The prediction of the tertiary structure of a protein from its primary sequence is a long-standing problem for life science researchers. Solutions to this problem have a direct impact on other research areas such as design and discovery of new drugs, understanding the sequenced genomic data, solving the puzzle of protein structure, and understanding the folding mechanism. Solving protein structure prediction (PSP) is considered a cornerstone for life science research to reveal the mechanism of cellular processes; it has been an open problem for past six decades. Globally, various research institutes are working toward a solution to PSP with different methods. In this paper, we present a review of de novo PSP using hydrophobic-polar lattice model. In the hydrophobic-polar model, the PSP problem is defined as a combinatorial optimization problem with an objective of finding the protein structure having the lowest free energy. This study is bounded to two key factors. The first is application of the evolutionary search algorithms for PSP, and the second is the evolution of the modeling method and its corresponding potential energy function. The goal of this paper is to give sufficient biological background to understand the fundamentals of proteins, their structure, and available computational methods.

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