 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9SP32 from www.uniprot.org...
The NucPred score for your sequence is 0.46 (see score help below)
1 MVMEDEPREATIKPSYWLDACEDISCDLIDDLVSEFDPSSVAVNESTDEN 50
51 GVINDFFGGIDHILDSIKNGGGLPNNGVSDTNSQINEVTVTPQVIAKETV 100
101 KENGLQKNGGKRDEFSKEEGDKDRKRARVCSYQSERSNLSGRGHVNNSRE 150
151 GDRFMNRKRTRNWDEAGNNKKKRECNNYRRDGRDREVRGYWERDKVGSNE 200
201 LVYRSGTWEADHERDVKKVSGGNRECDVKAEENKSKPEERKEKVVEEQAR 250
251 RYQLDVLEQAKAKNTIAFLETGAGKTLIAILLIKSVHKDLMSQNRKMLSV 300
301 FLVPKVPLVYQQAEVIRNQTCFQVGHYCGEMGQDFWDSRRWQREFESKQV 350
351 LVMTAQILLNILRHSIIRMETIDLLILDECHHAVKKHPYSLVMSEFYHTT 400
401 PKDKRPAIFGMTASPVNLKGVSSQVDCAIKIRNLETKLDSTVCTIKDRKE 450
451 LEKHVPMPSEIVVEYDKAATMWSLHETIKQMIAAVEEAAQASSRKSKWQF 500
501 MGARDAGAKDELRQVYGVSERTESDGAANLIHKLRAINYTLAELGQWCAY 550
551 KVGQSFLSALQSDERVNFQVDVKFQESYLSEVVSLLQCELLEGAAAEKVA 600
601 AEVGKPENGNAHDEMEEGELPDDPVVSGGEHVDEVIGAAVADGKVTPKVQ 650
651 SLIKLLLKYQHTADFRAIVFVERVVAALVLPKVFAELPSLSFIRCASMIG 700
701 HNNSQEMKSSQMQDTISKFRDGHVTLLVATSVAEEGLDIRQCNVVMRFDL 750
751 AKTVLAYIQSRGRARKPGSDYILMVERGNVSHAAFLRNARNSEETLRKEA 800
801 IERTDLSHLKDTSRLISIDAVPGTVYKVEATGAMVSLNSAVGLVHFYCSQ 850
851 LPGDRYAILRPEFSMEKHEKPGGHTEYSCRLQLPCNAPFEILEGPVCSSM 900
901 RLAQQAVCLAACKKLHEMGAFTDMLLPDKGSGQDAEKADQDDEGEPVPGT 950
951 ARHREFYPEGVADVLKGEWVSSGKEVCESSKLFHLYMYNVRCVDFGSSKD 1000
1001 PFLSEVSEFAILFGNELDAEVLSMSMDLYVARAMITKASLAFKGSLDITE 1050
1051 NQLSSLKKFHVRLMSIVLDVDVEPSTTPWDPAKAYLFVPVTDNTSMEPIK 1100
1101 GINWELVEKITKTTAWDNPLQRARPDVYLGTNERTLGGDRREYGFGKLRH 1150
1151 NIVFGQKSHPTYGIRGAVASFDVVRASGLLPVRDAFEKEVEEDLSKGKLM 1200
1201 MADGCMVAEDLIGKIVTAAHSGKRFYVDSICYDMSAETSFPRKEGYLGPL 1250
1251 EYNTYADYYKQKYGVDLNCKQQPLIKGRGVSYCKNLLSPRFEQSGESETV 1300
1301 LDKTYYVFLPPELCVVHPLSGSLIRGAQRLPSIMRRVESMLLAVQLKNLI 1350
1351 SYPIPTSKILEALTAASCQETFCYERAELLGDAYLKWVVSRFLFLKYPQK 1400
1401 HEGQLTRMRQQMVSNMVLYQFALVKGLQSYIQADRFAPSRWSAPGVPPVF 1450
1451 DEDTKDGGSSFFDEEQKPVSEENSDVFEDGEMEDGELEGDLSSYRVLSSK 1500
1501 TLADVVEALIGVYYVEGGKIAANHLMKWIGIHVEDDPDEVDGTLKNVNVP 1550
1551 ESVLKSIDFVGLERALKYEFKEKGLLVEAITHASRPSSGVSCYQRLEFVG 1600
1601 DAVLDHLITRHLFFTYTSLPPGRLTDLRAAAVNNENFARVAVKHKLHLYL 1650
1651 RHGSSALEKQIREFVKEVQTESSKPGFNSFGLGDCKAPKVLGDIVESIAG 1700
1701 AIFLDSGKDTTAAWKVFQPLLQPMVTPETLPMHPVRELQERCQQQAEGLE 1750
1751 YKASRSGNTATVEVFIDGVQVGVAQNPQKKMAQKLAARNALAALKEKEIA 1800
1801 ESKEKHINNGNAGEDQGENENGNKKNGHQPFTRQTLNDICLRKNWPMPSY 1850
1851 RCVKEGGPAHAKRFTFGVRVNTSDRGWTDECIGEPMPSVKKAKDSAAVLL 1900
1901 LELLNKTFS 1909
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.