AMS 3.0: prediction of post-translational modifications.
BACKGROUND
We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs.


RESULTS
The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods.


CONCLUSIONS
The standalone version of AMS 3.0 presents an efficient way to identify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at http://code.google.com/p/automotifserver under the Apache 2.0 license scheme.
Basu
Subhadip
S
Department of Computer Science and Engineering, Jadavpur University, Kolkata - 700032, India.
Plewczynski
Dariusz
D
D016571:Neural Networks, Computer; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software
D011506:Proteins; D020543:Proteome