Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

Abstract

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are wellestablished, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a singlepoint structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

TRACER: Molecular Optimization Using Conditional Transformer for Reaction-Aware Compound Exploration with Reinforcement Learning

“…Recent advances in deep learning models and their generalization capabilities have led to the development of generative models for de novo design and hit-to-lead optimization. [4][5][6][7][8] However, most of these methods only focus on "what to make" and do not sufficiently consider "how to make"; thus, these methods have difficulty in the generation of synthesizable molecules. 9 Chemists must search for candidates for synthetic experiments from the many compounds suggested by the generative models; this aspect is a significant barrier to moving to bench synthesis.…”

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization

“…In the third stage, the lead compound is optimized to maintain a favorable affinity and improve pharmacokinetics . Recently, target-to-hit and lead optimization stages have become more efficient owing to computational tools such as docking simulation, molecular dynamics (MD) simulation, and artificial intelligence. − …”

Gargoyles: An Open Source Graph-Based Molecular Optimization Method Based on Deep Reinforcement Learning

“…It involves a series of steps beginning with the identification of biological targets, followed by the development of hit compounds, optimization of lead compounds, and finally, preclinical and clinical testing of drug candidates. 1 Hit compounds are compounds that show initial activity against target proteins involved in diseases, such as 3CL protease, 2 spermidine synthase, 3,4 dephospho-CoA kinase, 5 and nicotinamide adenine dinucleotide (NAD)-dependent deacetylase Sirtuin 1, 6 and have potential to be developed as drugs. Hit compounds are found through various screening methods, including high-throughput screening, which searches for targets from a large compound library.…”

“…Hit compounds are compounds that show initial activity against target proteins involved in diseases, such as 3CL protease, spermidine synthase, , dephospho-CoA kinase, and nicotinamide adenine dinucleotide (NAD)-dependent deacetylase Sirtuin 1, and have potential to be developed as drugs. Hit compounds are found through various screening methods, including high-throughput screening, which searches for targets from a large compound library.…”