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

NucPred

Fetching P97924 from www.uniprot.org...

The NucPred score for your sequence is 0.96 (see score help below)

   1  MVLSGSFRNDGLKASDVLPILKEKVAFVSGGRDKRGGPILTFPARTNHDR    50
  51  IRQEDLRKLVTNLASVPSEDVCKRGFTVIIDMRGSKWDLIKPLLKTLQEA   100
 101  FPAEIHVALIIKPDNFWQKQKTNFGSSKFIFETSMVSVEGLTKLVDPSQL   150
 151  TEEFDGSLDYNHEEWIELRLSLEEFFNSAVHLLSRLEDLQEMLARKEFPV   200
 201  DVEGSRRLIDEHTQLKKKVLKAPVEELDREGQRLLQCIRCSDGFSGRNCI   250
 251  PGSADFQSLVPKITSLLDKLHSTRQHLHQMWHVRKLKLDQCFQLRLFEQD   300
 301  AEKMFDWISHNKELFLQSHTEIGVSYQHALDLQTQHNHFAMNSMNAYVNI   350
 351  NRIMSVASRLSEAGHYASQQIKQISTQLDQEWKSFAAALDERSTILAMSA   400
 401  VFHQKAEQFLSGVDAWCKMCSEGGLPSEMQDLELAIHHHQSLYEQVTQAY   450
 451  TEVSQDGKALLDVLQRPLSPGNSESLTATANYSKAVHQVLDVVHEVLHHQ   500
 501  RRLESIWQHRKVRLHQRLQLCVFQQDVQQVLDWIENHGEAFLSKHTGVGK   550
 551  SLHRARALQKRHDDFEEVAQNTYTNADKLLEAAEQLAQTGECDPEEIYKA   600
 601  ARHLEVRIQDFVRRVEQRKLLLDMSVSFHTHTKELWTWMEDLQKEVLEDV   650
 651  CADSVDAVQELIKQFQQQQTATLDATLNVIKEGEDLIQQLRSAPPSLGEP   700
 701  TEARDSAVSNNKTPHSSSISHIESVLQQLDDAQVQMEELFHERKIKLDIF   750
 751  LQLRIFEQYTIEVTAELDAWNEDLLRQMNDFNTEDLTLAEQRLQRHTERK   800
 801  LAMNNMTFEVIQQGQDLHQYIMEVQASGIELICEKDVDLAAQVQELLEFL   850
 851  HEKQHELELNAEQTHKRLEQCLQLRHLQAEVKQVLGWIRNGESMLNASLV   900
 901  NASSLSEAEQLQREHEQFQLAIEKTHQSALQVQQKAEALLQAGHYDADAI   950
 951  RECAEKVALHWQQLMLKMEDRLKLVNASVAFYKTSEQVCSVLESLEQEYR  1000
1001  RDEDWCGGRDKLGPAAEMDHVIPLLSKHLEQKEAFLKACTLARRNAEVFL  1050
1051  KYIHRNNVSMPSVASHTRGPEQQVKAILSELLQRENRVLHFWTLKKRRLD  1100
1101  QCQQYVVFERSAKQALDWIQETGEYYLSTHTSTGETTEETQELLKEYGEF  1150
1151  RVPAKQTKEKVKLLIQLADSFVEKGHIHATEIRKWVTTVDKHYRDFSLRM  1200
1201  GKYRYTLEKALGVNTEDNKDLELDIIPASLSDREVKLRDANHEVNEEKRK  1250
1251  SARKKEFIMAELLQTEKAYVRDLHECLETYLWEMTSGVEEIPPGILNKEH  1300
1301  IIFGNIQEIYDFHNNIFLKELEKYEQLPEDVGHCFVTWADKFQMYVTYCK  1350
1351  NKPDSNQLILEHAGTFFDEIQQRHGLANSISSYLIKPVQRVTKYQLLLKE  1400
1401  LLTCCEEGKGELKDGLEVMLSVPKKANDAMHVSMLEGFDENLDVQGELIL  1450
