Integrating Computational Design and Experimental Approaches for Next-Generation Biologics
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Abstract
Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.
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Flagged first, then by citations
- Authors
- Son, A., Park, J., Kim, W., Lee, W., Yoon, Y., Ji, J., & Kim, H.
- Affiliations
- Scripps Research Institute (US)
- Chungnam National University (KR)
- Corresponding author
- Kim, Hyunsoo
- Venue
- Biomolecules
- Publisher
- MDPI AG
- Published
- 2024-08-27
- Volume
- 14
- Issue
- 9
- Pages
- 1073
- ISSN
- 2218-273X (ISSN-L)
- PMC
- PMC11430650
- OpenAlex
- W4401906740
- Cited by
- 29
- References
- 18
- Citations by year
- 2026: 18 · 2025: 11
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biomolecules (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- BK21 FOUR Program by Chungnam National University Research (RS-2023-00209456, RS-2024-00402298, RS-2022-00155857)
- Korea government (MSIT) (RS-2023-00209456, RS-2024-00402298, RS-2022-00155857)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- antibody engineering, enzyme replacement therapy, structure-based design, protein language models, directed evolution, rational protein design, affinity maturation, bispecific antibodies, enzyme stability, intracellular protein delivery, de novo protein design, immunogenicity, targeted delivery, biopharmaceutical manufacturing
- MeSH terms
- Animals, Biological Products, Humans, Drug Design, Protein Engineering, Antibodies, Bispecific, Computational Biology
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