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

NucPred

Fetching O13290 from www.uniprot.org...

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

   1  MDKNDNSQCQLSTVSDEILIFLKKLLSLSVSDNLDDICETIALQSTFVDT    50
  51  FRSFINDEYYTVIYLYGSPCLTSSPTSPDSCTFGNMTFQWTSLLQDVFSG   100
 101  TSAFAIFKRFPLTDTPLTLQNMLYINQLPILKGGHKSNEIPERPINAIFL   150
 151  YTKYVMSCYFTAYLAMESVDDRSTIDLNSPSKGKELELTCQKFADFERSF   200
 201  TFFCREYQNSDTILQHHPLILSTIKHAEENNLDLSVRLLPSKVLSDSEFY   250
 251  KSLSNLVNVWLKTTRSLIKLFHDQISKTALEEFNFWQFYYRSLSRLNDQL   300
 301  HSRPVLFVLDILAFGKRFHTIASFNSETNIQCFVDKVGKIDALFKEISLD   350
 351  IFLSSSTLESLQLSAALLYSTFSKKWRNTGYPETRVLDFINFITEDLLKL   400
 401  ISRLLPALGALSLSNVDFSHRTAVSSDILSLCYIRLKDFLRISGSLKEEQ   450
 451  SYYGLKNSIKQIKAFENKLKYIQSFHEKHQQLIGALSEVYGLTHLTELEI   500
 501  LEHLNKKEHVFNILTVFKDLQSLNVLDISLKGVNAWNSLETSYYNCMTVL   550
 551  EDEVIAQLKSLLQYSKTSSQMFTTLMRFQPLFFRTRVRTSISDCLHLLVN   600
 601  RIKQELDLLKTRFTEDVSDTELIAMNELRNLPMASSAIIWATQLKNRLHE   650
 651  YTKNINIIFGEDWNNFPDGFELKVECITLQKRLDTNLIFTNWINDVSSRN   700
 701  LNFDFDSKIFYLTQSESAESPLRLSVSIDFDPCSFCKEIRTLAHLGYNIP   750
 751  SQLMELASCLQRIQLIAMCLIDSVQSFNDVSFEISKTEEERFLLQEYELA   800
 801  VRQHIVTGLFISWNDFIVGNLSTPPKCAIGKRNFLKNIHPNVENYAYQFS   850
 851  SLTSLLMNKRNAISHTYMQIQEQLFQLDICEYSGDIFLTIQRKLQDLIDL   900
 901  LYVNGYSNLPPFVRALNLRFQDLLISRCRKFLSFFKTTILTSGNENNDLK   950
 951  SKFSSDMYEKLRGFLKPTNLTIQRNIIEFDPPVYRKKEDSIYLLDMCLQS  1000
1001  VVNIPLLSIKTTAQRNCTLIGFFPVINRLESEILGIFESLLFHFDSILGY  1050
1051  QNYWKKVEPFLNLNSLNLLLKSELFQLDQCYSLSLYLIHLKSEVDEIGKV  1100
1101  TDFKIFSVNNTEFKSQVYLYLREWINALFDRFTFLLGKESEHLLNELDDT  1150
1151  HSSLSTVDFTVNNTESLINSLKIFKKAGCYKLNVEHKIITYQNYEMTFND  1200
1201  CDAFSEFNFSLLKDITSKWKDLLESFECRRLKLENNKDEILRNFSEFAKR  1250
1251  VNTETLSLISEWCASSLSLIKANYDEFSSTVDDFLFRFSKATEQCLMVKY  1300
1301  IKKDLEIEIEESCDFSIQTEEIHLYKKFKDVISSNLEFIVEIKNTRWKLF  1350
1351  DTATLSVQTTHQINALESVHTSFQHFKLFTNTKQSLNQLKDCTLLLQKLK  1400
1401  SCPLKPVHWISLFEITKSTEQLDFEKLLVSDILGIDLQAHESFITTLLNS  1450
1451  AVVEANLENQFNEVHSFWKNSYFSFKSFKGRNYIVVGCQELIDAVEKNMD  1500
1501  SLNLIKTSRHFKDGDMNITDLQSKMKIIVKFLNIWKEIQQIWTHLSAIFY  1550
1551  ESTYIQQLLPELAASFFNSSKTYMHLVTLLKERSYLYKVSNIPSLLESAA  1600
1601  KLSTTLEDSKKSLLKYFELQRHKISRLYFLGDDDLMELISNPCDPFVINK  1650
1651  QIIKLYPGIRSLIVDTENTNINGCTTNEGNELLFDNPICLLDNTQPLHWI  1700
1701  SSLEPFLKATLFQLFSTSFQQIRDFYYNKSRNVFCKEWFLRYPSQITLLS  1750
1751  LRCTLCHEIETGIADCCLDAVFNFINDGISSLVLLADENELSIKKKVTLM  1800
1801  FNELLHFKETVGLLCKNSFNNYFWSREVKAFYREDHDDEAVVIKMFSLEF  1850
1851  IYAFEYSELDDPIVYTDLTRNCFSVLLHSIASNLGGSPIGPAGTGKTETV  1900
1901  KAVSAYLGKNVFVFNCDNAFNYKTIQRILSGLAQIGTYICFDEFNRLDSG  1950
1951  TLSAISYDIQRIQSLVSHSDGLCQSPILLDAPTIFVTMNPGYLGRFKLPS  2000
2001  NLKKLFRPIWMGSPDNKKICEILFLSFGFKESSLLSQVLDSFFLCCSGSL  2050
2051  SNCLHYDFGLRAMKVVIKAAKRIKGFLKKKNTICQELEILWYAIREVLYP  2100
