REVOLUTIONIZING STRUCTURAL BIOLOGY: ARTIFICIAL INTELLIGENCE (AI) APPROACHES FROM PROTEIN SEQUENCE TO FUNCTION

Abstract

Artificial intelligence (AI) is revolutionizing protein science and transforming the fields of analytical and bioanalytical chemistry by harnessing advanced machine learning and deep learning techniques to address longstanding challenges. AI can now predict protein structures from amino acid sequences with near-experimental accuracy, as exemplified by breakthroughs such as AlphaFold2, significantly enhancing our understanding of protein function, dynamics, and interactions. In analytical chemistry, AI enables high-throughput protein characterization, structural analysis, and real-time data interpretation. In bioanalytical chemistry, it supports precise biomarker identification, protein quantification, and modeling of complex protein–protein interactions. Beyond structure prediction, AI accelerates the design of novel proteins and enzymes, facilitates proteomic data analysis for biomarker discovery, and aids drug development. While challenges remain in modeling dynamic systems and intrinsically disordered regions, the integration of AI promises to revolutionize analytical and bioanalytical methodologies, improve precision, and drive innovations in drug discovery, synthetic biology, and personalized medicine, positioning AI as a cornerstone of modern protein research.