“A substitution of one amino acid by another in a protein can have effects ranging from negligible to dramatic, either locally or distantly.” (Bikker et al., 1998)
Quantitative studies of how drugs modulate their targets have been instrumental in discovering new treatments. A rational drug discovery campaign hinges on a deep understanding of how distinct molecules interact with receptors at the molecular level. Fortunately, several techniques and methodologies have been developed to support this process. Notably, innovations in structural biology, such as X-ray crystallography and cryo-electron microscopy, have successfully elucidated ligand-receptor interactions. For example, Carlsson et al. (2010) utilised structural information and virtual ligand screening to discover novel ligands for the A2A adenosine receptor.
While these structural techniques offer significant advantages in drug discovery, no single method can fully capture the impact of residue alterations on ligand function and ligand-receptor interactions. But why is this information crucial? Kosar et al. (2024) addressed this question by using the role of the Trp2586.48 residue to guide and explain the discovery of a novel inverse agonist at the cannabinoid receptor 2. Furthermore, data on the role of specific residues within receptors can provide valuable insights for structure-activity relationship (SAR) studies, informing the development of ligands tailored to exploit available binding pocket space. In this context, mutagenesis has emerged as an "old-school" but essential technology to fill these knowledge gaps.
Mutagenesis involves deliberately altering the DNA sequence of a gene to study the resulting changes in protein function. Through either random or targeted (site-directed) approaches, mutagenesis can provide a comprehensive understanding of how drugs interact with different receptor regions and link these interactions to functional variations. This technique has uncovered fundamental aspects of the G protein-coupled receptor (GPCR) function, including specific ligand interactions, such as those at the adenosine A1 adenosine receptor (Nguyen et al., 2016). While advancements in site-directed technology have largely supplanted random mutagenesis, the latter has seen a resurgence in recent times for exploratory analyses to identify previously unknown regions critical to GPCR function. For example, random mutagenesis, combined with novel specificity high-throughput selection, has been used to identify specificity-determining positions without prior knowledge of structure or sequence homology (Di Roberto et al., 2017).
However, several challenges have limited the use of mutagenesis in drug discovery. Targeted mutagenesis may miss broader functional regions, while random mutagenesis generates extensive data that require labour-intensive screening. Furthermore, probing species- or subtype-selectivity introduces complexities due to the differing contexts of receptor subtypes across species. Integrating mutagenesis data with computational models also presents challenges, requiring high-quality data and robust algorithms. Moreover, these studies are resource-intensive, necessitating significant investments in technology and infrastructure.
To overcome these challenges, future directions include developing high-throughput techniques, better integrating experimental and computational methods, and improving frameworks for data interpretation. Advances in bioinformatics and machine learning will play a pivotal role in addressing these obstacles, enhancing our understanding of GPCR structure and function. For instance, Heydenreich et al. (2023) developed a data science framework that combined mutagenesis and structural data to contextualise ligand-induced structural changes and reveal the principles underlying efficacy and potency. Additionally, deep mutational scanning can generate large-scale datasets, allowing researchers to determine the functional consequences of every possible amino acid change at each position in a protein, as well as the biochemical activity of hundreds of thousands of protein variants (Fowler and Fields, 2014).
In summary, mutagenesis can be a critical tool in drug discovery, particularly for studying GPCRs. While advancements in structural biology techniques like X-ray crystallography and cryo-electron microscopy have deepened our understanding of ligand-receptor interactions, mutagenesis fills crucial knowledge gaps by revealing how specific amino acid changes impact receptor function. Despite its significant contributions, challenges such as data complexity, resource intensity, and integration with computational models remain. Future advancements, including high-throughput techniques and bioinformatics integration, will further enhance the utility of mutagenesis in drug discovery.
Reference
Bikker, J. A., Trumpp-Kallmeyer, S., & Humblet, C. (1998). G-protein coupled receptors: models, mutagenesis, and drug design. Journal of Medicinal Chemistry, 41(16), 2911-2927.
Carlsson, J., Yoo, L., Gao, Z. G., Irwin, J. J., Shoichet, B. K., & Jacobson, K. A. (2010). Structure-based discovery of A2A adenosine receptor ligands. Journal of Medicinal Chemistry, 53(9), 3748-3755.
Di Roberto, R. B., Chang, B., & Peisajovich, S. G. (2017). The directed evolution of ligand specificity in a GPCR and the unequal contributions of efficacy and affinity. Scientific Reports, 7(1), 16012.
Fowler, D. M., & Fields, S. (2014). Deep mutational scanning: a new style of protein science. Nature Methods, 11(8), 801-807.
Heydenreich, F. M., Marti-Solano, M., Sandhu, M., Kobilka, B. K., Bouvier, M., & Babu, M. M. (2023). Molecular determinants of ligand efficacy and potency in GPCR signaling. Science, 382(6677), eadh1859.
Kosar, M., Sarott, R. C., Sykes, D. A., Viray, A. E., Vitale, R. M., Tomašević, N., ... & Carreira, E. M. (2024). Flipping the GPCR switch: Structure-based development of selective cannabinoid receptor 2 inverse agonists. ACS Central Science, 10(5), 956-968.
Nguyen, A. T., Baltos, J.-A., Thomas, T., Nguyen, T. D., Muñoz, L. L., Gregory, K. J., White, P. J., Sexton, P. M., Christopoulos, A., & May, L. T. (2016). Extracellular loop 2 of the adenosine A1 receptor has a key role in orthosteric ligand affinity and agonist efficacy. Molecular Pharmacology, 90(6), 703-714.
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