alexa Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines

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Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines

Assessing the quality of a protein structure model is essential for protein structure prediction. Here, we developed a Support Vector Machine (SVM) method to predict the quality score (GDT-TS score) of a protein structure model from the features extracted from the sequence alignment used to generate the model. We developed a Support Vector Machine (SVM) model quality assessment method, taking either a query-single-template pairwise alignment or a query-multitemplate alignment as input. For the pairwise alignment scheme, the input features fed into the SVM predictor include the normalized e-value of the given alignment, the percentage of identical residue pairs in the alignment, the percentage of residues of the query aligned with those of the template, and the sum of the BLOSUM scores of all aligned residues divided by the length of the aligned positions. Similarly, for the multiple-alignment scheme, the input features include the percentage of the residues of the target sequence aligned with those in one or more templates, the percentage of aligned residues of the target sequence that are the same as that of any one template, the average BLOSUM score of aligned residues and the average Gonnet160 score of aligned residues. A SVM regression predictor was trained on the training data to predict the GDT-TS scores of the models from the input features. The Root Mean Square Error (RMSE) and the Absolute Mean Error (ABS) between predicted and real GDT-TS scores were calculated to evaluate the performance.


For More Information :

Deng X, Li J, Cheng J (2013) Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines. J Proteomics Bioinform S9:001

 
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