Abstract
As the United States continues to become more technologically advanced, a shift in the growing reliance on computational tools for modeling and drug design has increasingly become more prevalent [1]. The research project is aimed to address the accuracy of the molecular visualization system PyMOL surrounding protein-ligand interactions (specifically lactotransferrin) and its effectiveness of predictions, therefore, ensuring its reliability for in-depth gene research and its structural modeling dexterity. The gap that this experiment is addressing is the effectiveness of PyMOL based on its ability to simulate protein-ligand interactions and predict binding affinities in proteins, specifically regarding the hydrogen bond relationship between lactoferrin and iron. I will be comparing H-bond experimental data derived from sources that have been attained via a systematic review and H-bond data that comes from simulations produced from PyMOL, in which the level of error and accuracy will then be measured using the percent error equation and the statistical difference found by conducting a paired t-test. After comparison, my findings depict that PyMOL resulted in below the set threshold of 90% percent accuracy in order to be considered accurate, this being 86.81% accuracy, however, the paired t-test revealed that the difference between the simulated and experimental values was not significantly different. This indicates that although PyMOL did not reach the set threshold of accuracy, 90%, the deviation from the experimental values is small enough that the difference witnessed could be due to natural variation rather than a consistent level of error within the system.
