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.
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.
Dynamic allosterism allows the propagation of signal throughout a protein. The PDZ (PSD-95/Dlg1/ZO-1) family has been named as a classic example of dynamic allostery in small modular domains. While the PDZ family consists of more than 200 domains, previous efforts have primarily focused on a few well-studied PDZ domains, including PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ. Taken together, experimental and computational studies have identified regions of these domains that are dynamically coupled to ligand binding. These regions include the αA helix, the αB lower-loop, and the αC helix. In this review, we summarize the specific residues on the αA helix, the αB lower-loop, and the αC helix of PTP-BL PDZ2, PSD-95 PDZ3, and Par6 PDZ that have been identified as participants in dynamic allostery by either experimental or computational approaches. This review can serve as an index for researchers to look back on the previously identified allostery in the PDZ family. Interestingly, our summary of previous work reveals clear consistencies between the domains. While the PDZ family has a low sequence identity, we show that some of the most consistently identified allosteric residues within PTP-BL PDZ2 and PSD-95 PDZ3 domains are evolutionarily conserved. These residues include A46/A347, V61/V362, and L66/L367 on PTP-BL PDZ2 and PSD-95 PDZ3, respectively. Finally, we expose a need for future work to explore dynamic allostery within (1) PDZ domains with multiple binding partners and (2) multidomain constructs containing a PDZ domain.
The dynamic association and dissociation between proteins are the basis of cellular signal transduction. This process becomes much more complicated if one or both interaction partners are intrinsically disordered because intrinsically disordered proteins can undergo disorder-to-order transitions upon binding to their partners. p53, a transcription factor with disordered regions, plays significant roles in many cellular signaling pathways. It is critical to understand the binding/unbinding mechanism involving these disordered regions of p53 at the residue level to reveal how p53 performs its biological functions. Here, we studied the dissociation process of the intrinsically disordered N-terminal transactivation domain 2 (TAD2) of p53 and the transcriptional adaptor zinc-binding 2 (Taz2) domain of transcriptional coactivator p300 using a combination of classical molecular dynamics, steered molecular dynamics, self-organizing maps, and time-resolved force distribution analysis (TRFDA). We observed two different dissociation pathways with different probabilities. One dissociation pathway starts from the TAD2 N-terminus and propagates to the α-helix and finally the C-terminus. The other dissociation pathway is in the opposite order. Subsequent TRFDA results reveal that key residues in TAD2 play critical roles. Besides the residues in agreement with previous experimental results, we also highlighted some other residues that play important roles in the disassociation process. In the dissociation process, non-native interactions were formed to partially compensate for the energy loss due to the breaking of surrounding native interactions. Moreover, our statistical analysis results of other experimentally determined complex structures involving either Taz2 or TAD2 suggest that the binding of the Taz2-TAD2 complex is mainly governed by the binding site of Taz2, which includes three main binding regions. Therefore, the complexes involving Taz2 may follow similar binding/unbinding behaviors, which could be studied together to generate common principles.
The PDZ family is comprised of small modular domains that play critical roles in the allosteric modulation of many cellular signaling processes by binding to the C‐terminal tail of different proteins. As dominant modular proteins that interact with a diverse set of peptides, it is of particular interest to explore how different binding partners induce different allosteric effects on the same PDZ domain. Because the PICK1 PDZ domain can bind different types of ligands, it is an ideal test case to answer this question and explore the network of interactions that give rise to dynamic allostery. Here, we use all‐atom molecular dynamics simulations to explore dynamic allostery in the PICK1 PDZ domain by modeling two PICK1 PDZ systems: PICK1 PDZ‐DAT and PICK1 PDZ‐GluR2. Our results suggest that ligand binding to the PICK1 PDZ domain induces dynamic allostery at the αA helix that is similar to what has been observed in other PDZ domains. We found that the PICK1 PDZ‐ligand distance is directly correlated with both dynamic changes of the αA helix and the distance between the αA helix and βB strand. Furthermore, our work identifies a hydrophobic core between DAT/GluR2 and I35 as a key interaction in inducing such dynamic allostery. Finally, the unique interaction patterns between different binding partners and the PICK1 PDZ domain can induce unique dynamic changes to the PICK1 PDZ domain. We suspect that unique allosteric coupling patterns with different ligands may play a critical role in how PICK1 performs its biological functions in various signaling networks.
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