The tertiary structure of protein is difficult to predict accurately directly from a protein sequence. The intermediate step is required to predict the structure which project the one dimensional structure into the three dimensional structure. This intermediate step is called secondary structure of protein. The secondary structure of protein plays a key role in the designing of drugs. There are many machine learning algorithms such as HMM (hidden markov model), SVM (Support vector machine), NN (neural network), Fuzzy Logic. A technique which we used to predict the secondary structure of protein is Support Vector Machine (SVM) with Hidden markov transition encoding matrix. Support vector machine is a supervised machine learning method and is based on the principle of the structural risk minimization. The concept of SVM is based on the construction of hyper plane in the high dimensional space to classify the data into the categories. The main objective is to increase the accuracy and decrease the error of prediction.

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Data Set Kernel Function Markov Transition Encoding Scheme Secondary Structure Support Vector Machine.