNLLNIQIKQQQEAREQAESRGEHFTSISTEIINRPAYCDFILAQIK 300
301 ATSFLAYVFIRGYAPEFLQDYVNFVPDLIIRLLQDCPSELSSARKELLHA 350
351 TRHILSTNYKKLFLPKLDYLFDERILIGNGFTMHETLRPLAYSTVADFIH 400
401 NIRSELQLSEIEKTIKIYTGYLLDESLALTVQIMSAKLLLNLVERILKLG 450
451 KENPQEAPRAKKLLMIIIDSYMNRFKTLNRQYDTIMKYYGRYETHKKEKA 500
501 EKLKNSIQDNDKESEEFMRKVLEPSDDDHLMPQPKKEDINDSPDVEMTES 550
551 DKVVKNDVEMFDIKNYAPILLLPTPTNDPIKDAFYLYRTLMSFLKTIIHD 600
601 LKVFNPPPNEYTVANPKLWASVSRVFSYEEVIVFKDLFHECIIGLKFFKD 650
651 HNEKLSPETTKKHFDISMPSLPVSATKDARELMDYLAFMFMQMDNATFNE 700
701 IIEQELPFVYERMLEDSGLLHVAQSFLTSEITSPNFAGILLRFLKGKLKD 750
751 LGNVDFNTSNVLIRLFKLSFMSVNLFPNINEVVLLPHLNDLILNSLKYST 800
801 TAEEPLVYFYLIRTLFRSIGGGRFENLYRSIKPILQVLLQSLNQMILTAR 850
851 LPHERELYVELCITVPVRLSVLAPYLPFLMKPLVFALQQYPDLVSQGLRT 900
901 LELCIDNLTAEYFDPIIEPVIDDVSKALFNLLQPQPFNHAISHNVVRILG 950
951 KLGGRNRQFLKPPTDLTEKTELDIDAIADFKINGMPEDVPLSVTPGIQSA 1000
1001 LNILQSYKSDIHYRKSAYKYLTCVLLLMTKSSAEFPTNYTELLKTAVNSI 1050
1051 KLERIGIEKNFDLEPTVNKRDYSNQENLFLRLLESVFYATSIKELKDDAM 1100
1101 DLLNNLLDHFCLLQVNTTLLNKRNYNGTFNIDLKNPNFMLDSSLILDAIP 1150
1151 FALSYYIPEVREVGVLAYKRIYEKSCLIYGEELALSHSFIPELAKQFIHL 1200
1201 CYDETYYNKRGGVLGIKVLIDNVKSSSVFLKKYQYNLANGLLFVLKDTQS 1250
1251 EAPSAITDSAEKLLIDLLSITFADVKEEDLGNKVLENTLTDIVCELSNAN 1300
1301 PKVRNACQKSLHTISNLTGIPIVKLMDHSKQFLLSPIFAKPLRALPFTMQ 1350
1351 IGNVDAITFCLSLPNTFLTFNEELFRLLQESIVLADAEDESLSTNIQKTT 1400
1401 EYSTSEQLVQLRIACIKLLAIALKNEEFATAQQGNIRIRILAVFFKTMLK 1450
1451 TSPEIINTTYEALKGSLAENSKLPKELLQNGLKPLLMNLSDHQKLTVPGL 1500
1501 DALSKLLELLIAYFKVEIGRKLLDHLTAWCRVEVLDTLFGQDLAEQMPTK 1550
1551 IIVSIINIFHLLPPQADMFLNDLLLKVMLLERKLRLQLDSPFRTPLARYL 1600
1601 NRFHNPVTEYFKKNMTLRQLVLFMCNIVQRPEAKELAEDFEKELDNFYDF 1650
1651 YISNIPKNQVRVVSFFTNMVDLFNTMVITNGDEWLKKKGNMILKLKDMLN 1700
1701 LTLKTIKENSFYIDHLQLNQSIAKFQALYLRFTELSERDQNPLLLDFIDF 1750
1751 SFSNGIKASYSLKKFIFHNIIASSNKEKQNNFINDATLFVLSDKCLDARI 1800
1801 FVLKNVINSTLIYEVATSGSLKSYLVEDKKPKWLELLHNKIWKNSNAILA 1850
1851 YDVLDHHDLFRFELLQLSAIFIKADPEIIAEIKKDIIKFCWNFIKLEDTL 1900
1901 IKQSAYLVTSYFISKFDFPIKVVTQVFVALLRSSHVEARYLVKQSLDVLT 1950
1951 PVLHERMNAAGTPDTWINWVKRVMVENSSSQNNILYQFLISHPDLFFNSR 2000
2001 DLFISNIIHHMNKITFMSNSNSDSHTLAIDLASLILYWENKTLEITNVNN 2050
