MCP: a multi-component learning machine for prediction of protein secondary structure based on d-FKNN and edit-SVM

The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through translation of this sequence into protein and formation of a certain 3D structure. Proteins biological function is tightly connected to its specific 3D structure. Prediction of protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein secondary structure are expensive and time-consuming. Therefore, in the past 45 years, various machine learning approaches have been put forth. Nevertheless, their average accuracy has hardly reached beyond 80%. The possible underlying reasons are abstruse sequence structure relation, noise, class imbalance and high dimensionality of encoding schemes which represent protein sequences. In this paper, we have developed an accurate multi-component prediction machine to overcome challenges of protein secondary structure prediction. The principal tenet behind the proposed approach is to directly process amino-acid sequences to reveal deeper and, simultaneously, more biologically meaningful sequence structure relation. Taking this approach, it is possible to prevent losing rich information hidden in sequence data, which is biologically believed to be sufficient for structure adoption. Additionally, it facilitates resolving the high dimensionality of the numerical representation for protein sequences. Moreover, the multi-component designation can better address the high complexity of the relation between sequence and structure. To pursue these objectives, we have employed various …