Similar Binding Modes of cGMP Analogues Limit Selectivity in Modulating Retinal CNG Channels via the Cyclic Nucleotide-Binding Domain.

In treating retinitis pigmentosa, a genetic disorder causing progressive vision loss, selective inhibition of rod cyclic nucleotide-gated (CNG) channels holds promise. Blocking the increased Ca2+-influx in rod photoreceptors through CNG channels can potentially delay disease progression and improve the quality of life for patients. To find inhibitors for rod CNG channels, we investigated the impact of 16 cGMP analogues on both rod and cone CNG channels using the patch-clamp technique. Although modifications at the C8 position of the guanine ring did not change the ligand efficacy, modifications at the N1 and N2 positions rendered cGMP largely ineffective in activating retinal CNG channels. Notably, PET-cGMP displayed selective potential, favoring rod over cone, whereas Rp-cGMPS showed greater efficiency in activating cone over rod CNG channels. Ligand docking and molecular dynamics simulations on cyclic nucleotide-binding domains showed comparable binding energies and binding modes for cGMP and its analogues in both rod and cone CNG channels (CNGA1 vs CNGA3 subunits). Computational experiments on CNGB1a vs CNGB3 subunits showed similar binding modes albeit with fewer amino acid interactions with cGMP due to an inactivated conformation of their C-helix. In addition, no clear correlation could be observed between the computational scores and the CNG channel efficacy values, suggesting additional factors beyond binding strength determining ligand selectivity and potency. This study highlights the importance of looking beyond the cyclic nucleotide-binding domain and toward the gating mechanism when searching for selective modulators. Future efforts in developing selective modulators for CNG channels should prioritize targeting alternative channel domains.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels.

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

Targeting ion channels with ultra-large library screening for hit discovery.

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.