A Protein is a large molecule that consists of a vast number of atoms; one can only imagine the complexity of such a molecule. Protein is a series of amino acids that bind to each other to form specific sequences known as peptide chains. Proteins fold into three-dimensional conformations (or so-called protein’s native structure) to perform their functions. However, not every protein folds into a correct structure as a result of mutations occurring in their amino acid sequences. Consequently, this mutation causes many protein misfolding diseases. Protein folding is a severe problem in the biological field. Predicting changes in protein stability free energy in relation to the amino acid mutation (ΔΔG) aids to better comprehend the driving forces underlying how proteins fold to their native structures. Therefore, measuring the difference in Gibbs free energy provides more insight as to how protein folding occurs. Consequently, this knowledge might prove beneficial in designing new drugs to treat protein misfolding related diseases. The protein-energy profile aids in understanding the sequential, structural, and functional relationship, by assigning an energy profile to a protein structure. Additionally, measuring the changes in the protein-energy profile consequent to the mutation (ΔΔE) by using an approach derived from statistical physics will lead us to comprehend the protein structure thoroughly. In this work, we attempt to prove that ΔΔE values will be approximate to ΔΔG values, which can lead the future studies to consider that the energy profile is a good predictor of protein binding affinity as Gibbs free energy to solve the protein folding problem.