The study of protein folding has led to and will continue to provide many breakthroughs in the fields of health, medicine, and agriculture. A process of determining the lowest energy conformation of a protein sequence is often aided by computational optimization strategies such as the Ant Colony Optimization metaheuristic. Changes were made to how the ACO metaheuristic behaves in an attempt to improve solution quality and time efficiency. The results strongly indicate no improvements were made as acceptable energy states and run times were not achieved. In conclusion, normalization of pheromone and heuristic values is of the highest priority when implementing an ACO metaheuristic. Scaling factors associated with pheromone and heuristic values can be fine tuned to provide significant increases in optimization efficiency.