 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q27171 from www.uniprot.org...
The NucPred score for your sequence is 0.76 (see score help below)
1 MEESETQLNVKVQEQGKLYSANEIENFNQYLSAICLSLLIIDKDQWNVAC 50
51 HEDVNQQNICQFLSDSQIKALIVSKTVENEKFNIQIRSEYEASNNYAHTI 100
101 CFLKRHTFQYDNQLQPQQFSNHVQVINVGYAESQGGANPFTLSHNYVQNC 150
151 FIPIFTQYKGEIDKKRIVDQSSYNDLIKKLNEVNLAFIKCRQNVEVPEII 200
201 LQFDPRIKEAVKQRGGKPTIEDAAQLNKPDIVQSISQTVTRWISDINQIS 250
251 NTKLELTNASIVDEINYWMSMERSLFFIENQLKQPEVDFTIEVLTQAKKM 300
301 NITAQFKEIALKQSLQKCQSCNQFMKEFPINNLLIATNLVEIKDAMIQIF 350
351 QHMKKLSNIQETYTIPRSLQLAESFSRELTNEMIKYFKGFQILHIKYVDF 400
401 KGLIIKTQEIFSQWDEEYKIFKQSIVKKSVHQKDQYGQFEHIKLQKQIQH 450
451 IQRLREMHENLKEVIEQIIQNDQEEQKENVQQFATLQEIQQAYDIFKNVE 500
501 VFDLSRDGEDQFFRALKQYEIAIESVEATITTNLRDSLGSASSAKEMFRI 550
551 LAKFNKLFSRPRIKGAIQEYQSQLLKTVHKDIQSLQNKFKETYQKSQNSR 600
601 LASARDIPLTSGFVIWSKQLQIRLQKYMQKVEQILGPQWAEDTDGKKCKE 650
651 MGETFERILDSGPALEDWKQEINHHNKAVSQNEKLFEVVTRRRGLEIRVN 700
701 YEKKLSQLFKEVRNLSNMKTKVPYSISHIANDAKASYPFALSLQESLHTY 750
751 IQITSQLNAKSAKLVAALRKEVQLQIGQGFNYLWTHKTQLQPYVKKFTDK 800
801 VFELEQAVNGLNERIGQIESLCEAMKTCPVDSLADKLKDIQEVIDSLCFN 850
851 NFSNLHIWIQDIDKQIESILCDRVTVQMKEWLNQFINYQKIQERGLVNQT 900
901 VVHELKLQDQIIYVDPPVEYAKYFWFQEFHKMIGQICSLPRLVANRFDNT 950
951 IQQNTGPWGTQRDLDYSTTINKINQQLIKDAYSQIGQLLEDMEQYVQTWL 1000
1001 NYQSLWELDIKQVEQILQDDIEKWQQMLTDIKQGRATFDNSTTEEHFGAI 1050
1051 IIDYRMVQVKINHKYDAWHKELLNHFGNKFGEQLRVFNKNVTTEKEKLLK 1100
1101 INFQDLTSDIIESITIIQEQDKKFPGWSADIESFKNGQKVLDRQRYQYPG 1150
1151 DWLSFEQVEMQWNQFKQIRSKKLQSQESEMNNIQSKIQQDERYLNQQIQE 1200
1201 IEEQWKTSKPDSGDCSPNEAEQILKSLNEQLISVQEKYEKCSQAKEILKM 1250
1251 DPPTHQQKLNVLLESISDLQDVWQELGKIWKVMQSIKEQLISALQNKKIK 1300
1301 DTCDEAQKQLNGVSTKTRNYDAFEKMKEKVKNYIKMNKLIMDLKDESMKE 1350
1351 RHWRQLLSKLKINESLNQLQMQHLWNANLLNYENLAKDIMTVARGEQVLE 1400
1401 TMISQVKDFWNSFELELVKYQTKCKLIRGWDELFQKLDEDLNNLASMKIS 1450
1451 PFYKTFEAEISQWDDKLQKVKLTMDIWIDVQRRWVYLEGIFFGSSDIKTQ 1500
