Molecular modeling tools for protein structures study - Aphafold applications

Recent advances in artificial intelligence have transformed protein structure prediction, making high-quality structural models and complex predictions accessible at unprecedented speed. However, for many biologically and pharmacologically relevant systems, especially membrane proteins, predicted structures remain static hypotheses rather than functionally resolved molecular states. This work examines the strengths and limitations of AlphaFold-based and related deep-learning approaches in protein modeling, with a focus on their use in structure-function analysis, interaction prediction, and drug-discovery workflows. AI-based methods provide powerful first-draft models, sequence-derived representations, and co-folding predictions for proteins, ligands, nucleic acids, and complexes, but their outputs must be interpreted critically because confidence scores may not capture errors in domain orientation, membrane topology, physical validity, or conformational state. These limitations become particularly relevant when predicted structures are used as input features for machine-learning models of membrane-protein folding classes, protein-protein interactions, and protein-ligand binding. Results based on ESM-derived embeddings show strong predictive performance for folding-class classification and interaction tasks, whereas the addition of AlphaFold-derived surface descriptors can reduce accuracy when the predicted conformation does not represent the biologically active state.