AI-Based Protein Engineering: Accelerating the Design of Novel Biomolecules for Healthcare and Industry

Monday Mbila

AI-Based Protein Engineering: Accelerating the Design of Novel Biomolecules for Healthcare and Industry

Author(s)	Monday Mbila
Country	United States
Abstract	Artificial intelligence (AI) is transforming protein engineering by enabling researchers to predict, design and optimise biomolecules with unprecedented speed and scale. Traditional protein engineering relies heavily on iterative cycles of mutation, expression, screening and experimental validation, which can be expensive and time-consuming when exploring the enormous sequence space of proteins. Recent advances in deep learning, protein language models, structure prediction, generative AI and computational protein design have created new opportunities for rationally designing proteins with desired structural, catalytic and functional properties. This paper examines the emerging role of AI in protein engineering, focusing on sequence-function prediction, structure modelling, de novo protein design, directed evolution, enzyme optimisation, therapeutic protein development and industrial biotechnology. The paper discusses how AI models can integrate protein sequences, structures, evolutionary information and experimental measurements to identify promising candidates before laboratory testing. Applications in drug discovery, vaccines, diagnostics, biocatalysis, food technology, biomaterials and sustainable manufacturing are explored. Particular attention is given to AI-assisted workflows that combine computational design with high-throughput experimentation and active learning. The paper also considers challenges related to training-data quality, protein-function generalisation, experimental validation, interpretability, model bias and biological safety. An integrated AI-protein engineering framework is proposed in which generative models produce candidate sequences, predictive models estimate their properties, laboratory experiments validate selected candidates and experimental results continuously improve the computational models. The study argues that AI is shifting protein engineering from predominantly trial-and-error optimisation towards data-driven and increasingly programmable biomolecular design. The convergence of AI, synthetic biology, structural biology and automation could therefore establish a new engineering paradigm for creating biomolecules tailored to specific healthcare, industrial and environmental applications.
Keywords	Artificial Intelligence, Protein Engineering, Protein Design, Generative AI, Protein Language Models, Enzyme Engineering, Computational Biology, Synthetic Biology, Drug Discovery, Biomolecular Engineering.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-28

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Based Protein Engineering: Accelerating the Design of Novel Biomolecules for Healthcare and Industry

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