1501 LQNEYNKFKDIDSQFTNLMKKVAQKPQLMDVQGIPNLAKTLERLSDFLQK 1550
1551 IQKALGDYLETQRQAFARFYFVGDDDLLDIIGNSKDVTNVQRHFPKMYAG 1600
1601 IVQLQSRKDGNDDVVLGMSSKEGEVVPFSKEVKIAEDPRINIWLGKVDNE 1650
1651 MMNSLALDLEKSVLDIQANQQNRMKVIEEHPAQIILLALQVGWCFSVESS 1700
1701 FNNEQQMKQTLQYVLEFLSELAESVLKDHPKQLRQKFEQIITDFVHQRDV 1750
1751 IRLLMNNKINSKNDFGWQYHMRFNWNSKEADPGKRLLIQMGNAQFHYGFE 1800
1801 YLGVAEKLVQTPLTDKCFLTLTQALHLRMGGSPFGPAGTGKTESVKALGA 1850
1851 QLGRFVLVFNCDETFDFNAMGRIFVGLCQVGAWGCFDEFNRLEERMLSAC 1900
1901 SQQILLIQTGLREKQKQIELMGKDVKLSSQMGVFVTMNPGYAGRSNLPEN 1950
1951 LKQLFRQMAMVKPDRELIAQVMLFSQGFRTAEKLAGKIVSLFELCDNQLS 2000
2001 SQPHYDFGLRALKSVLNSAGNMKRQEMIDRKQEPVPQSEIEEFEQTILLR 2050
2051 SVCDTVVPKLIKDDIKLLETLLQGVFPGSCIPEIKEEQLRKELALACQRK 2100
2101 NLQSSKNFIEKVLQLYQIQRLQHGLMLVGPCGCGKSAAWRVLLEAMYKCD 2150
2151 KVKGEFYIVDPKAISKDELYGRLDNTTLEWTDGVFTSILRKIISNQRQES 2200
2201 TRRHWIIFDGDVDPEWAENLNSVLDDNKLLTLPNGERLAIPPNVRMIFEV 2250
2251 ETLKYATLATVSRCGMVWFSEETINDENIFYHFLERLKQDDYDQQKSEDD 2300
2301 NNKQVNSQESELRTKCVKALESIIKFLSQFLQIAQKPEYKHVMEFTRIRV 2350
2351 LESTFALVRRSISNIIEYNENNSEVPLEDDQINDFMVKQFLIAVMWGVAG 2400
2401 SMNLYQRTQYSKEICQLLPHNVILPQFNDSAPSLIDFEVTLPEAQWSQYK 2450
2451 KKVPQIEIDPQRVTDADLIIETVDTLRHKDVLCGWLNEHRPFLLCGPPGS 2500
2501 GKTMTLMSTLKALTDFEMIFINFSSSTMPQLIIKQFDHYCEYKKTTNGVF 2550
2551 LQPKNQKWLVVFCDEINLPDQDKYGTMAIITFLRQLTEQHGFWRSSDRQW 2600
2601 ISLDRIQFVGACNPPTDVGRKPLTPRFLRHCPLILVDFPGPESLKQIYGT 2650
2651 FNKAMLRRTVNLKQYSEQLTNAMVEFYTKSQQHFTADQQAHYIYSPRELT 2700
2701 RWKYALNEALEPLESVEDLVRLWAHEGLRLFQDRLVHEHEKEWCNKLIDQ 2750
2751 VAYNNFNNLKDEALQRPILFSNYLHKVYQSVDREELRKYIQGRLKQFNEE 2800
2801 ELSVPLVVFDDVLDHILRIDRVLKQPLGHLLLVGSSGVGKTTLTRFVSWI 2850
2851 NNLTVFQIKAGRDYQLADFDNDLREVMKRAGAKGEKITFIFDESNVLGPS 2900
2901 FLEKMNALLASGEIPGLFENDEYLALINLLKENSNQNKQFDSSEEQLFKN 2950
2951 FTYQVQRNLHVVFTMNPKNPDFSNRTASSPALFNRCVIDWFGDWTNEALF 3000
3001 QVGKAFTMYIDPPENAFSKKIKDETQRQHILVSTLVYIQNTIIELNNKLQ 3050
