ABSTRACT
Amino acid substitutions in proteins due to gene mutations play a crucial role in the evolutionary process, enhancing an organism’s adaptability and survival. The main objective of these substitutions is to improve the protein’s functionality and structural integrity. To assess the impact of amino acid changes on protein function, structural stability, and drug binding, various computational and experimental methods are employed. This study aims to explore the relationship between atomic fluctuations and positional variability to identify mutational hotspots. We propose an empirical score that combines atomic fluctuations, measured as “root-mean-squared fluctuations,” with amino acid variability, calculated using “Wu-Kabat’s coefficient of variability,” which can lead to drug resistance. This score has been developed and computationally validated using two well-studied HIV-1 protease inhibitors, nelfinavir and darunavir. Additionally, we applied this method to anaplastic lymphoma kinase (ALK) and its inhibitor, crizotinib, to evaluate its effectiveness. The predictions are accurate for amino acids that directly interact with the inhibitors. One of the advantages of this approach is its computational efficiency compared to free energy-based methods. However, the predictions are less reliable for amino acids that do not directly bind to the inhibitors, and the current methodology does not account for double or triple mutants. Despite these limitations, the simplicity of the method makes it appealing for applications beyond identifying mutational hotspots.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version of the manuscript has been revised significantly including the title based on the suggestion of the reviewers.