A classical lay summary by Dr. Shubham Vishnoi, from the University of Limerick.
The biopharmaceutical industry has begun to capitalize on recent advances in computational modelling, particularly supported by artificial intelligence, machine learning, and with the help of high-performance computing facilities. These advancements enable access to the as-yet unexplored molecular chemical space for biomolecular engineering and drug discovery.
Using advanced computer technology, researchers are designing peptides that could become powerful medicines for illnesses like diabetes and cancer. These peptides are made up of amino acids, the building blocks of proteins. They are like keys that fit perfectly into locks within our bodies, targeting specific disease processes. While computational peptide design is like using a computer to design a key that fits perfectly into a lock, instead of opening a door, it could unlock new treatments for various health problems (Vanhee et al. 2011).
Researchers use special software and algorithms to predict how these peptides will behave and interact with other molecules in the body. By precisely engineering novel peptides, researchers can design peptide molecules that target specific biomolecular pathways involved in these diseases. For example, in cancer, these peptides could be tailored to attack cancer cells while leaving healthy cells unharmed. In diabetes, peptides could be designed to regulate insulin production or improve glucose metabolism. Similarly, peptides could also be developed as sustainable alternatives to conventional therapeutics for G protein-coupled receptor (GPCR)-linked disorders, promising biocompatible and tailorable next-generation therapeutics for metabolic disorders including type-2 diabetes (Vishnoi et al. 2023). This approach offers hope for more effective and personalized treatments for these serious health conditions.
To sum up, computationally designed peptides could pave the way for further investigation, leading to the development of a better array of medicines with fewer side effects and offering personalized treatments. This in silico technique is not limited to modelling a specific class of peptides and has applicability in generating designs with a wide variety of peptides, functioning in combination with various amino acid representations.
References
Vanhee, P., van der Sloot, A.M., Verschueren, E., Serrano, L., Rousseau, F. and Schymkowitz, J. (2011) 'Computational design of peptide ligands', Trends in Biotechnology, 29(5), 231-239, available: http://dx.doi.org/10.1016/j.tibtech.2011.01.004.
Vishnoi, S., Bhattacharya, S., Walsh, E.M., Okoh, G.I. and Thompson, D. (2023) 'Computational Peptide Design Cotargeting Glucagon and Glucagon-like Peptide-1 Receptors', Journal of Chemical Information and Modeling, 63(15), 4934-4947, available: http://dx.doi.org/10.1021/acs.jcim.3c00752.