Kinetically controlled synthesis of rotaxane geometric isomers.

Geometric isomerism in mechanically interlocked systems-which arises when the axle of a mechanically interlocked molecule is oriented, and the macrocyclic component is facially dissymmetric-can provide enhanced functionality for directional transport and polymerization catalysis. We now introduce a kinetically controlled strategy to control geometric isomerism in [2]rotaxanes. Our synthesis provides the major geometric isomer with high selectivity, broadening synthetic access to such interlocked structures. Starting from a readily accessible [2]rotaxane with a symmetrical axle, one of the two stoppers is activated selectively for stopper exchange by the substituents on the ring component. High selectivities are achieved in these reactions, based on coupling the selective formation reactions leading to the major products with inversely selective depletion reactions for the minor products. Specifically, in our reaction system, the desired (major) product forms faster in the first step, while the undesired (minor) product subsequently reacts away faster in the second step. Quantitative 1H NMR data, fit to a detailed kinetic model, demonstrates that this effect (which is conceptually closely related to minor enantiomer recycling and related processes) can significantly improve the intrinsic selectivity of the reactions. Our results serve as proof of principle for how multiple selective reaction steps can work together to enhance the stereoselectivity of synthetic processes forming complex mechanically interlocked molecules.

Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study.

Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design.

Nano-Bio Interactions between DNA Nanocages and Human Serum Albumin.

DNA nanostructures have emerged as promising nanomedical tools due to their biocompatibility and tunable behavior. Recent work has shown that DNA nanocages decorated with organic dendrimers strongly bind human serum albumin (HSA), yet the dynamic structures of these complexes remain uncharacterized. This theoretical and computational investigation elucidates the fuzzy interactions between dendritically functionalized cubic DNA nanocages and HSA. The dendrimer-HSA interactions occur via nonspecific binding with the protein thermodynamically and kinetically free to cross the open faces of the cubic scaffold. However, the rigidity of the DNA scaffold prevents the binding energetics from scaling with the number of dendrimers. These discoveries not only provide a useful framework by which to model general interactions of DNA nanostructures complexed with serum proteins but also give valuable molecular insight into the design of next-generation DNA nanomedicines.

Efficient Multigram Procedure for the Synthesis of Large Hydrazone-linked Molecular Cages.

Covalently linked molecular cages can provide significant advantages (including, but not limited to enhanced thermal and chemical stability) over metal-linked coordination cages. Yet, while large coordination cages can now be created routinely, it is still challenging to create chemically robust, covalently linked molecular cages with large internal cavities. This fundamental challenge has made it difficult, for example, to introduce endohedral functional groups into covalent cages to enhance their practical utility (e.g., for selective guest recognition or catalysis), since the cavities would have simply been filled up with such endohedral functional groups in most cases. Here we now report the synthesis of some of the largest known covalently linked molecular tetrahedra. Our new covalent cages all contain 12 peripheral functional groups, which keep them soluble. They are formed from a common vertex, which aligns the hydrazide functions required for the hydrazone linkages with atropisomerism. While we previously reported this vertex as a building block for the smallest member of our hydrazone-linked tetrahedra, our original synthesis was not feasible to be carried out on the larger scales required to successfully access the larger tetrahedra. To overcome this synthetic challenge, we now present a greatly improved synthesis of our vertex, which only requires a single chromatographic step (compared to 3 chromatographic purification steps, which were needed for the initial synthesis). Our new synthetic route enabled us to create a whole family of molecular cages with increasing size (all linked with hydrolytically stable hydrazone bonds), with our largest covalent cage featuring p-quarterphenyl linkers and the ability to encapsulate a hypothetical sphere of approximately 3 nm in diameter. These results now open up the possibility to introduce functional groups required for selective recognition and catalysis into chemically robust covalent cages (without blocking the cavities of the covalent cages).

Structural and functional fine mapping of cysteines in mammalian glutaredoxin reveal their differential oxidation susceptibility.

Protein-S-glutathionylation is a post-translational modification involving the conjugation of glutathione to protein thiols, which can modulate the activity and structure of key cellular proteins. Glutaredoxins (GLRX) are oxidoreductases that regulate this process by performing deglutathionylation. However, GLRX has five cysteines that are potentially vulnerable to oxidative modification, which is associated with GLRX aggregation and loss of activity. To date, GLRX cysteines that are oxidatively modified and their relative susceptibilities remain unknown. We utilized molecular modeling approaches, activity assays using recombinant GLRX, coupled with site-directed mutagenesis of each cysteine both individually and in combination to address the oxidizibility of GLRX cysteines. These approaches reveal that C8 and C83 are targets for S-glutathionylation and oxidation by hydrogen peroxide in vitro. In silico modeling and experimental validation confirm a prominent role of C8 for dimer formation and aggregation. Lastly, combinatorial mutation of C8, C26, and C83 results in increased activity of GLRX and resistance to oxidative inactivation and aggregation. Results from these integrated computational and experimental studies provide insights into the relative oxidizability of GLRX's cysteines and have implications for the use of GLRX as a therapeutic in settings of dysregulated protein glutathionylation.

GPCRLigNet: rapid screening for GPCR active ligands using machine learning.

Molecules with bioactivity towards G protein-coupled receptors represent a subset of the vast space of small drug-like molecules. Here, we compare machine learning models, including dilated graph convolutional networks, that conduct binary classification to quickly identify molecules with activity towards G protein-coupled receptors. The models are trained and validated using a large set of over 600,000 active, inactive, and decoy compounds. The best performing machine learning model, dubbed GPCRLigNet, was a surprisingly simple feedforward dense neural network mapping from Morgan fingerprints to activity. Incorporation of GPCRLigNet into a high-throughput virtual screening workflow is demonstrated with molecular docking towards a particular G protein-coupled receptor, the pituitary adenylate cyclase-activating polypeptide receptor type 1. Through rigorous comparison of docking scores for molecules selected with and without using GPCRLigNet, we demonstrate an enrichment of potentially potent molecules using GPCRLigNet. This work provides a proof of principle that GPCRLigNet can effectively hone the chemical search space towards ligands with G protein-coupled receptor activity.

Disconnect between the effects of serelaxin on renal function and outcome in acute heart failure.

BACKGROUND: We aimed to study whether improvement in renal function by serelaxin in patients who were hospitalized for acute heart failure (HF) might explain any potential effect on clinical outcomes. METHODS: We included 6318 patients from the RELAXin in AHF-2 (RELAX-AHF2) study. Improvement in renal function was defined as a decrease in serum creatinine of ≥ 0.3 mg/dL and ≥ 25%, or increase in estimated glomerular filtration rate of ≥ 25% between baseline and day 2. Worsening renal function (WRF) was defined as the reverse. We performed causal mediation analyses regarding 180-day all-cause mortality (ACM), cardiovascular death (CVD), and hospitalization for HF/renal failure. RESULTS: Improvement in renal function was more frequently observed with serelaxin when compared with placebo [OR 1.88 (95% CI 1.64-2.15, p < 0.0001)], but was not associated with subsequent clinical outcomes. WRF occurred less frequent with serelaxin [OR 0.70 (95% CI 0.60-0.83, p < 0.0001)] and was associated with increased risk of ACM, worsening HF and the composite of CVD and HF or renal failure hospitalization. Improvement in renal function did not mediate the treatment effect of serelaxin [CVD HR 1.01 (0.99-1.04), ACM HR 1.01 (0.99-1.03), HF/renal failure hospitalization HR 0.99 (0.97-1.00)]. CONCLUSIONS: Despite the significant improvement in renal function by serelaxin in patients with acute HF, the potential beneficial treatment effect was not mediated by improvement in renal function. These data suggest that improvement in renal function might not be a suitable surrogate marker for potential treatment efficacy in future studies with novel relaxin agents in acute HF. Central illustration. Conceptual model explaining mediation analysis; treatment efficacy of heart failure therapies mediated by renal function.

Helical Molecular Springs with Varying Spring Constants.

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 longitudinal 1 H 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.

Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery.

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.

Concerted Rolling and Penetration of Peptides during Membrane Binding.

Peptide binding to membranes is common and fundamental in biochemistry and biophysics and critical for applications ranging from drug delivery to the treatment of bacterial infections. However, it is largely unclear, from a theoretical point of view, what peptides of different sequences and structures share in the membrane-binding and insertion process. In this work, we analyze three prototypical membrane-binding peptides (α-helical magainin, PGLa, and ß-hairpin tachyplesin) during membrane binding, using molecular details provided by Markov state modeling and microsecond-long molecular dynamics simulations. By leveraging both geometric and data-driven collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions, we reveal how the slowest kinetic process of membrane binding is the dynamic rolling of the peptide from an attached to a fully bound state. These results not only add fundamental knowledge of the theory of how peptides bind to biological membranes but also open new avenues to study general peptides in more complex environments for further applications.

Carbonyl-to-Alkyne Electron Donation Effects in up to 10-nm-Long, Unimolecular Oligo(p-phenylene ethynylenes).

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.

A computational study of cooperative binding to multiple SARS-CoV-2 proteins.

Structure-based drug design targeting the SARS-CoV-2 virus has been greatly facilitated by available virus-related protein structures. However, there is an urgent need for effective, safe small-molecule drugs to control the spread of the virus and variants. While many efforts are devoted to searching for compounds that selectively target individual proteins, we investigated the potential interactions between eight proteins related to SARS-CoV-2 and more than 600 compounds from a traditional Chinese medicine which has proven effective at treating the viral infection. Our original ensemble docking and cooperative docking approaches, followed by a total of over 16-micorsecond molecular simulations, have identified at least 9 compounds that may generally bind to key SARS-CoV-2 proteins. Further, we found evidence that some of these compounds can simultaneously bind to the same target, potentially leading to cooperative inhibition to SARS-CoV-2 proteins like the Spike protein and the RNA-dependent RNA polymerase. These results not only present a useful computational methodology to systematically assess the anti-viral potential of small molecules, but also point out a new avenue to seek cooperative compounds toward cocktail therapeutics to target more SARS-CoV-2-related proteins.

Molecular Basis for Environment Sensing by a Nucleoid-Structuring Bacterial Protein Filament.

The histone-like nucleoid structuring (H-NS) protein controls the expression of hundreds of genes in Gram-positive bacteria through its capability to coat and condense DNA. This mechanism requires the formation of superhelical H-NS protein filaments that are sensitive to temperature and salinity, allowing H-NS to act as an environment sensor. We use multiscale modeling and simulations to obtain detailed insights into the mechanism of H-NS filament's sensitivity to environmental changes. Through the simulations of the superhelical H-NS filament, we reveal how different environments induce heterogeneity of H-NS monomers. Further, we observe that transient self-association within the H-NS filament creates temperature-inducible strain and might mildly oppose DNA binding. We also probe different H-NS-DNA complex architectures and show that complexation enhances the stability of both DNA and H-NS superhelices. Overall, our results provide unprecedented molecular insights into the environmental sensing and DNA interactions of a prototypical nucleoid-structuring bacterial protein filament.

Enhanced sampling protocol to elucidate fusion peptide opening of SARS-CoV-2 spike protein.

Large-scale conformational transitions in the spike protein S2 domain are required during host-cell infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although conventional molecular dynamics simulations have been extensively used to study therapeutic targets of SARS-CoV-2, it is still challenging to gain molecular insight into the key conformational changes because of the size of the spike protein and the long timescale required to capture these transitions. In this work, we have developed an efficient simulation protocol that leverages many short simulations, a dynamic selection algorithm, and Markov state models to interrogate the structural changes of the S2 domain. We discovered that the conformational flexibility of the dynamic region upstream of the fusion peptide in S2 is coupled to the proteolytic cleavage state of the spike protein. These results suggest that opening of the fusion peptide likely occurs on a submicrosecond timescale after cleavage at the S2' site. Building on the structural and dynamical information gained to date about S2 domain dynamics, we provide proof of principle that a small molecule bound to a seam neighboring the fusion peptide can slow the opening of the fusion peptide, leading to a new inhibition strategy for experiments to confirm. In aggregate, these results will aid the development of drug cocktails to inhibit infections caused by SARS-CoV-2 and other coronaviruses.

Iterative Exponential Growth of Oxygen-Linked Aromatic Polymers Driven by Nucleophilic Aromatic Substitution Reactions.

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 (SNAr) 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) 1H-1H 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.

Aggregation State of Synergistic Antimicrobial Peptides.

By integrating various simulation and experimental techniques, we discovered that antimicrobial peptides (AMPs) may achieve synergy at an optimal concentration and ratio, which can be caused by aggregation of the synergistic peptides. On multiple time and length scales, our studies obtain novel evidence of how peptide coaggregation in solution can affect the disruption of membranes by synergistic AMPs. Our findings provide crucial details about the complex molecular origins of AMP synergy, which will help guide the future development of synergistic AMPs as well as applications of anti-infective peptide cocktail therapies.