1451  QDAFQVWDPKSLIRKGRERHLFLFEISLVFSKEIKDSSGHTKYVYKNKLL  1500
1501  TSELGVTEHVEGDPCKFALWSGRTPSSDNKTVLKASNIETKQEWIKNIRE  1550
1551  VIQERIIHLKGALKEPIQLPKTPAKLRNNSKRDGVEDGDSQGDGSSQPDT  1600
1601  ISIASRTSQNTVESDKLSGGCELTVVLQDFSAAHSSELSIQVGQTVELLE  1650
1651  RPSERPGWCLVRTTERSPPQEGLVPSSTLCISHSRSSVEMDCFFPLVKDS  1700
1701  YSHSSGENGGKSESVANLQSQPSLNSIHSSPGPKRSTNTLKKWLTSPVRR  1750
1751  LNSGKADGNIKKQKKVRDGRKSFDLGSPKPGDETTPQGDSADEKSKKGWG  1800
1801  EDEPDEESHTPLPPPMKIFDNDPTQDEMSSLLAARQAPTDVPTAADLVSA  1850
1851  IEKLVKSKLTLEGGSYRGSLKDPTVCLNEGMAPPTPPRNLEEEQKAKALR  1900
1901  GRMFVLNELVQTEKDYVKDLGIVVEGFMKRIEEKGVPEDMRGKEKIVFGN  1950
1951  IHQIYDWHKDFFLAELEKCIQEQDRLAQLFIKHERKLHIYVWYCQNKPRS  2000
2001  EYIVAEYDAYFEEVKQEINQRLTLSDFLIKPIQRITKYQLLLKDFLRYSE  2050
2051  KAGLECSDIEKAVELMCLVPKRCNDMMNLGRLQGFEGTLTAQGKLLQQDT  2100
2101  FYVIELDAGMQSRTKERRVFLFEQIVIFSELLRKGSLTPGYMFKRSIKMN  2150
2151  YLVLEEDVDDDPCKFALMNRETSERVILQAANSDIQQAWVQDINQVLETQ  2200
2201  RDFLNALQSPIEYQRKERSTAVIRSQPPRVPQASPRPYSSVPVGSEKPPK  2250
2251  GSSYNPPLPPLKISTSNGSPGFDYHQPGDKFDASKQNDLGGCNGTSTMAV  2300
2301  IKDYYALKENEICVSQGEVVQVLAVNQQNMCLVYQPASDHSPAAEGWVPG  2350
2351  SILAPLTKATAPAESSDESIKKSCSWHTLRMRKRADVENTGKNEATGPRK  2400
2401  PKDILGNKASVKETNSSEESECDDLDPNTSMEILNPNFIQEVAPEFLVPL  2450
2451  VDVTCLLGDTVLLQCKACGRPKPTITWKGPDQNILDTDNSSATYTISSCD  2500
2501  SGESTLKICNLMPQDSGIYTCIATNDHGTASTSATVKVQGVPAAPNRPIA  2550
2551  QERSCTSVILRWLPPASTGNCTISGYTVEYREEGSQVWQQSVASTLDTYL  2600
2601  VIEDLSPGCPYQFRVSASNPWGISLPSEPSEFVHLPEYDAAADGATISWK  2650
2651  ENFDSAYTELNEIGRGRFSIVKKCIHKATRKDVAVKFVSKKMKKKEQAAH  2700
2701  EAALLQHLQHPQYVTLHDTYESPTSYILILELMDDGRLLDYLMNHDELME  2750
2751  EKVAFYIRDIMEALQYLHNCRVAHLDIKPENLLIDLRIPVPRVKLIDLED  2800
2801  AVQISGHFHIHHLLGNPEFAAPEVIQGIPVSLGTDIWSIGVLTYVMLSGV  2850
2851  SPFLDESKEETCINVCRVDFSFPHEYFCGVSNAARDFINVILQEDFRRRP  2900
2901  TAATCLQHPWLQPHNGSYSKIPLDTSRLACFIERRKHQNDVRPIPNVKSY  2950
2951  IVNRVNQGT                                           2959

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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