Modeling of All Mutant and Wild Protein Structures Using Metaverse ESM: Surface Area Analysis and Implications

The integration of virtual reality and advanced modeling platforms in the Metaverse has revolutionized the way biochemical data is visualized and analyzed. Specifically, the Evolutionary Scale Modeling (ESM) within the Metaverse provides an innovative environment for simulating and examining protein structures, allowing for both mutant and wild type analyses. Traditional bioinformatics tools often require substantial computational resources and can be limited in interactive capabilities, posing challenges in rapidly modeling variations in protein structures, especially for educational and research purposes in the Metaverse. This study exploits the capabilities of the Metaverse ESM to generate and analyze surface area models of all mutant and wild protein structures. We applied logistic regression, a robust machine learning method, to classify residues based on their surface area characteristics such as total, apolar, backbone, and sidechain areas. This approach facilitated a streamlined analysis directly within the Metaverse platform, enhancing accessibility and interactive learning. Our model achieved perfect classification metrics-accuracy, precision, recall, and F1 score of 1.0 - highlighting the effectiveness of combining Metaverse ESM tools with machine learning. The ROC curve further demonstrated the model's exceptional discriminative ability with an AUC of 1.0, supported by a clear and accurate confusion matrix. The successful implementation of logistic regression for protein surface analysis within the Metaverse showcases the potential for these technologies to simplify and enhance biochemical education and research. This paves the way for broader applications of virtual reality in scientific studies, making complex molecular biology concepts more accessible and engaging through immersive experiences.