Predicting protein-ligand binding site with differential and support vector machine

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
International Joint Conference on Neural Networks, 2012, pp. 2724 - 2729
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012001724OK.pdf Published version399.78 kB
Adobe PDF
Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. When the scores are calculated from these methods, the method of classification is very important and can affect the prediction results greatly. A developed support vector machine (SVM) is used to classify the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential, offset from protein, conservation score and the information around the pockets. Since SVM is sensitive to the input parameters and the positive samples are more relevant than negative samples, differential evolution (DE) is applied to find out the suitable parameters for SVM. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.
Please use this identifier to cite or link to this item: