マルチステートモデリングで薬剤発見を進める

タイトル: Applying multi-state modeling using AlphaFold2 for kinases and its application for ensemble screening

概要: Structure-based virtual screening (SBVS) is a pivotal computational approach in drug discovery, enabling the identification of potential drug candidates within vast chemical libraries by predicting their interactions with target proteins. The SBVS relies on the receptor protein structures, making it sensitive to structural variations. Kinase, one of the major drug targets, is known as one of the typical examples of an active site conformation change caused by the type of binding inhibitors. Examination of human kinase structures shows that the majority of conformations have the DFGin state. Thus, SBVS using the structures might cause a favor of type of ligand type I inhibitors, bind to the DFGin state, rather than finding the diverse scaffolds. Recent advances in protein structure prediction, such as AlphaFold2 (AF2), offer promising solutions but may still be possibly influenced by the structural bias in existing templates. To address these challenges, we introduce a multi-state modeling (MSM) protocol for kinase structures. We apply MSM to AF2 by providing state-specific templates, allowing us to overcome structural biases and thus apply them to kinase SBVS. We benchmarked our MSM models in three categories: quality of predicted models, reproducibility of ligand binding poses, and identification of hit compounds by ensemble SBVS. The results demonstrate that MSM-generated models exhibit comparable or improved structural accuracy compared to standard AF2 models. We also show that MSM models enhance the accuracy of cognate docking, effectively capturing the interactions between kinases and their ligands. In virtual screening experiments using DUD-E compound libraries, our MSM approach consistently outperforms standard AF2 modeling. Notably, MSM-based ensemble screening excels in identifying diverse hit compounds for kinases with structurally diverse active sites, surpassing standard AF2 models. We highlight the potential of MSM in broadening the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors. Author SummaryOne of the main problems with structure-based virtual screening is structural flexibility. Ensemble screening is one of the conventional approaches to solving the issue. Gathering experimental structures or molecular simulations could be used to compile the receptor structures. Recent developments in algorithms for predicting protein structures, like AlphaFold2, suggest that different receptor conformations could be produced. However, the prediction approaches produce biased structures because of the bias in the structure database. In order to solve the problem, we developed a protocol called multi-state modeling for kinases. Rather than supplying multiple sequence alignments as an input, we gave the AlphaFold2 a specific template structure and the sequence alignment between the template and query. Our findings imply that our technique can yield a particular structural state of interest with an enhanced or comparable structural quality to AlphaFold2 and predict highly accurate protein-ligand complex structures. Lastly, compared to the typical AlphaFold2 models, ensemble screening using the multi-state modeling approach improves the structure-based virtual screening performance, particularly for diverse active molecular scaffolds.