3051 KGAKRFNYITPRDYLDFLKHFEKLHNEKKSQLEDQQLHLNVGLDKLKETE 3100
3101 QQVLEMQKSLDQKKVELLTKERQAGEKLQTIIEEKKIAEKKKEDSTRLSS 3150
3151 DAEKKAKEMEVRQSQVNKELNEALPALENAKQCVNSIKKDDLNQIRALGS 3200
3201 PPALVKLTMEAVVCAINSLEKSPEWKDVQKSMANMNFINNVINFNTETMP 3250
3251 PKVKKFILTKYLSAQEWNIDRINFASKAAGPLAMWLDSQLKYADILQKVD 3300
3301 PLRQEVAKLLQESDELNTQKKIYDDEVAAAEAKIHNLQQEYSELISQKES 3350
3351 IKSEMLKVQEKVTRSQALLSDLSGERVRWEEASQNFKSQLATMIGDVLLL 3400
3401 LAIPVLYWVLDHFYRKVVINTWKDYLSGQANIFYRQDLSLIEFLSRPSDR 3450
3451 LNWQLHTLPSDDLCMENAIILYRFQRYPLVIDPSGQALSFISSLYKDKKL 3500
3501 ARTSFTDESFLKTLETCLRFGCPLLVQDVEKVDPILNSVLNNETYKTGGR 3550
3551 VLIRVGNQEIDFSQGFTMFMITRDSTARFTPDLCSRVTFVNFTVTQSSLQ 3600
3601 EQCLNIFLRNESPETEEKRLNLMKLQGEYIVKLRELEDQLLDSLNNSRGS 3650
3651 ILEDEKVIQTLEKLKKEAAVIVQEMKQADTIMNEVMNTTHSYVPLANTTS 3700
3701 KIFFSLTSLANIHYLYQFSLQFFMDTIYNVLNKNEQLQKIPKQDLIKRRI 3750
3751 LIFNEMFKEIYKRMNFSLLQEDKLVFAITLAQVKLGDNTLGQEFLNVFKP 3800
3801 PTVMETTFSNTFLQGKLSIQQLKQLEGITQQNQTFNRLIDNLNKNEDRWL 3850
3851 NFLNDEAPENDIPTQWYNEVQRDDIVKLDWIDSHQLKRQLDDLHILRIFR 3900
3901 ADRFQIIARKLINQILGEGFMDEQTVDMKLVVEKEASNKIPILLCSAPGF 3950
3951 DPSFKVEQLSREMGIKLTSVAIGSAEGFDQAEYEITQSVKSGSWVMLKNV 4000
4001 HLATSWLNDLEKKLFRLTPNANFRIFLTMEFNPKIPTTLIRQSYKLVFEP 4050
4051 PDGIKASLIRTFKTVLSQQRTDRQPVERARLHFLLAWLHAVILERLRFTP 4100
4101 IGWSKTYEFNEADQRCSLDLIDEYVDALGIRQNIDPSKLPWDAFRTILTQ 4150
4151 NLYGGKVDNEYDQKILQSLVEQFFTEQSFNHNHPLFFTLEGKEAITVPEG 4200
4201 RTYLDFMQWIEQLPKTESPEWSGLPSNVERVQRDQLTQKLITKVQNLQQE 4250
4251 GEEEITQIEVQTEKTQKKDNKKSDQVQWLQDLLEKVEKFKAILPNKISPL 4300
4301 ERTADSINDPLFRFLDREITVASKLLKAVRQNIEELIQLAQGKILATNIL 4350
4351 RQLAKDVFNNIVPAQWNKYNVITMPLNDWVGDFKRRIDQFDLLGKTKDFQ 4400
4401 KGQVWFGGLLFPEAYLTATRQYVAQANKWSLEELELQMIPEDQGIDEDSF 4450
4451 VIEGVSMEGGHLDSKTLQVRIVNEISVALKPITLKWCKTSQKGVVGDDEI 4500
4501 VLPVYLNKTRKNLIFSLKVKMGKLNRYTLYQKGLSFILFN 4540
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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