 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9W4E2 from www.uniprot.org...
The NucPred score for your sequence is 0.83 (see score help below)
1 MADIMRPPYSEIKRPDEIVRMTTADNLKFAVLIGLIEVGQVTNREVVNTV 50
51 LHLLVGGEFDMELNFVIQDAQNIKHMLELLDHCPPNLQAEIWSVFIAILR 100
101 KSVRNLQACTDVGLIEHVLVRLQRSETVVADLLIEMLGVLASYSITVKEL 150
151 KLLFGTMKATNGKWPRHSAKLLNVLRQMPHRNGPDVFFSFPGRKGSAMVL 200
201 PPLAKWPYENGFTFTTWFRLDPINSVNIEREKPYLYCFKTSKGVGYTAHF 250
251 VGNCLVLTSMKVKGKGFQHCVKYEFQPRKWYMIAIVYIYNRWTKSEIKCL 300
301 VNGQLASSTEMAWFVSTNDPFDKCYIGATPELDEERVFCGQMSAIYLFSE 350
351 ALTTQQICAMHRLGPGYKSQFRFDNECYLNLPDNHKRVSHFQLLPATLGA 400
401 SALSGGSGSGTGSGTGNDASAAAAAAVAAGQQQQLQLQFQILAAEQEARA 450
451 IDWSDEKLDLNAAFVKIRAVLTARNAVTLAGSSSTGTTAVATAAAAAAAA 500
501 GAGAGTTAAATSAAAAAAATQNENDAAVGQQQHATHHHATAATGSADDPL 550
551 GHLPTGNASSSSSSFEQLRRMSSVSSLNSMVGSADTEEVNQLKAVLYDGK 600
601 LSNAIVFMYNPVATDGQLCLQSSPKGNVSYFVHTPHALMLQDVKAVVTHS 650
651 IHCTLNSIGGIQVLFPLFSQLDMAHEGLGDIKRDPTLCSKLLGFICELVE 700
701 TSQTVQQHMIQNRGFLVISFMLQRSSREHLTLEVLGSFLNLTKYLVTCLS 750
751 ANSDLLLKQLFCFSFLTWQLLDHVLFNPALWIYTPANVQARLYSYLATEF 800
801 LSDTQIYSNVRRVSTVLQTVHTLKYYYWVVNPRAKSGIIPKGLDGPRPAQ 850
851 KDILAIRAYILLFLKQLIMIGNGVKEDELQSILNYLTTMHEDENLHDVLQ 900
901 MLISLMSEHPSSMVPAFDVKHGVRSIFKLLAAESQLIRLQALKLLGFFLS 950
951 RSTHKRKYDVMSPHNLYTLLAERLLLYEESLSLPTYNVLYEIMTEHISQQ 1000
1001 ILYTRHPEPESHYRLENPMILKVVATLIRQSKQTESLIDVKKLFLQDMTL 1050
1051 LCNSNRENRRTVLQMSVWQEWLIAMAYIHPKSSEEQKISDMVYSLFRMLL 1100
1101 HHAIKHEYGGWRVWVDTLAIVHSKVSYEEFKLQFAQMYEHYERQRTDNIT 1150
1151 DPALRQARPISTISGWEREELHQQQNGGSAAAVATNQTAAVKGSVSIASL 1200
1201 EDVPPVVEEEVEELELEEVEIQEGPITEETEQKSVIANISDVYNEQLKTD 1250
1251 ATCNGNLEDVKEEEPVQQQIGDLEKQPEPSTPLGALRETLQLGDDMDVEE 1300
1301 LELATAKDALNAEQHVSRVLQASEAALNDCKMAVDDVLQESSSVLKDEEI 1350
1351 ELAVNEVVQGVLNNEKKTQSQDNKDNKEQPGEQDVNVSLLNSKNLLNNNN 1400
1401 NNNNNSPSPTPTTATATAETEAETEVNANEIVSSTEAPKAETETSVAPEV 1450
1451 ETPETAKPSPIVPSPVLATNQKTEDAANKLNNNEKLAEISASPEPPIVVE 1500
1501 TPEADLLQLSDSETKPNKETEAEDSVALAVRDIVEQLIDKVIDATEAESA 1550
1551 SETKTETNNNEIPKKEKQTSEEPEDVETAETLAAAAKEIVQEVVEAALVM 1600
1601 VQEESTQEKPEKGANSEEEKNEIGKEEILLQLEEKPASTEVQETKIEGDL 1650
1651 KKPEDPKGHSSVEPKTPNLEEPKPQETEQQKSQEVAEELPQKPEEQVVAI 1700
1701 VNQVLDTLVDDTVKAVAAEQTTQTSPAPEEQSPQILAMESPATSVRVKPT 1750
