SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q2QLA2 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MATDGASCEPDFSRSPEDAAGATAEAAKKEFDVDTLSKSELLMLLSVMEG    50
  51  ELEARDLVIEALRARRKEVFIQERYGRFNLNDPFLALQRDYEAGAGDKEK   100
 101  KPVCTNPLSILEAVMAHCRKMQERMSTQLAAAESRQKKLEMEKLQLQALE   150
 151  QEHKKLASRLEEERGKNKHVVLMLVKECKQLSSKVIEEAQKLEEVMAKLE   200
 201  EEKKKTSALEEELSAEKRRSTEMEAQMEKQLSEFDTEREQLRAKLHREEA   250
 251  HTTDLKEEIDKMKKMIEQLKRGNDNKPSLSLPRKTKDRRLVSISVGTEGP   300
 301  MTRSVACQTDPVIESSDHVKKLPLTMPVKPSTGSPLVSANAKGNVCTNAA   350
 351  LVRPGIDRQASHGDLIGSSLPTVPPPSANRIEENGPSTGSTADLTSSTPP   400
 401  LPNNAAPPTVQTPGVAPQSYSQASPMHSLHSPCANASLHPGLNPRIQAAR   450
 451  FRFQGNANDPDQNGNTTQSPPSRDVSPTSRENLVAKQLARNTVTQALSRF   500
 501  TSPQAGAPPRPGVSPTGDVGTYPPVGRTSLKTPGVARVDRGNPPPIPPKK   550
 551  PGLSQTPSPPHPQLKVIMDSSRASNAGAKVDNKTVASPPSSLPQGNRVIN   600
 601  EENLPKSSSPQLPPKPSIDLTVAPAGCAVSALATSQVGAWPAETPGLNQP   650
 651  ACSESSLVIPTTIAFSSSINPVSASSCRAGASDSLLVTASGWSPSLTPLL   700
 701  MSGGPAPLAGRPTLLQQAAAQGNVTLLSMLLNEEGLDINYSCEDGHSALY   750
 751  SAAKNGHTDCVRLLLNAEAQVDAADKNGFTPLCAAAAQGHFKCVQLLIAY   800
 801  DANINHAADGGQTPLYLACKNGNKECIKLLLEAGSDRSIKTRDGWTPVHA   850
 851  AVDTGNVDSLKLLMYHRAPAHGSSLHKEEPESSIFDLDRQGEESPEGTFK   900
 901  PVVPADLINQADREGWTAAHIAASKGFKNCLEILCRHGGLEPERRDKCSR   950
 951  TAHDVATDDCKHLLENLNALKIPLRISVGEIQPGNYGSDDFECENTICAL  1000
1001  NIRKQTSWDDFSKAVTQALTNHFQAISSDGWWSLEDMKFNNTTDSSIGLG  1050
1051  ASSVRSITLGNVPWSAGQSFTQSPWDFMRKNKAEQITVLLSGPQEGCLSS  1100
1101  VTYTSMIPLQMLQNYLRLVEQYHNVIFHGPEGSLQDYIAHQLALCMKHRQ  1150
1151  MAAGFSCEIVRAEVDAGFSKEQLVDLFISSACLIPVKQSPVKKKIIIILE  1200
1201  NLERSSLSELLGDFLAPLENRCPESPCTFQKGNGTAECYYFHENCFLMGT  1250
1251  IAKACLQGSDLLVQQHFRWVQLRWDGEPMQGLLQRFLRRKVVNKFRGQVP  1300
1301  SPCDPVCKTVDWALAVWRQLNSCLARLGTPEALLGPKYFLSCPVIPGHAQ  1350
1351  ATVKWMSKLWNAVIAPRVQDAILSRASVKRQPGLGQTIAKKHPSQGQQAV  1400
1401  VKAALSILLNKAVLHGCPLPRAELDQHTADFKGGSFPLSLVSSYNSCSKK  1450
1451  KGESGAWRKVSTSPRKKSSRFSSPTWNKPDLSEEGIKNKTISQLNCNRNA  1500
1501  SLSKQKSFENDLSLTLSLDQRFSLGSDDEADLVKELQSMCSSKSESDISK  1550
1551  IADSRDDLRRFDSPGNNPAFSATVNNPRMPVSQKEVSPLSSHQTTECSNS  1600
1601  QSKTELGVSRVKSFLPVPRSKVTPCSQNTKRSSSSSNTRQIEINNNSKEE  1650
1651  IWNLRKNEQVEKPNK                                     1665

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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