Modern Approaches to Protein Constructions: A Comprehensive Review of Computational Tools and Databases for De Novo Protein Design and Engineering

Abstract

The field of protein engineering has witnessed transformative advancements, with computational tools and databases driving novel innovations in de novo protein design. This review consolidates and critiques a comprehensive range of modern computational resources, offering a unique focus on their applications across diverse domains, including protein stability prediction, posttranslational modification analysis, and mutation effect evaluation. Key contributions include a detailed examination of tools integrating machine learning and artificial intelligence to enhance predictive accuracy and streamline protein engineering workflows. By highlighting underexplored tools and novel methodologies, such as advanced protein–ligand interaction predictors and neural network–based stability assessment models, this study establishes itself as a unique reference for researchers aiming to develop tailored proteins for therapeutic, industrial, and biomedical applications.