In a recent study published in the journal Advances in Scienceresearchers in Sweden conducted virtual screens of more than 16 million compounds using multi-receptor models developed by AlphaFold and homology modeling techniques. These models were based on different protein structures to identify trace amine-associated receptor 1 (TAAR1) agonists for the potential treatment of various neuropsychiatric conditions. They found that the AlphaFold-based screen had a higher success rate and helped discover potent TAAR1 agonists, leading to a promising drug candidate that showed physiological effects in mice.
Study: AlphaFold accelerates discovery of psychotropic agonists targeting trace amine-related receptor 1. Image credit: Corona Borealis Studio / Shutterstock
Background
The advent of machine learning methods, including AlphaFold, has revolutionized protein structure prediction, achieving near-experimental accuracy and providing models for many therapeutically relevant proteins, such as G protein-coupled receptors (GPCRs). This has generated significant interest in the use of AlphaFold models for drug design, as access to accurate protein structures can potentially accelerate drug discovery. However, studies comparing AlphaFold with experimentally determined GPCR structures have shown mixed results for the effectiveness of AlphaFold in predicting GPCR-drug complexes. Although AlphaFold can model binding sites with high accuracy, these studies highlighted that the predicted ligand binding modes often differed from those derived from experimentally determined structures. While AlphaFold is reported to model binding sites with high accuracy, performance in binding simulations and virtual views often lags behind experimentally determined structures. This discrepancy suggests that while AlphaFold can outperform traditional homology models in some aspects, it still requires further improvement to accurately predict dynamic protein-ligand interactions. These findings suggest that while AlphaFold is superior to traditional homology models, it may not yet be completely suitable for structure-based drug design, highlighting the need for further optimization of these models to improve their accuracy in predicting protein interactions -ligand.
TAAR1, a GPCR with no experimental structure available at the time of the study, was the primary focus of this research because of its potential as a drug target. The researchers aimed to investigate the effectiveness of AlphaFold models in structure-based virtual screening, particularly for TAAR1 agonists, and to compare these results with traditional homology modeling techniques.
About the study
To assess the effectiveness of AlphaFold versus homology models in identifying TAAR1 ligands, the researchers generated multiple models for TAAR1 using both techniques and conducted two comprehensive structure-based virtual screens. These screens involved docking a library of 16 million fragment-like compounds, assessing their potential as TAAR1 ligands based on docking scores and predicted binding modes. The performance of these models was compared based on their ability to enrich known TAAR1 ligands and predict accurate receptor-agonist complexes. Docking screens included the evaluation of 218 trillion complexes, with 6.8 million compounds successfully docked in AlphaFold models and 11.3 million in homology models.
The research focused on analyzing the structural differences between the AlphaFold and homology models, particularly the size and shape of the TAAR1 binding site. To assess the structure-activity relationships of TAAR1 activation, the researchers used compound 30, previously identified as the most potent by an AlphaFold screen. A series of analogues was then created. These compounds were fitted into AlphaFold models, with particular emphasis on how these models represented the orthosteric position and other critical binding regions. Sixteen promising analogs were selected for further evaluation. Various assays were used to evaluate the agonistic activity of the compounds, which evaluated activity at 27 aminergic GPCRs. In addition, a cyclic adenosine 3′,5′-monophosphate (cAMP) accumulation assay was used to measure potency, and pharmacokinetic profiling was performed to assess solubility, plasma protein binding, permeability, and metabolic stability.
In addition, in vivo studies involving core body temperature (CBT) in TAAR1 wild-type (TAAR1-WT) and TAAR1-knockout (TAAR1-KO) mice, pulse inhibition tests (PPI), and locomotion experiments were performed to assess the antipsychotic effects of compounds. In addition to assessing these physiological effects, structural comparisons were made between the AlphaFold models and newly released cryo-electron microscopy (cryo-EM) structures of TAAR1. These comparisons revealed that the AlphaFold models provided a more compact representation of the binding pocket, which influenced binding results and binding mode predictions.
Results and discussion
The study found that AlphaFold models outperformed homology models in virtual viewing, achieving a 60% success rate compared to a 22% success rate from the homology model screen. AlphaFold-derived agonists exhibited higher potency and diverse chemical structures. This higher success rate was attributed to AlphaFold’s more accurate prediction of the extracellular and orthosteric binding sites, although the models struggled with larger synthetic ligands. Compound 65 showed high activity and was found to be more effective than ulotaront. The selectivity profiles showed that compounds 30 and 65 were similar to ulotaront but also showed activity at additional receptors. Compound 65 showed improved selectivity compared to ulotaront, as well as excellent solubility, low plasma protein binding, good permeability and favorable metabolic stability.
However, the study also highlighted some limitations of the AlphaFold models. In vivo pharmacokinetic studies revealed rapid distribution of the compound in the brain. Behavioral tests showed that compound 65 effectively reduced CBT in TAAR1-WT mice but had no effect in TAAR1-KO mice. The compound also enhanced PPI in WT mice, similar to risperidone, but not in TAAR1-KO mice. In locomotion tests, compound 65 reduced basal locomotion and inhibited hyperlocomotion in WT mice but not in TAAR1-KO mice.
The research also highlighted that while AlphaFold models were generally more accurate than homology models, they still had important limitations. For example, AlphaFold struggled to predict the dynamic, multiple conformations of GPCRs, a critical aspect in accurately modeling binding sites for larger synthetic ligands. Structural comparisons revealed that AlphaFold models provided more accurate predictions of extracellular and orthosteric binding sites compared to homology models. Nevertheless, recently released cryo-EM structures showed that experimental data could provide better insights into binding modes, particularly for complex substituents. However, experimental cryo-EM structures showed better alignment with binding modes for larger synthetic ligands. This finding suggests that while AlphaFold is a powerful tool, it may need further refinement or combination with other techniques to fully capture the dynamic nature of GPCR-ligand interactions.
Conclusion
In conclusion, the study suggests that predicted machine learning structures, such as those generated by AlphaFold, can efficiently recognize GPCR ligands, accelerating drug discovery for new targets such as TAAR1. However, the study also highlights the need to continue developing these models to enhance their predictive power, particularly for complex ligands and dynamic protein conformations. Among the identified compounds, compound 65 exhibited greater potency, selectivity and favorable pharmacokinetic properties compared to ulotaront. It also showed promising antipsychotic-like effects in vivomaking it a potentially strong candidate for the development of new treatments for neuropsychiatric disorders.
