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The Gene or DNA sequence in every cell does not control genetic properties onits own; Rather, this is done through translation of DNA into protein andsubsequent formation of a certain 3D structure. The biological function of aprotein is tightly connected to its specific 3D structure. Prediction of theprotein secondary structure is a crucial intermediate step towards elucidatingits 3D structure and function. Traditional experimental methods for predictionof protein structure are expensive and time-consuming. Therefore, variousmachine learning approaches have been proposed to predict the protein secondarystructure. Nevertheless, the average accuracy of the suggested solutions hashardly reached beyond 80%. The possible underlying reasons are the ambiguoussequence-structure relation, noise in input protein data, class imbalance, andthe high dimensionality of the encoding schemes that represent the proteinsequence. In this paper, we propose an accurate multi-component predictionmachine to overcome the challenges of protein structure prediction. We devise amulti-component designation to address the high complexity challenge insequence-structure relation. Furthermore, we utilize a compound stringdissimilarity measure to directly interpret protein sequence content and avoidinformation loss. In order to improve the accuracy, we employ two differentclassifiers including support vector machine and fuzzy nearest neighbor andcollectively aggregate the classification outcomes to infer the final proteinsecondary structures. We conduct comprehensive experiments to compare our modelwith the current state-of-the-art approaches. The experimental resultsdemonstrate that given a set of input sequences, our multi-component frameworkcan accurately predict the protein structure. Nevertheless, the effectivenessof our unified model an be further enhanced through framework configuration.

MCP: a Multi-Component learning machine to Predict protein secondary structure