The Role of Artificial Intelligence in Protein Structural and Functional Prediction: Current Status and Future Prospective

Artificial intelligence (AI) has transformed the landscape of protein structural and functional prediction, significantly advancing the accuracy and efficiency of these processes. Currently, AI-driven methods, especially deep learning algorithms, enable the prediction of protein 3D structures from amino acid sequences with unprecedented precision. Artificial intelligence (AI) has emerged as a transformative force in the field of protein science, offering powerful tools for the structural and functional prediction of proteins. AI models use vast databases of known protein structures and leverage evolutionary information from multiple sequence alignments or protein language models to infer spatial conformations of proteins. Deep neural networks, convolutional neural networks, and graph-based models enhance prediction accuracy beyond traditional homology or ab initio methods. AlphaFold2’s breakthrough in CASP14 demonstrated near-experimental accuracy for many proteins, ushering in a new era of AI-based structural biology. AI-driven protein structure and function prediction tools are democratizing access to complex biological data, making it possible for many research groups to accelerate discovery without expensive and time-consuming experiments. Machine learning models, such as DeepGO-SE, utilize pretrained protein language models alongside biological knowledge and protein interaction networks to predict Gene Ontology functions. These models improve prediction accuracy even for proteins with unknown interactions. This review discusses the latest advancements in AI-driven methodologies, including deep learning models and large language models, highlighting their significant contributions to resolving protein structures, functional annotation, and interaction mapping. The article summarizes current achievements, evaluates the strengths and limitations of AI approaches, and outlines future prospects for integrating AI with experimental data to accelerate discoveries in proteomics and drug discovery.