In our study, we have proposed a hybrid method using SVM in conjunction with the PSSM features for prediction of DNA-binding sites in proteins from amino acid sequences by achieving high accuracy for novel proteins. Using the same PSSM features, simulation results show that our method SVM-PSSM is better than fuzzy k-NN method and much better than the existing neural network based method in terms of net prediction (NP) accuracy by increasing the NP values for training and test accuracies up to 13.45% and 16.53%, respectively. Although previous researches proposed that amino acids physico-chemical properties such as ASA, electric charge, and hydropathy are related to DNA-proteins interactions, when using PSSM combines these physico-chemical properties as features, they only keep the original performance. It seems that the further well design of combining PSSM and physico-chemical properties features are needed to enhance the performance.
To best of our knowledge, up to now, the proposed method is the most effective method for recognizing mechanism of binding residues in proteins based on protein sequence without using 3D structural information, such as hydrogen bond, hydrophobic, hydrophilic, ion interaction, etc. By adjusting the cut-off value of the SVM classifier, the proposed prediction method would be helpful to biologist for filtering novel proteins without significant homology with known protein to find out the potential binding regions in proteins.
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