DTU Health Tech
Department of Health Technology
DTU Health Tech
Department of Health Technology
The NetAcet 1.0 server predicts substrates of N-acetyltransferase A (NatA). The method was trained on yeast data but, as mentioned in the article describing the method, it obtains similar performance values on mammalian substrates acetylated by NatA orthologs.
Restrictions
At most 2000 sequences and 200,000 amino acids per submission;
each sequence not less than 40 and not more than 4,000 amino acids.
Confidentiality:
The sequences are kept confidential and will be deleted
after processing.
For publication of results, please cite:
NetAcet: Prediction of N-terminal acetylation sites.
Lars Kiemer, Jannick Dyrløv Bendtsen and Nikolaj Blom.
Accepted in Bioinformatics, 2004.
Please note that the sequences containing other symbols e.g. X (unknown) will be discarded before processing. The sequences can be input in the following two ways:
All the other symbols will be converted to X before processing. The sequences can be input in the following two ways:
Both ways can be employed at the same time: all the specified sequences will
be processed. However, there may be not more than 10 sequences
in total in one submission. The sequences shorter than 15
or longer than 4000 amino acids will be ignored.
At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.
>RS0A_YEAST - netAcet 1.0 prediction SLPATFDLTPEDAQLLLAANTHLGARNVQVHQEPYVFNARPDGVHVINVGKTWEKLVLAARIIAAIPNPEDVVAISSRTF GQRAVLKFAAHTGATPIAGRFTPGSFTNYITRSFKEPRLVIVTDPRSDAQAIKEASYVNIPVIALTDLDSPSEFVDVAIP CNNRGKHSIGLIWYLLAREVLRLRGALVDRTQPWSIMPDLYFYRDPEEVEQQVAEEATTEEAGEEEAKEEVTEEQAEATE WAEENADNVEW #Seq-Position-Residue Score Acetylation predicted #------------------------------------------------------ RS0A_YEAST-1-S 0.513 yes //
NetAcet: Prediction of N-terminal acetylation sites.
Lars Kiemer, Jannick Dyrløv Bendtsen and Nikolaj Blom.
Bioinformatics, 21(7):1269-70, 2005.
Center for Biological Sequence Analysis, BioCentrum-DTU,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
A very important task in machine learning methods is to obtain a clean and accurate dataset for training
and testing. Bias and noise in the data set often lead to wrong predictions.
The data used for NetAcet were extracted
from Table 2 in Polevoda et al. and from the Yeast Protein Map.
All inconsistensies between the two data sets were removed resulting in a positive set of 61 sequences and 76 negative sequences.
Sequences were truncated to their N-terminal 40 residues and subsequently homology
reduced by visual inspection of a neighbour-joining tree generated from a ClustalW
multible alignment. Four sequences were removed from the positive dataset due to close
homology to other sequences and following this reduction the two closest homologs were 52% identical although the average homology is much lower.
Below is shown an unrooted phylogenetic tree of the positive data set before homology reduction.
Below is shown an unrooted phylogenetic tree of the negative data set before homology reduction.
To visualise the sequence information content for N-terminal acetylation,
we have generated sequence logos for the yeast training set.
The total height of the stack of letters at each position shows
the amount of sequence conservation at the position, while the relative
height of each letter shows the relative abundance of the corresponding
amino acid.
Description of data sets
Dataset extraction
Homology reduction
Sequence logos
Download the training sets
Data set
The section describes the extraction and homology reduction of the data sets used for
training of NetAcet 1.0.
Extraction
Homology reduction
Sequence logos
Blue:
Positively charged residues
Red:
Negatively charged residues
Green:
Neutral polar residues
Black:
Hydrophobic residues
Download the dataset
The datasets used for the training of NetAcet can be downloaded here:
Positive training set
Negative training set
If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0). If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).
If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.
Correspondence:
Technical Support:
