In silico identification of small molecule agonist binding sites on KCC2

Purpose: Potassium-Chloride Cotransporter 2 (KCC2) is a neuronal membrane protein specific to the central nervous system. It is responsible for removing Cl- ions from the intracellular space, maintaining a normal Cl- gradient essential for proper function at inhibitory synapses. Dysregulation causes an upward shift in the Cl- reversal potential resulting in a hyperexcitable state of the postsynaptic neuron. Existing literature indicates that KCC2 may be involved in the addiction pathway of a variety of drugs of abuse, including opioids and alcohol. This makes KCC2 an attractive potential drug target when treating substance use disorders. A novel direct KCC2 agonist, VU0500469, was recently identified experimentally; however, no binding sites were identified or characterized. The goal of this project is to identify likely binding sites of this protein-ligand pair via computer simulation. Methods: A 3D model of human KCC2 was obtained from RCSB Protein Databank. VU0500469 was reconstructed manually. Protein-ligand computational simulations were run using AutoDock Tools and AutoDock Vina, GNINA, and P2Rank to identify direct interactions between VU0500469, and KCC2. Results: Results between simulations were then compared, and several possible VU0500469 binding pocket sites were successfully identified. We plan to further investigate molecular binding dynamics using CHARMM. Conclusion: The binding sites identified may represent targets for the development of additional KCC2 agonists

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery