Transient receptor potential canonical (TRPC) channels are Ca2؉ -permeable nonselective cation channels implicated in diverse physiological functions, including smooth muscle contractility and synaptic transmission. However, lack of potent selective pharmacological inhibitors for TRPC channels has limited delineation of the roles of these channels in physiological systems. Here we report the identification and characterization of ML204 as a novel, potent, and selective TRPC4 channel inhibitor. A high throughput fluorescent screen of 305,000 compounds of the Molecular Libraries Small Molecule Repository was performed for inhibitors that blocked intracellular Ca 2؉ rise in response to stimulation of mouse TRPC4␤ by -opioid receptors. ML204 inhibited TRPC4␤-mediated intracellular Ca 2؉ rise with an IC 50 value of 0.96 M and exhibited 19-fold selectivity against muscarinic receptor-coupled TRPC6 channel activation. In wholecell patch clamp recordings, ML204 blocked TRPC4␤ currents activated through either -opioid receptor stimulation or intracellular dialysis of guanosine 5-3-O-(thio)triphosphate (GTP␥S), suggesting a direct interaction of ML204 with TRPC4 channels rather than any interference with the signal transduction pathways. Selectivity studies showed no appreciable block by 10 -20 M ML204 of TRPV1, TRPV3, TRPA1, and TRPM8, as well as KCNQ2 and native voltage-gated sodium, potassium, and calcium channels in mouse dorsal root ganglion neurons. In isolated guinea pig ileal myocytes, ML204 blocked muscarinic cation currents activated by bath application of carbachol or intracellular infusion of GTP␥S, demonstrating its effectiveness on native TRPC4 currents. Therefore, ML204 represents an excellent novel tool for investigation of TRPC4 channel function and may facilitate the development of therapeutics targeted to TRPC4.

The Kir inward rectifying potassium channels have a broad tissue distribution and are implicated in a variety of functional roles. At least seven classes (Kir1 – Kir7) of structurally related inward rectifier potassium channels are known, and there are no selective small molecule tools to study their function. In an effort to develop selective Kir2.1 inhibitors, we performed a high-throughput screen (HTS) of more than 300,000 small molecules within the MLPCN for modulators of Kir2.1 function. Here we report one potent Kir2.1 inhibitor, ML133, which inhibits Kir2.1 with IC50 of 1.8 μM at pH 7.4 and 290 nM at pH 8.5, but exhibits little selectivity against other members of Kir2.x family channels. However, ML133 has no effect on Kir1.1 (IC50 > 300 μM), and displays weak activity for Kir4.1 (76 μM) and Kir7.1 (33 μM), making ML133 the most selective small molecule inhibitor of the Kir family reported to date. Due to the high homology within the Kir family, the channels share a common design of a pore region flanked by two transmembrane domains, identification of site(s) critical for isoform specificity would be an important basis for future development of more specific and potent Kir inhibitors. Using chimeric channels between Kir2.1 and Kir1.1 and site-directed mutagenesis, we have identified D172 and I176 within M2 segment of Kir2.1 as molecular determinants critical for the potency of ML133 mediated inhibition. Double mutation of the corresponding residues of Kir1.1 to those of Kir2.1 (N171D and C175I) transplants ML133 inhibition to Kir1.1. Together, the combination of a potent, Kir2 family selective inhibitor and identification of molecular determinants for the specificity provides both a tool and a model system to enable further mechanistic studies of modulation of Kir2 inward rectifier potassium channels.

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The inhibition of protein–protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein–protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2’s prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.