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1. Recognition of non-CpG repeats in Alu and ribosomal RNAs by the Z-RNA binding domain of ADAR1 induces A-Z junctions

   https://doi.org/10.1038/s41467-021-21039-0  

   Nichols, Parker J.; Bevers, Shaun; Henen, Morkos; Kieft, Jeffrey S.; Vicens, Quentin; Vögeli, Beat (December 2021, Nature Communications)

   null (Ed.)

   Abstract Adenosine-to-inosine (A-to-I) editing of eukaryotic cellular RNAs is essential for protection against auto-immune disorders. Editing is carried out by ADAR1, whose innate immune response-specific cytoplasmic isoform possesses a Z-DNA binding domain (Zα) of unknown function. Zα also binds to CpG repeats in RNA, which are a hallmark of Z-RNA formation. Unexpectedly, Zα has been predicted — and in some cases even shown — to bind to specific regions within mRNA and rRNA devoid of such repeats. Here, we use NMR, circular dichroism, and other biophysical approaches to demonstrate and characterize the binding of Zα to mRNA and rRNA fragments. Our results reveal a broad range of RNA sequences that bind to Zα and adopt Z-RNA conformations. Binding is accompanied by destabilization of neighboring A-form regions which is similar in character to what has been observed for B-Z-DNA junctions. The binding of Zα to non-CpG sequences is specific, cooperative and occurs with an affinity in the low micromolar range. This work allows us to propose a model for how Zα could influence the RNA binding specificity of ADAR1. 

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2. Dynamic Mechanism for Subtype Selectivity of Endocannabinoids

   https://doi.org/10.1101/2024.10.25.620304  

   Dutta, Soumajit; Zhao, Lawrence; Shukla, Diwakar (October 2024, bioRxiv)

   Endocannabinoids are naturally occurring lipid-like molecules that bind to cannabinoid receptors (CB₁and CB₂) and regulate many of human bodily functions via the endocannabinoid system. There is a tremendous interest in developing selective drugs that target the CB receptors. However, the biophysical mechanisms responsible for the subtype selectivity for endocannbinoids have not been established. Recent experimental structures of CB receptors show that endocannbinoids potentially bind via membrane using the lipid access channel in the transmembrane region of the receptors. Furthermore, the N-terminus of the receptor could move in and out of the binding pocket thereby modulating both the pocket volume and its residue composition. On the basis of these observations, we propose two hypothesis to explain the selectivity of the endocannabinoid, anandamide for CB₁receptor. First, the selectivity arises from distinct enthalpic ligand-protein interactions along the ligand binding pathway formed due to the movement of N-terminus and subsequent shifts in the binding pocket composition. Second, selectivity arises from the volumetric differences in the binding pocket allowing for differences in ligand conformational entropy. To quantitatively test these hypothesis, we perform extensive molecular dynamics simulations (∼0.9 milliseconds) along with Markov state modeling and deep learning-based VAMPnets to provide an interpretable characterization of the anandamide binding process to cannabinoid receptors and explain its selectivity for CB₁. Our findings reveal that the distinct N-terminus positions along lipid access channels between TM1 and TM7 lead to different binding mechanisms and interactions between anandamide and the binding pocket residues. To validate the critical stabilizing interactions along the binding pathway, relative free energy calculations of anandamide analogs are used. Moreover, the larger CB₂pocket volume increases the entropic effects of ligand binding by allowing higher ligand fluctuations but reduced stable interactions. Therefore, the opposing enthalpy and entropy effects between the receptors shape the endocannabinoid selectivity. Overall, the CB₁selectivity of anandamide is explained by the dominant enthalpy contributions due to ligand-protein interactions in stable binding poses. This study shed lights on potential selectivity mechanisms for endocannabinoids that would aid in the discovery of CB selective drugs 

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3. Deconvoluting binding sites in amyloid nanofibrils using time-resolved spectroscopy

   https://doi.org/10.1039/D2SC05418C  

   Jiang, Bo; Umezaki, Utana; Augustine, Andrea; Jayasinghe-Arachchige, Vindi M.; Serafim, Leonardo F.; He, Zhi Mei; Wyss, Kevin M.; Prabhakar, Rajeev; Martí, Angel A. (February 2023, Chemical Science)

   Steady-state fluorescence spectroscopy has a central role not only for sensing applications, but also in biophysics and imaging. Light switching probes, such as ruthenium dipyridophenazine complexes, have been used to study complex systems such as DNA, RNA, and amyloid fibrils. Nonetheless, steady-state spectroscopy is limited in the kind of information it can provide. In this paper, we use time-resolved spectroscopy for studying binding interactions between amyloid-β fibrillar structures and photoluminescent ligands. Using time-resolved spectroscopy, we demonstrate that ruthenium complexes with a pyrazino phenanthroline derivative can bind to two distinct binding sites on the surface of fibrillar amyloid-β, in contrast with previous studies using steady-state photoluminescence spectroscopy, which only identified one binding site for similar compounds. The second elusive binding site is revealed when deconvoluting the signals from the time-resolved decay traces, allowing the determination of dissociation constants of 3 and 2.2 μM. Molecular dynamic simulations agree with two binding sites on the surface of amyloid-β fibrils. Time-resolved spectroscopy was also used to monitor the aggregation of amyloid-β in real-time. In addition, we show that common polypyridine complexes can bind to amyloid-β also at two different binding sites. Information on how molecules bind to amyloid proteins is important to understand their toxicity and to design potential drugs that bind and quench their deleterious effects. The additional information contained in time-resolved spectroscopy provides a powerful tool not only for studying excited state dynamics but also for sensing and revealing important information about the system including hidden binding sites. 

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4. Negatively charged, intrinsically disordered regions can accelerate target search by DNA-binding proteins

   https://doi.org/10.1093/nar/gkad045  

   Wang, Xi; Bigman, Lavi S.; Greenblatt, Harry M.; Yu, Binhan; Levy, Yaakov; Iwahara, Junji (February 2023, Nucleic Acids Research)

   Abstract In eukaryotes, many DNA/RNA-binding proteins possess intrinsically disordered regions (IDRs) with large negative charge, some of which involve a consecutive sequence of aspartate (D) or glutamate (E) residues. We refer to them as D/E repeats. The functional role of D/E repeats is not well understood, though some of them are known to cause autoinhibition through intramolecular electrostatic interaction with functional domains. In this work, we investigated the impacts of D/E repeats on the target DNA search kinetics for the high-mobility group box 1 (HMGB1) protein and the artificial protein constructs of the Antp homeodomain fused with D/E repeats of varied lengths. Our experimental data showed that D/E repeats of particular lengths can accelerate the target association in the overwhelming presence of non-functional high-affinity ligands (‘decoys’). Our coarse-grained molecular dynamics (CGMD) simulations showed that the autoinhibited proteins can bind to DNA and transition into the uninhibited complex with DNA through an electrostatically driven induced-fit process. In conjunction with the CGMD simulations, our kinetic model can explain how D/E repeats can accelerate the target association process in the presence of decoys. This study illuminates an unprecedented role of the negatively charged IDRs in the target search process. 

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5. Improving the generalizability of protein-ligand binding predictions with AI-Bind

   https://doi.org/10.1038/s41467-023-37572-z  

   Chatterjee, Ayan; Walters, Robin; Shafi, Zohair; Ahmed, Omair Shafi; Sebek, Michael; Gysi, Deisy; Yu, Rose; Eliassi-Rad, Tina; Barabási, Albert-László; Menichetti, Giulia (December 2023, Nature Communications)

   Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery. 

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