Chemical Science

The inhibition of A{beta}42 ({beta}-amyloid) fibril formation is a key therapeutic strategy in Alzheimer's disease research. Paddlewheel diruthenium complexes have shown promising activity against A{beta}42 aggregation and preformed fibril disaggregation, yet their molecular mode of action remains poorly understood. In this work, we perform atomistic simulations to explore how charge modulation influences the interactions of three analogous paddlewheel diruthenium complexes, the parent neutral complex [Ru2Cl(D-p-FPhF)(O2CCH3)3], and its anionic [Ru2Cl2(D-p-FPhF)(O2CCH3)3]- and cationic [Ru2(D-p-FPhF)(O2CCH3)3]+ counterparts (D-p-FPhF- is the N,N' -bis(4-fluorophenyl)formamidinato ligand) with A{beta}42. Our results indicate that electrostatic tuning governs binding affinity and the extent of interaction across the A{beta}42 fibril surface. As the complexes' charge changes from -1 to +1, the interaction pattern shifts from localized contacts to widespread, multi-site engagement encompassing key charged, aromatic, and hydrophobic regions of A{beta}42. This enhanced binding correlates with longer-lived, thermodynamically stable interactions at the fibril interface, which effectively lower the free energy penalty for fibril disassembly. Overall, our findings propose a mechanism in which charge-dependent activation through ligand exchange enhances fibril recognition and promotes disruptive binding modes, demonstrating the potential of charge-tunable diruthenium complexes as therapeutic modulators of A{beta}42 fibril stability.

Serotonin is a key neurotransmitter, and aptamer-based tools using the 44 nt Apt44 have been successfully developed for its in vitro and in vivo detection. Nevertheless, the structural basis of recognition by this aptamer remains unclear. Here we report high resolution NMR structures of Apt38, a 6-nt truncated variant in the third loop of Apt44, in free and serotonin-bound states. Both structures reveal a two layered antiparallel chair type G quadruplex core with three edgewise loops and a terminal duplex, forming a G quadruplex duplex hybrid structure. Serotonin binds at the G quadruplex duplex junction, stabilized by stacking, electrostatic attraction, hydrogen bonding, and hydrophobic contacts. Apt38 is preorganized for binding, whereas the longer third loop of Apt44 introduces conformational dynamics into the G quadruplex scaffold, which enables a pronounced binding triggered conformational switch in PBS buffer, explaining its sensing mechanism. Our work reveals the recognition and sensing mechanism of the serotonin aptamer and provides a framework for aptamer design in serotonin biosensing.

-Synuclein (S), a protein that plays a central role in Parkinsons disease and related synucleinopathies, is an intrinsically disordered protein (IDP) whose functional interactions and aggregation behavior can be strongly influenced by post-translational modifications (PTMs). Phosphorylation, acetylation, and other PTMs regulate Ss interactions with lipid membranes and binding partners, whereas their dysregulation is associated with aggregation and neuronal toxicity. Despite significant progress through chemical and semi-synthetic approaches, investigating the combinatorial effects of PTMs has remained challenging due to the lack of accessible, site-specific methods. Here, we present an integrated strategy combining genetic code expansion, enzymatic modification, and intein-mediated click chemistry to generate S variants bearing multiple defined PTMs and a C-terminal fluorescent label. The resulting constructs enable direct evaluation of how individual and combined PTMs influence S structure, lipid binding, and cellular internalization. Our approach expands the molecular toolkit for dissecting PTM crosstalk in S and other aggregation-prone IDPs, advancing mechanistic understanding and supporting the development of therapeutic strategies for neurodegenerative disease.

Discovering functional peptides across vast sequence space remains a formidable challenge, particularly when experimental training data is scarce. We present Minimal Data Maximal Insight (MDMI), a two-stage structure-guided computational pipeline that designs functional peptide variants using only a small, annotated dataset. Rather than relying on sequence information alone, MDMI integrates three-dimensional structural features derived from predicted peptide-protein complexes into a machine learning model that captures interface geometry and binding energetics. This structure-aware predictor, paired with a genetic algorithm for sequence exploration, reduced false positives from 70% to close to zero in an all-negative benchmark panel compared with a sequence-only model in computational benchmarking, and produced approximately four-fold more high-confidence in silico binders than state-of-the-art peptide/protein design baselines. Using the split-GFP system as a testbed, where fluorescence provides a direct functional readout of peptide-protein complementation, MDMI identified peptides with up to 38% sequence divergence from wild-type in Stage 1 while retaining measurable activity. In Stage 2, motif-guided recombination of successful Stage 1 variants produced highly divergent yet functional peptides bearing over 50% sequence difference from wild-type, revealing two distinct functional clusters in sequence space. As further validation, a top-performing candidate expressed as a full-length GFP fusion retained a GFP-like emission profile, supporting formation of a fluorescent GFP-like scaffold. These results demonstrate that structure-informed pipelines can uncover remote functional sequence space from minimal data, with broad implications for peptide and therapeutic analog discovery.

