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

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.

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