2101  SLIYQDIPLFFKAEESYFNFPAVKANAFIDPDNFEVNIEQTLSKNFFGNN  2150
2151  QYLKLKIMQLYQMSEAYNGIILLGKTGSGKSQIFRILQSALLNIGIDCIV  2200
2201  YVISPKALTKESLFGSMNMDTREWTDGVFTKLLRKTRDSCYYKRYMFVFD  2250
2251  DELSPEWVEAMNSLLDDNKTLTLSNGERIALQPYVKIFFEADSVASLTRA  2300
2301  TISRCGLICISNIDDNILSSTDKMLSFTSGATNYPLGSSNDEFSTVFSKV  2350
2351  LTDEVMMNLISSCYKFSVDLQHIMNFTKQRFFTTFYSLLDQTKLFTRSSN  2400
2401  ITESLSFKELCNYLKKKICYILAWCCTGDTDAKSRERFTHWLMQNASVDL  2450
2451  PEIKDFEHVSILDFDVSLETQSWYPIAGKTLKSSALKYAGNTVIPTLDTV  2500
2501  RYAEFLNFSLTKNRCVIFCGPPGSGKSMLMLGTLRSRQDVEVIALNFSIS  2550
2551  TSSKSVVSFLEQSTVYYRSTGMTIMCPKNHEKVLVLFCDEINLPRSRNCL  2600
2601  AEDVICFLRHMLEHQGFWHPLHKEWVTIKNIFVCGACNPSTDIGRNDFPE  2650
2651  RFLRRTVLIFVDYPESYSLVTIYNALLEKSALINQYKTIILNIVKASVKF  2700
2701  YQVLRENFKSSTQGYVYTPRDLTRWLISFKNYAESYAETNNLSLIKVWYH  2750
2751  EACRVLLDRLVSQKECSWGMTELQKVIVTDFGEFEVSVIFEKQIIFTDIL  2800
2801  KNGLEFLDFASLRPKLESLYKKFYSSHPNNTLVFVDETITHILRFHRILN  2850
2851  NSGMHALLQGSVGLGQKAVVEFVCWLNSFSLFELQKNQTYSIEDFEDNLK  2900
2901  SILILAGTTNCKACLAINESIAGVPGFLDLLNNLLTNSEVSNFFDQNDWA  2950
2951  EIKKNLNKLNEFQPLKFDSEESVTEIFMNNVFQNLCVVFYVYTSADVDFQ  3000
3001  TNSLSPALLNRCTIDYYHSWDYHSMLQIANEVLQETISLNALDHDNPNLK  3050
3051  NIKGSSIYDAVAQAVVNTHTSIVWEFKHLGKTSYFSCLHFIRFLNTFCLI  3100
3101  FGRDANKLSKEKSRIENGFKKIKETSQGIDKFKEALSDQQNVLFSKTKTA  3150
3151  NDRLQCIIQTKQAVEAKKVYSLQAEASLQKKSFLLNEKKNSVMKEVSYAK  3200
3201  PAVIEARKSVSDIKKAHLIELRSLSRPPMAIRITMEVVCKLLGFSATDWK  3250
3251  NVQQLLKRDDFIPKILNYNLEKELSINLRRKIEQDYFSNPIFTFDSVNRA  3300
3301  SKACGPLLLWIKSICNYSKVLEKLEPLNSEVDRLKLEQKNAEECIQETIA  3350
3351  ACKDLDEKLLQLQEEYASMISEIHSMELQMDEVKCKMQRSIEVITDLSIE  3400
3401  RNEWSGFLNLYPKRMWNLVGESLMEASFVVYAGNLDPSMRIFLRNKCEPI  3450
3451  ISSFGFPISKSAVRTNIERCVQTSIESKYYKNLTDYSLENIYIIQENKSP  3500
3501  LLIIDPSSQILDILPSLYKGKASDLISFSNKSFQNQIKLALLSGSAIIIK  3550
3551  DAELWDVSIEPLLKPEFFTGSGEVQTTFAKDTITITLPLNIIFFSEVQSN  3600
3601  ELENKASKFMNVVNFTLSISLLETQMLKSVISVQEPGVFKQKDNCFTLKL  3650
3651  SIERQIRSLQEQLLKTLCSSNENIVGTDEIVVLLKNLKEKHETIRLAYSE  3700
3701  SQSINRKVDELIRRYKLSIKSFLSVVVVFQHFISLKKSYSFSFNFIWSTF  3750
3751  HQMLNVVLENRNQDFKSLIMDALRDLIRRCFLYIFPEDRVLFLFLLMFFF  3800
3801  FPKETESLRKLLIVNGKTLELEQSYLNFFETCSDSNERGGLESLFFKTHA  3850
3851  SNIQNFCTEVLANTHCEEDCLKLLYDLWSSAFKVEFSNIKYDFLKIINDE  3900
3901  SESRMPTIVYLMENCEIDSLLQNAKIPQNIKKLTVSLGSAENESLADSYL  3950
3951  KLASTEPLWLFINNIHLSTPWAEKLPSKMSNHLHKNSRIVCLSEIHNQLP  4000
4001  HQLLCISRSIVFNKQTSFKNNLLNLLELLPTMTHTLPHNRFRLFFFLSWL  4050
4051  HATLAEIYCFTCSSWKEPCYFDDSDFYFGTKILCNILYRNVHLEEFSWGT  4100
4101  FKDLLLNVVYGPKVSASSDFIALDKILKRLIAQFKTQISSNILLTDNFKF  4150
4151  ILPYEITFSSAKEVIGQLPDEIPPGWLDIPENSKRKRTDIYFSMCI      4196

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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