Macrocyclization of a Ligand Targeting a Toxic RNA Dramatically Improves Potency

Affinity selection-mass spectrometry with linearizable macrocyclic peptide libraries

Despite their potential, the preparation of large synthetic macrocyclic libraries for ligand discovery and development has been limited. Here, we produce 100-million-membered macrocyclic libraries containing natural and nonnatural amino acids. Near-quantitative intramolecular disulfide formation is facilitated by rapid oxidation with iodine in solution. After use in affinity selection, treatment with dithiothreitol enables near-quantitative reduction, rendering linear peptide analogs for standard tandem mass spectrometry. We use these libraries to discover macrocyclic binders to cadherin-2 and anti-hemagglutinin antibody clone 12ca5. Structure-activity relationship studies of an initial cadherin-binding peptide [CBP; apparent dissociation constant (Kd) = 53 nanomolar] reveal residues responsible for driving affinity (hotspots) and mutation-tolerant residues (coldspots). Two original macrocyclic libraries are prepared in which these hotspots and coldspots are derivatized with nonnatural amino acids. Following discovery and validation, high-affinity ligands are discovered from the coldspot library, with NCBP-4 demonstrating improved affinity (Kd = 29 nanomolar). Overall, we expect that this work will improve the use of macrocyclic libraries in therapeutic peptide development.

Experimental and computational investigations of RNA duplexes containing N7-regioisomers of adenosine and LNA-adenosine

Although glycosidic bonds in purines typically involve the N9 position, the chemical synthesis of adenosine produces N7-ribofuranosyladenine (7A) as a kinetically favorable ribosylation product. Similarly, in the synthesis of LNA-adenosine (AL), a minor product, N7-LNA-adenosine (7AL), is observed. While extensive research has focused on investigating the properties of N9-regioisomers of adenosine, 7A has been largely overlooked and considered as a side-product. In this study, we conducted comprehensive experimental and computational investigations to elucidate the structural and thermodynamic properties of 7A and 7AL. Our results reveal that 7A and 7AL primarily enhance the thermodynamic stability of 1 × 1 mismatches when paired with purines but decrease stability when paired with pyrimidines. Utilizing nuclear magnetic resonance and computational techniques, we discovered that 1 × 1 7A:A and 7AL:A prefer anti-anti conformations, while 1 × 1 7A:G and 7AL:G prefer syn-anti orientations, both forming two hydrogen bond states, resulting in enhanced duplex stabilities. Altogether, these findings underscore the unique properties of 7A and 7AL when incorporated in RNA, which could advance structure-based RNA studies and potentially be utilized to modulate binding affinity, selectivity and biostability of RNA molecules.

Dynamic docking of small molecules targeting RNA CUG repeats causing myotonic dystrophy type 1

Expansion of RNA CUG repeats causes myotonic dystrophy type 1 (DM1). Once transcribed, the expanded CUG repeats strongly attract muscleblind-like 1 (MBNL1) proteins and disturb their functions in cells. Because of its unique structural form, expanded RNA CUG repeats are prospective drug targets, where small molecules can be utilized to target RNA CUG repeats to inhibit MBNL1 binding and ameliorate DM1-associated defects. In this contribution, we developed two physics-based dynamic docking approaches (DynaD and DynaD/Auto) and applied them to nine small molecules known to specifically target RNA CUG repeats. While DynaD uses a distance-based reaction coordinate to study the binding phenomenon, DynaD/Auto combines results of umbrella sampling calculations performed on 1 × 1 UU internal loops and AutoDock calculations to efficiently sample the energy landscape of binding. Predictions are compared with experimental data, displaying a positive correlation with correlation coefficient (R) values of 0.70 and 0.81 for DynaD and DynaD/Auto, respectively. Furthermore, we found that the best correlation was achieved with MM/3D-RISM calculations, highlighting the importance of solvation in binding calculations. Moreover, we detected that DynaD/Auto performed better than DynaD because of the use of prior knowledge about the binding site arising from umbrella sampling calculations. Finally, we developed dendrograms to present how bound states are connected to each other in a binding process. Results are exciting, as DynaD and DynaD/Auto will allow researchers to utilize two novel physics-based and computer-aided drug-design methodologies to perform in silico calculations on drug-like molecules aiming to target complex RNA loops.

Massively Parallel Optimization of the Linker Domain in Small Molecule Dimers Targeting a Toxic r(CUG) Repeat Expansion

RNA contributes to disease pathobiology and is an important therapeutic target. The downstream biology of disease-causing RNAs can be short-circuited with small molecules that recognize structured regions. The discovery and optimization of small molecules interacting with RNA is, however, challenging. Herein, we demonstrate a massively parallel one-bead-one-compound methodology, employed to optimize the linker region of a dimeric compound that binds the toxic r(CUG) repeat expansion [r(CUG)exp] causative of myotonic dystrophy type 1 (DM1). Indeed, affinity selection on a 331,776-member library allowed the discovery of a compound with enhanced potency both in vitro (10-fold) and in DM1-patient-derived myotubes (5-fold). Molecular dynamics simulations revealed additional interactions between the optimized linker and the RNA, resulting in ca. 10 kcal/mol lower binding free energy. The compound was conjugated to a cleavage module, which directly cleaved the transcript harboring the r(CUG)exp and alleviated disease-associated defects.