Computer-Aided Technology for Bioactive Protein Design and Clinical Application

Abstract

Computer-aided protein design (CAPD) has become a transformative field, harnessing advances in computational power and deep learning to deepen the understanding of protein structure, function, and design. This review provides a comprehensive overview of CAPD techniques, with a focus on their application to protein-based therapeutics such as monoclonal antibodies, protein drugs, antigens, and protein polymers. This review starts with key CAPD methods, particularly those integrating deep learning-based predictions and generative models. These approaches have significantly enhanced protein drug properties, including binding affinity, specificity, and the reduction of immunogenicity. This review also covers CAPD's role in optimizing vaccine antigen design, improving protein stability, and customizing protein polymers for drug delivery applications. Despite considerable progress, CAPD faces challenges such as model overfitting, limited data for rare protein families, and the need for efficient experimental validation. Nevertheless, ongoing advancements in computational methods, coupled with interdisciplinary collaborations, are poised to overcome these obstacles, advancing protein engineering and therapeutic development. In conclusion, this review highlights the future potential of CAPD to transform drug development, personalized medicine, and biotechnology.