This thesis presents our research on protein structure prediction on discrete lattices. Given a protein’s amino acid sequence, the protein structure prediction problem is to find its three dimensional native structure that has the minimum free energy. Knowledge about the native protein structures and their respective folding process is a key to understand protein functionalities and consequently the basics of life. Protein structure prediction problem is one of the most challenging problems in molecular biology. In-vitro laboratory methods applied to this problem are very time-consuming, cost- expensive and failure-prone. Also, the search based optimization methods used are com- putationally very expensive. To tackle these, researchers have used various simplified models, such as low resolution energy functions and lattice-based structures, and applied incomplete local search methods on them. The simplified models help obtain back-bone structures first and then hierarchically work out the details. Local search methods can normally quickly find solutions although they suffer from re-visitation and stagnancy, and require good heuristics. In the literature, researchers have mostly used primitive ap- proaches based on random decisions at various choice points. Consequently, these methods are applicable to small-sized proteins only. In this thesis, we present a number of techniques to improve the performance of lo- cal search methods applied to protein structure prediction problem using discrete lattices. Firstly, we propose a memory based local search framework that maintains a set of already explored solutions for avoiding re-visitation and stores previously unexplored but promi- nent solutions for restarting to handle stagnation. A novel encoding scheme for protein structures is proposed to handle symmetry present in the search space. We also propose an approximate matching strategy that results in reducing redundancy in the search space.