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AMS 3.0: prediction of post-translational modifications.

Basu S, Plewczynski D - BMC Bioinformatics (2010)

Bottom Line: The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs.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.The standalone version of AMS 3.0 presents an efficient way to identify post-translational modifications for whole proteomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata - 700032, India.

ABSTRACT

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.

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AUC values for predictors. Comparison of scope of AUC best values for the kinase families PKA, PKC, CDK and CK2, using AMS3, GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor.
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Figure 3: AUC values for predictors. Comparison of scope of AUC best values for the kinase families PKA, PKC, CDK and CK2, using AMS3, GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor.

Mentions: Many researchers concentrated their efforts on prediction of four major kinase families, namely CDK, CK2, PKA and PKC. To compare the current technique with the existing ones, we have conducted detailed experiments with those four kinase families from the latest Phospho.ELM dataset. Figure 2 (a-b) shows scopes of AUCs for these four kinase families for the train and test datasets of AMS3. In the current work we have compared the performance of AMS3 with the existing state-of-the-art prediction systems for phosphorylation sites in protein sequences. Figure 3 shows a comparative analysis of the current technique with standard predictors, namely GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor. Table 3 lists the comparative performances, i.e. sensitivity, specificity and accuracy, of aforementioned prediction systems (and their variations) with the current one. In another comparison, we have plotted the ROC curves of the four kinase families Figure 4(a-d) for different runs of trainset/testset results for AMS3 and compared the same with claimed ROC values of the standard predictors.


AMS 3.0: prediction of post-translational modifications.

Basu S, Plewczynski D - BMC Bioinformatics (2010)

AUC values for predictors. Comparison of scope of AUC best values for the kinase families PKA, PKC, CDK and CK2, using AMS3, GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2874555&req=5

Figure 3: AUC values for predictors. Comparison of scope of AUC best values for the kinase families PKA, PKC, CDK and CK2, using AMS3, GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor.
Mentions: Many researchers concentrated their efforts on prediction of four major kinase families, namely CDK, CK2, PKA and PKC. To compare the current technique with the existing ones, we have conducted detailed experiments with those four kinase families from the latest Phospho.ELM dataset. Figure 2 (a-b) shows scopes of AUCs for these four kinase families for the train and test datasets of AMS3. In the current work we have compared the performance of AMS3 with the existing state-of-the-art prediction systems for phosphorylation sites in protein sequences. Figure 3 shows a comparative analysis of the current technique with standard predictors, namely GPS, KinasePhos, NetPhosK, PPSP, PredPhospho, Scansite and Meta Predictor. Table 3 lists the comparative performances, i.e. sensitivity, specificity and accuracy, of aforementioned prediction systems (and their variations) with the current one. In another comparison, we have plotted the ROC curves of the four kinase families Figure 4(a-d) for different runs of trainset/testset results for AMS3 and compared the same with claimed ROC values of the standard predictors.

Bottom Line: The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs.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.The standalone version of AMS 3.0 presents an efficient way to identify post-translational modifications for whole proteomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata - 700032, India.

ABSTRACT

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.

Show MeSH