1751 EVDSTTQTTPKNEAGSSLLVEQVQQVLQEDDAQQSAGMTIEDEDYSNQQA 1800
1801 AAAVENANSSQLDANHYGPGNPESKQQQQRSKSGSTRPMFSPGPTRPPFR 1850
1851 IPEFKWSYIHQRLLSDVLFSLETDIQVWRSHSTKSVLDFVNSSENAIFVV 1900
1901 NTVHLISQLADNLIIACGGLLPLLASATSPNSELDVLEPTQGMPLEVAVS 1950
1951 FLQRLVNMADVLIFATSLNFGELEAEKNMSSGGILRQCLRLVCTCAVRNC 2000
2001 LECKERTRYNVGALARDVPGAAHLQALIRGAQASPKNIVESITGQLSPVK 2050
2051 DPEKLLQDMDVNRLRAVIYRDVEETKQAQFLSLAIVYFISVLMVSKYRDI 2100
2101 LEPPAEPQIQRQSPVLQRTAGGGGRQIQDSDYEIIVVDENNPSVLADNDS 2150
2151 HSSGPPSIKANQTTTIATTTTTTATTHINNNNTKTTTSNAPTATIKIQQQ 2200
2201 PLSPPKPLVPQKLPKSVDSDVGSLNMNSTENEVPEVESSSEILIDDHKPS 2250
2251 HSNDESWTDVNLNEDAAVQAASAGIVVGLVDNRGNVISDKHDPSHHNQQQ 2300
2301 QQQAGIIGQQQQHGSLGHSERGDKPDSEISVVRVPDGYGGAGSGGGVNSG 2350
2351 QGQGVPSNQRPRPEELPMKAPALVAQLPLTTPSREASLTQKLEIALGPVC 2400
2401 PLLREIMVDFAPFLSKTLVGSHGQELLMEGKGLTTFKNSHSVVELVMLLC 2450
2451 SQEWQNSLQKHAGLAFIELINEGRLLSHAMKDHIVRVANEAEFILNRMRA 2500
2501 DDVLKHADFESQCAQTLLERREEERMCDHLITAARRRDNVIASRLLEKVR 2550
2551 NIMCNRHGAWGDSSSTSSGGAIVGAVQKSPYWKLDAWEDDARRRKRMVQN 2600
2601 PRGSSHPQATLKAALENGGPEDAILQTRDEFHTQIAVSRTHPSGQHNGEL 2650
2651 LDDAELLIEDRELDLDLTGPVNISTKARLIAPGLVAPGTVSITSTEMFFE 2700
2701 VDEEHPEFQKIDGEVLKYCDHLHGKWYFSEVRAIFSRRYLLQNVALEIFL 2750
2751 ASRTSILFAFPDQHTVKKVIKALPRVGVGIKYGIPQTRRASMMSPRQLMR 2800
2801 NSNMTQKWQRREISNFEYLMFLNTIAGRTYNDLNQYPIFPWVLTNYESKD 2850
2851 LDLSLPSNYRDLSKPIGALNPSRRAYFEERYESWDSDTIPPFHYGTHYST 2900
2901 AAFTLNWLVRVEPFTTMFLALQGGKFDYPDRLFSSVSLSWKNCQRDTSDV 2950
2951 KELIPEWYFLPEMFYNSSGYRLGHREDGALVDDIELPPWAKSPEEFVRIN 3000
3001 RMALESEFVSCQLHQWIDLIFGYKQRGPEAIRATNVFYYLTYEGSVDLDG 3050
3051 VLDPVMREAVENQIRNFGQTPSQLLMEPHPPRSSAMHLSPMMFSAMPEDL 3100
3101 CQMLKFYQNSPVIHISANTYPQLSLPSVVTVTAGHQFAVNRWNCNYTASV 3150
3151 QSPSYAESPQSPGSNQPLTIDPVLAVHGTNNNSNAASRRHLGDNFSQMLK 3200
3201 IRSNCFVTTVDSRFLIACGFWDNSFRVFATETAKIVQIVFGHFGVVTCMA 3250
3251 RSECNITSDCYIASGSADCTVLLWHWNARTQSIVGEGDVPTPRATLTGHE 3300
3301 QAVTSVVISAELGLVVSGSSNGPVLIHTTFGDLLRSLDPPAEFHSPELIT 3350
3351 MSREGFIVINYDKGNVAAYTINGKKLRHETHNDNLQCMLLSRDGEYLMTA 3400
3401 GDRGIVEVWRTFNLAPLYAFPACNAGIRSLALTHDQKYLLAGLSTGSIIV 3450
3451 FHIDFNRWHHEYQQRY 3466
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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