  The Gene or DNA sequence in every cell does not control genetic properties on
its own; Rather, this is done through translation of DNA into protein and
subsequent formation of a certain 3D structure. The biological function of a
protein is tightly connected to its specific 3D structure. Prediction of the
protein secondary structure is a crucial intermediate step towards elucidating
its 3D structure and function. Traditional experimental methods for prediction
of protein structure are expensive and time-consuming. Therefore, various
machine learning approaches have been proposed to predict the protein secondary
structure. Nevertheless, the average accuracy of the suggested solutions has
hardly reached beyond 80%. The possible underlying reasons are the ambiguous
sequence-structure relation, noise in input protein data, class imbalance, and
the high dimensionality of the encoding schemes that represent the protein
sequence. In this paper, we propose an accurate multi-component prediction
machine to overcome the challenges of protein structure prediction. We devise a
multi-component designation to address the high complexity challenge in
sequence-structure relation. Furthermore, we utilize a compound string
dissimilarity measure to directly interpret protein sequence content and avoid
information loss. In order to improve the accuracy, we employ two different
classifiers including support vector machine and fuzzy nearest neighbor and
collectively aggregate the classification outcomes to infer the final protein
secondary structures. We conduct comprehensive experiments to compare our model
with the current state-of-the-art approaches. The experimental results
demonstrate that given a set of input sequences, our multi-component framework
can accurately predict the protein structure. Nevertheless, the effectiveness
of our unified model an be further enhanced through framework configuration.
