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We report a general synthetic route toward helical ladder polymers with varying spring constants, built with chirality‐assisted synthesis (CAS). Under tension and compression, these shape‐persistent structures do not unfold, but rather stretch and compress akin classical Hookean springs. Our synthesis is adaptable to helices with different pitch and diameter, which allowed us to investigate how molecular flexibility in solution depends on the exact geometry of the ladder polymers. Specifically, we showed with molecular dynamic simulations and by measuring the longitudinal¹H NMR relaxation times (T₁) for our polymers at different Larmor frequencies, that increasing the helix diameter leads to increased flexibility. Our results present initial design rules for tuning the mechanical properties of intrinsically helical ladder polymers in solution, which will help inspire a new class of robust, spring‐like molecular materials with varying mechanical properties.

 

The lack of biologically relevant protein structures can hinder rational design of small molecules to target G protein-coupled receptors (GPCRs). While ensemble docking using multiple models of the protein target is a promising technique for structure-based drug discovery, model clustering and selection still need further investigations to achieve both high accuracy and efficiency. In this work, we have developed an original ensemble docking approach, which identifies the most relevant conformations based on the essential dynamics of the protein pocket. This approach is applied to the study of small-molecule antagonists for the PAC1 receptor, a class B GPCR and a regulator of stress. As few as four representative PAC1 models are selected from simulations of a homology model and then used to screen three million compounds from the ZINC database and 23 experimentally validated compounds for PAC1 targeting. Our essential dynamics ensemble docking (EDED) approach can effectively reduce the number of false negatives in virtual screening and improve the accuracy to seek potent compounds. Given the cost and difficulties to determine membrane protein structures for all the relevant states, our methodology can be useful for future discovery of small molecules to target more other GPCRs, either with or without experimental structures. 

This work presents the first transition metal-free synthesis of oxygen-linked aromatic polymers by integrating iterative exponential polymer growth (IEG) with nucleophilic aromatic substitution (S N Ar) reactions. Our approach applies methyl sulfones as the leaving groups, which eliminate the need for a transition metal catalyst, while also providing flexibility in functionality and configuration of the building blocks used. As indicated by 1) 1 H- 1 H NOESY NMR spectroscopy, 2) single-crystal X-ray crystallography, and 3) density functional theory (DFT) calculations, the unimolecular polymers obtained are folded by nonclassical hydrogen bonds formed between the oxygens of the electron-rich aromatic rings and the positively polarized C–H bonds of the electron-poor pyrimidine functions. Our results not only introduce a transition metal-free synthetic methodology to access precision polymers but also demonstrate how interactions between relatively small, neutral aromatic units in the polymers can be utilized as new supramolecular interaction pairs to control the folding of precision macromolecules. 

We synthesized some of the longest unimolecular oligo(p-phenylene ethynylenes) (OPEs), which are fully substituted with electron-withdrawing ester groups. An iterative convergent/divergent (a.k.a. iterative exponential growth – IEG) strategy based on Sonogashira couplings was utilized to access these sequence-defined macromolecules with up to 16 repeating units and 32 ester substituents. The carbonyl groups of the ester substituents interact with the triple bonds of the OPEs, leading to (i) unusual, angled triple bonds with increased rotational barrier, (ii) enhanced conformational disorder, and (iii) associated broadening of the UV/Vis absorption spectrum. Our results demonstrate that fully air-stable, unimolecular OPEs with ester groups can readily be accessed with IEG chemistry, providing new macromolecular backbones with unique geometrical, conformational, and photophysical properties. 

Abstract Electrophilic aromatic substitution reactions are of profound importance for the synthesis of biologically active compounds and other advanced materials. They represent an important means to activate specific aromatic C–H bonds without requiring transition-metal catalysts. Surprisingly, few stereoselective variants are known for electrophilic aromatic substitutions, which limits the utility of these classical reactions for stereoselective synthesis. While many electrophilic aromatic substitutions lead to achiral products (due to the planar nature of aromatic rings), there are important examples where chiral products are produced, including desymmetrization reactions of aromatic cyclophanes and of prochiral substrates with multiple aromatic rings. This Synpacts article now illustrates how chiral arms, when placed precisely above and underneath delocalized carbocations, can act as chiral auxiliaries to convert classical electrophilic aromatic substitution reactions into powerful diastereo- and enantioselective transformations. 

ABSTRACT Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.