2051 TKTDSDGDVVMSDSKSDINPVEADTTAIIVDANNNSPISLHLREACTAFL 2100
2101 IRYVCASNHRAIETELGLRAINILSELISDKHWTNVNVKLVYFEKFLIFQ 2150
2151 DLDSENILYYCMNALDVLYVFFKNKTKEWIMENLPTIQNLLEKCIKSDHH 2200
2201 DVQEALQKVLQVIMKAIKAQGVSVIIEEESPGKTFIQMLTSVITQDLQET 2250
2251 SSVTAGVTLAWVLFMNFPDNIVPLLTPLMKTFSKLCKDHLSISQPKDAMA 2300
2301 LEEARITTKLLEKVLYILSLKVSLLGDSRRPFLSTVALLIDHSMDQNFLR 2350
2351 KIVNMSRSWIFNTEIFPTVKEKAAILTKMLAFEIRGEPSLSKLFYEIVLK 2400
2401 LFDQEHFNNTEITVRMEQPFLVGTRVEDIGIRKRFMTILDNSLERDIKER 2450
2451 LYYVIRDQNWEFIADYPWLNQALQLLYGSFNREKELSLKNIYCLSPPSIL 2500
2501 QEYLPENAEMVTEVNDLELSNFVKGHIASMQGLCRIISSDFIDSLIEIFY 2550
2551 QDPKAIHRAWVTLFPQVYKSIPKNEKYGFVRSIITLLSKPYHTRQISSRT 2600
2601 NVINMLLDSISKIESLELPPHLVKYLAISYNAWYQSINILESIQSNTSID 2650
2651 NTKIIEANEDALLELYVNLQEEDMFYGLWRRRAKYTETNIGLSYEQIGLW 2700
2701 DKAQQLYEVAQVKARSGALPYSQSEYALWEDNWIQCAEKLQHWDVLTELA 2750
2751 KHEGFTDLLLECGWRVADWNSDRDALEQSVKSVMDVPTPRRQMFKTFLAL 2800
2801 QNFAESRKGDQEVRKLCDEGIQLSLIKWVSLPIRYTPAHKWLLHGFQQYM 2850
2851 EFLEATQIYANLHTTTVQNLDSKAQEIKRILQAWRDRLPNTWDDVNMWND 2900
2901 LVTWRQHAFQVINNAYLPLIPALQQSNSNSNINTHAYRGYHEIAWVINRF 2950
2951 AHVARKHNMPDVCISQLARIYTLPNIEIQEAFLKLREQAKCHYQNMNELT 3000
3001 TGLDVISNTNLVYFGTVQKAEFFTLKGMFLSKLRAYEEANQAFATAVQID 3050
3051 LNLAKAWAQWGFFNDRRLSEEPNNISFASNAISCYLQAAGLYKNSKIREL 3100
3101 LCRILWLISIDDASGMLTNAFDSFRGEIPVWYWITFIPQLLTSLSHKEAN 3150
3151 MVRHILIRIAKSYPQALHFQLRTTKEDFAVIQRQTMAVMGDKPDTNDRNG 3200
3201 RRQPWEYLQELNNILKTAYPLLALSLESLVAQINDRFKSTTDEDLFRLIN 3250
3251 VLLIDGTLNYNRLPFPRKNPKLPENTEKNLVKFSTTLLAPYIRPKFNADF 3300
3301 IDNKPDYETYIKRLRYWRRRLENKLDRASKKENLEVLCPHLSNFHHQKFE 3350
3351 DIEIPGQYLLNKDNNVHFIKIARFLPTVDFVRGTHSSYRRLMIRGHDGSV 3400
3401 HSFAVQYPAVRHSRREERMFQLYRLFNKSLSKNVETRRRSIQFNLPIAIP 3450
3451 LSPQVRIMNDSVSFTTLHEIHNEFCKKKGFDPDDIQDFMADKLNAAHDDA 3500
3501 LPAPDMTILKVEIFNSIQTMFVPSNVLKDHFTSLFTQFEDFWLFRKQFAS 3550
3551 QYSSFVFMSYMMMINNRTPHKIHVDKTSGNVFTLEMLPSRFPYERVKPLL 3600
3601 KNHDLSLPPDSPIFHNNEPVPFRLTPNIQSLIGDSALEGIFAVNLFTISR 3650
3651 ALIEPDNELNTYLALFIRDEIISWFSNLHRPIIENPQLREMVQTNVDLII 3700
3701 RKVAQLGHLNSTPTVTTQFILDCIGSAVSPRNLARTDVNFMPWF 3744
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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