The Trials and Tribulations of Structure Assisted Design of KCa Channel Activators.

Calcium-activated K+ channels constitute attractive targets for the treatment of neurological and cardiovascular diseases. To explain why certain 2-aminobenzothiazole/oxazole-type KCa activators (SKAs) are KCa3.1 selective we previously generated homology models of the C-terminal calmodulin-binding domain (CaM-BD) of KCa3.1 and KCa2.3 in complex with CaM using Rosetta modeling software. We here attempted to employ this atomistic level understanding of KCa activator binding to switch selectivity around and design KCa2.2 selective activators as potential anticonvulsants. In this structure-based drug design approach we used RosettaLigand docking and carefully compared the binding poses of various SKA compounds in the KCa2.2 and KCa3.1 CaM-BD/CaM interface pocket. Based on differences between residues in the KCa2.2 and KCa.3.1 models we virtually designed 168 new SKA compounds. The compounds that were predicted to be both potent and KCa2.2 selective were synthesized, and their activity and selectivity tested by manual or automated electrophysiology. However, we failed to identify any KCa2.2 selective compounds. Based on the full-length KCa3.1 structure it was recently demonstrated that the C-terminal crystal dimer was an artefact and suggested that the "real" binding pocket for the KCa activators is located at the S4-S5 linker. We here confirmed this structural hypothesis through mutagenesis and now offer a new, corrected binding site model for the SKA-type KCa channel activators. SKA-111 (5-methylnaphtho[1,2-d]thiazol-2-amine) is binding in the interface between the CaM N-lobe and the S4-S5 linker where it makes van der Waals contacts with S181 and L185 in the S45A helix of KCa3.1

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

Pose Classification using 3D Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early-stage drug discovery. With the recent increase in available small–molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a 3D convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation datasets including the standard PDBbind CASF-2016 benchmark dataset and various compound libraries with structurally different protein targets including an ion-channel dataset extracted from Protein Data Bank (PDB) and an inhouse KCa3.1 inhibitor dataset. Our experiments show that excluding false-positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein, compared to the initial screen based on the docking scores

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives

Pharmacology of Small- and Intermediate-Conductance Calcium-Activated Potassium Channels.

The three small-conductance calcium-activated potassium (KCa2) channels and the related intermediate-conductance KCa3.1 channel are voltage-independent K+ channels that mediate calcium-induced membrane hyperpolarization. When intracellular calcium increases in the channel vicinity, it calcifies the flexible N lobe of the channel-bound calmodulin, which then swings over to the S4-S5 linker and opens the channel. KCa2 and KCa3.1 channels are highly druggable and offer multiple binding sites for venom peptides and small-molecule blockers as well as for positive- and negative-gating modulators. In this review, we briefly summarize the physiological role of KCa channels and then discuss the pharmacophores and the mechanism of action of the most commonly used peptidic and small-molecule KCa2 and KCa3.1 modulators. Finally, we describe the progress that has been made in advancing KCa3.1 blockers and KCa2.2 negative- and positive-gating modulators toward the clinic for neurological and cardiovascular diseases and discuss the remaining challenges

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores