Prediction of Catalytic Residues in Protein Active Site by SVM
A Structure Based Drug Designing Approach
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In this study,prediction of physiochemical properties of catalytic sites residues using a suitable Artificial Neural Networking (ANN) Backpropagation algorithm coupled with a set of structural proteins with the properties of their amino acid residues. The method has been applied to a set of 100 structural proteins from the Protein Data Bank (PDB.Using Ligplot program for searching of active site residues the identified amino acid residues were classified in 15 different categories based on their physiochemical properties. After classification of active and non active site amino acids, their pr...
In this study,prediction of physiochemical properties of catalytic sites residues using a suitable Artificial Neural Networking (ANN) Backpropagation algorithm coupled with a set of structural proteins with the properties of their amino acid residues. The method has been applied to a set of 100 structural proteins from the Protein Data Bank (PDB.Using Ligplot program for searching of active site residues the identified amino acid residues were classified in 15 different categories based on their physiochemical properties. After classification of active and non active site amino acids, their properties were converted into machine language. Furthermore, we created Neural Network Using Matlab software and generated algorithm for training and testing of data. Thereafter, analysis of results showed that 95% of active site s physiochemical properties were correctly predicted. It is hoped that this work would help in determining the surface topographic properties for ligand binding sites residues in protein. The computational outcome would be helpful in ligand designing,and structural identification and functional sites Comparison.
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