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Introduction:The proteins of the Bin/Amphiphysin/Rvs167 (BAR) domain superfamily arebelieved to induce membrane curvature. PICK1 is a distinctive protein that consists of both a BAR anda PDZ domain, and it has been associated with numerous diseases. It is known to facilitate membranecurvature during receptor-mediated endocytosis. In addition to understanding how the BAR domainfacilitates membrane curvature, it's particularly interesting to unravel the hidden links between thestructural and mechanical properties of the PICK1 BAR domain. Methods:This paper employs steered molecular dynamics (SMD) to investigate the mechanical propertiesassociated with structural changes in the PICK1 BAR domains. Results:Our findings suggest that not only do helix kinks assist in generating curvature of BAR domains,but they may also provide the additional flexibility required to initiate the binding betweenBAR domains and the membrane Conclusion:We have observed a complex interaction network within the BAR monomer and at thebinding interface of the two BAR monomers. This network is crucial for maintaining the mechanicalproperties of the BAR dimer. Owing to this interaction network, the PICK1 BAR dimer exhibits differentresponses to external forces applied in opposite directions. 

Introduction: The PICK1 PDZ domain has been identified as a potential drug target forneurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5,have been discovered experimentally to bind to the PDZ domain with a relatively high bindingaffinity. With the rapid growth of computational research, there is an urgent need for more efficientcomputational methods to design viable ligands that target proteins.Method: Recently, a newly developed program called AfDesign (part of ColabDesign) at https://github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has beensuggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitatingthe ligand development process. To evaluate the performance of this program, we exploredits ability to target the PICK1 PDZ domain, given our current understanding of it. We found thatthe designated length of the ligand and the number of recycles play vital roles in generating ligandswith optimal properties.Results: Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparableligands to that of prior identified ligands. Moreover, these designed ligands displayed significantlylower binding energy compared to manually created sequences.Conclusion: This work demonstrated that AfDesign can potentially be a powerful tool to facilitatethe exploration of the ligand space for the purpose of targeting PDZ domains. 

The PDZ family has drawn attention as possible drug targets because of the domains’ wide ranges of function and highly conserved binding pockets. The PICK1 PDZ domain has been proposed as a possible drug target because the interactions between the PICK1 PDZ domain and the GluA2 subunit of the AMPA receptor have been shown to progress neurodegenerative diseases. BIO124 has been identified as a sub µM inhibitor of the PICK1–GluA2 interaction. Here, we use all-atom molecular dynamics simulations to reveal the atomic-level interaction pattern between the PICK1 PDZ domain and BIO124. Our simulations reveal three unique binding conformations of BIO124 in the PICK1 PDZ binding pocket, referred to here as state 0, state 1, and state 2. Each conformation is defined by a unique hydrogen bonding network and a unique pattern of hydrophobic interactions between BIO124 and the PICK1 PDZ domain. Interestingly, each conformation of BIO124 results in different dynamic changes to the PICK1 PDZ domain. Unlike states 1 and 2, state 0 induces dynamic coupling between BIO124 and the αA helix. Notably, this dynamic coupling with the αA helix is similar to what has been observed in other PDZ–ligand complexes. Our analysis indicates that the interactions formed between BIO124 and I35 may be the key to inducing dynamic coupling with the αA helix. Lastly, we suspect that the conformational shifts observed in our simulations may affect the stability and thus the overall effectiveness of BIO124. We propose that a physically larger inhibitor may be necessary to ensure sufficient interactions that permit stable binding between a drug and the PICK1 PDZ domain. 

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.