Authors
Dariusz Plewczynski and Adrian Tkacz
Summary
The rapid increase in genomic information requires new automatic techniques to investigate protein functions. The function of proteins is partially determined by short sequence segments. For example the phosphorylation by protein kinases is an important mechanism for controlling intracellular processes. Many kinases are known, but the identification of their potential biological targets is still ongoing research. High substrate specificity of protein kinases ensures correct transmission of signals in cells. The specificity is largely determined by the primary sequence of the target site, but we lack general, efficient and error prune tools for identifying these sites. Most methods designed to predict functional motifs process local sequence information around post-translational modification sites. We present here an advanced computational protocol for rapid identification of post-translational modifications (PTM) in proteins on the whole genome scale. The AutoMotif Server (AMS) identifies various types of post-translational modifications in protein sequences. A query protein sequence is dissected into overlapping short segments. Each segment is projected into an abstract space of sequence fragments by 10 different representations. Those projections are compared with the database of representations of known and confirmed by experiments post-translational modification sites using the support vector machine (SVM) approach 1, 2. The supervised machine learning approach is able to predict the most of post-translational modification sites in proteins. It is based on the classification of the biological functional information acquired from the Swiss-Prot database version 4.2. The classification models are then used to predict new modification sites in proteins. Users can access a list of sites in proteins annotated as being able to undergo certain post-translational modification in Swiss-Prot database and add new annotated sequence segments from proteins (positive instances). The AMS server was demonstrated 3, 4 to gain high accuracy in distinguishing short sequence fragments that are post-translational modified from those that are not. The efficiency of the classification for each type of modifications and the prediction power of several versions of the method is estimated using the standardized leave-one-out tests. The sensitivities of the protocol for all types of modifications are in the range of 70%. The AutoMotif Server is freely available at http://automotif.bioinfo.pl/. The local version of the software is available on request from the authors. The parameters (the search type, the number of top models, and the PTM type) are optional and can be easily modified. The following protocol describes how to use AMS server to detect various types of post-translational modifications, and how to understand the resulting score for a given prediction.Further details
The protocol was published on Protocol Exchange in 2007. To see the entire protocol, click on the source link.Advertisement
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