Size-selective Catalytic Polymer Acylation with a Molecular Tetrahedron.

Selective catalysis at the molecular level represents a cornerstone of chemical synthesis. However, it still remains an open question how to elevate tunable catalysis to larger length scales to functionalize whole polymer chains in a selective manner. We now report a hydrazone-linked tetrahedron with wide openings, which acts as a catalyst to size-selectively functionalize polydisperse polymer mixtures. Our experimental and computational evidence supports a dual role of the hydrazone-linked tetrahedron. To accelerate functionalization of the polymer substrates, the tetrahedron (i) unfolds the polymer substrates and/or breaks the polymer aggregates as well as (ii) enables target sites (amino groups) on the polymers to coordinate with catalytic units (triglyme) attached to the tetrahedron. With the tetrahedron as the catalyst, we find that the reactivity of the shorter polymers increases selectively. Our findings enable the possibility to engineer hydrolytically stable molecular polyhedra as organocatalysts for size-selective polymer modification.

Selective Monofunctionalization Enabled by Reaction-History-Dependent Communication in Catalytic Rotaxanes.

Selective monofunctionalization of substrates with distant, yet equally reactive functional groups is difficult to achieve, as it requires the second functional group to selectively modulate its reactivity once the first functional group has reacted. We now show that mechanically interlocked catalytic rings can effectively regulate the reactivity of stoppering groups in rotaxanes over a distance of about 2 nm. Our mechanism of communication is enabled by a unique interlocked design, which effectively removes the catalytic rings from the substrates by fast dethreading as soon as the first reaction has taken place. Our method not only led to a rare example of selective monofunctionalization, but also to a "molecular if function". Overall, the study presents a way to get distant functional groups to communicate with each other in a reaction-history-dependent manner by creating linkers that can ultimately perform logical operations at the molecular level.

Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the 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, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning 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 machine learning 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 machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.

Targeting the PAC1 Receptor for Neurological and Metabolic Disorders.

The pituitary adenylate cyclase-activating polypeptide (PACAP)-selective PAC1 receptor (PAC1R, ADCYAP1R1) is a member of the vasoactive intestinal peptide (VIP)/secretin/glucagon family of G protein-coupled receptors (GPCRs). PAC1R has been shown to play crucial roles in the central and peripheral nervous systems. The activation of PAC1R initiates diverse downstream signal transduction pathways, including adenylyl cyclase, phospholipase C, MEK/ERK, and Akt pathways that regulate a number of physiological systems to maintain functional homeostasis. Accordingly, at times of tissue injury or insult, PACAP/PAC1R activation of these pathways can be trophic to blunt or delay apoptotic events and enhance cell survival. Enhancing PAC1R signaling under these conditions has the potential to mitigate cellular damages associated with cerebrovascular trauma (including stroke), neurodegeneration (such as Parkinson's and Alzheimer's disease), or peripheral organ insults. Conversely, maladaptive PACAP/PAC1R signaling has been implicated in a number of disorders, including stressrelated psychopathologies (i.e., depression, posttraumatic stress disorder, and related abnormalities), chronic pain and migraine, and metabolic diseases; abrogating PAC1R signaling under these pathological conditions represent opportunities for therapeutic intervention. Given the diverse PAC1R-mediated biological activities, the receptor has emerged as a relevant pharmaceutical target. In this review, we first describe the current knowledge regarding the molecular structure, dynamics, and function of PAC1R. Then, we discuss the roles of PACAP and PAC1R in the activation of a variety of signaling cascades related to the physiology and diseases of the nervous system. Lastly, we examine current drug design and development of peptides and small molecules targeting PAC1R based on a number of structure- activity relationship studies and key pharmacophore elements. At present, the rational design of PAC1R-selective peptide or small-molecule therapeutics is largely hindered by the lack of structural information regarding PAC1R activation mechanisms, the PACAP-PAC1R interface, and the core segments involved in receptor activation. Understanding the molecular basis governing the PACAP interactions with its different cognate receptors will undoubtedly provide a basis for the development and/or refinement of receptor-selective therapeutics.