"Mirror life", self-replicating organisms composed of non-natural-chirality biomacromolecules, presents a future threat with potentially global consequences. Consequently, there is strong agreement among experts that it should not be created. However, there is some disagreement over how effective existing medical countermeasures might prove against mirror bacteria in the event that they were created. Here, we leverage computational chemistry methods including docking and molecular dynamics to determine the likely binding efficacy of existing antibiotics against natural and mirror bacterial protein targets. We find that most existing antibiotics fail to bind to mirror bacterial protein targets, unlike their natural-chirality targets. This suggests altered binding of current medical countermeasures, which may impact the antimicrobial activity against mirror bacteria were the latter were created.

The incorporation of non-canonical amino acids (ncAAs) into proteins, developed in the past 20 years, has opened new avenues with respect to protein structure, protein modification, protein-protein interaction or enzyme catalysis beyond what is possible with the 20 proteinogenic AAs. Although >300 unusual building blocks including several ncAAs have been described in nonribosomal peptides (NRPs) naturally, we aimed to further expand the scope of the underlying nonribosomal peptide synthetases (NRPS) to incorporate ncAAs beyond the naturally available ones. We have therefore systematically screened for ncAA accepting NRPS systems, applied NRPS engineering to transfer the respective ncAA-accepting parts into other NRPSs and thereby created novel peptides that were further derivatized in post-enzymatic chemical synthesis reactions directly in bacterial culture extracts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/738027v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@90552forg.highwire.dtl.DTLVardef@1c8a5e0org.highwire.dtl.DTLVardef@2549dorg.highwire.dtl.DTLVardef@1012911_HPS_FORMAT_FIGEXP M_FIG C_FIG

Precisely modifying proteins at multiple sites in their native, folded structures offers unique opportunities to answer molecular and cellular-level biological questions. Here, we present a genetic code expansion strategy for site-specific integration of a fluorophore-quencher pair comprising two non-canonical amino acids--acridonylalanine (Acd) and methyltetrazinyl phenylalanine (Tet) -- into a protein expressed in E. coli. The Acd and Tet pair requires no post-translational labeling, and quenching can be switched off by biorthogonal or photochemical reactions of Tet for convenient internal control experiments. Mechanistic studies based on Stern-Volmer quenching, fluorescence lifetime measurements, and "proline ruler" peptides established the distance dependence of quenching. As proof-of-concept, we applied this strategy to study: 1) calmodulin, a calcium-sensing protein, 2) RecA, a DNA damage sensor in bacteria, and 3) LexA, a transcriptional repressor whose activation by RecA governs acquired antibiotic resistance in bacteria. Using these proteins, we demonstrate that dual Acd/Tet labeling provides molecular-level insights into protein dynamics, enables high-throughput drug screening, and advances tools for studying protein structure-function relationships.

Developing enzymatic mechanisms for C-F bond formation remains a long-standing challenge. Here, we repurposed the biosynthetic nonheme Fe enzyme EgtB, which features a three-histidine facial triad, to catalyze C(sp3)-H fluorination reactions. Directed evolution of EgtB afforded two new-to-nature fluorine atom transferases with opposite enantiopreference, EgtBCHF1 and EgtBCHF2, with up to 28-fold improved total activity. In contrast to our previously evolved nonheme Fe fluorine atom transfer biocatalyst ACCOCHF, which contains a two-histidine-one-carboxylate facial triad, the evolved EgtBCHF variants displayed unexpected hydroxylation activity. 18O-labeling experiments showed that the hydroxy group originated from water rather than residual O2. Computational studies suggested that the three-histidine-supported Fe(III) center exhibits enhanced Lewis acidity compared to the two-histidine-one-carboxylate system, allowing deprotonation of Fe(III)-bound water to form a Fe(III)-OH species to catalyze radical hydroxylation. Primary coordination-sphere mutagenesis in EgtB and ACCO further supported the critical role of Fe coordination chemistry in controlling radical rebound reactivity and selectivity. Computational studies revealed that Fe coordination chemistry strongly influences both fluorine atom abstraction and radical rebound, with the intrinsic C-X (X = F, OH, and N3) bond forming radical rebound preference following the order N3 > OH > F. Furthermore, multivariate linear regression analysis revealed that fluorine atom abstraction is primarily governed by the intrinsic Fe-F bond strength, whereas fluorine rebound is predominantly controlled by the electronic structure of the Fe(III) intermediate. Together, these findings provide mechanistic insights into nonheme Fe enzymology and reprogramming toward selective radical rebound reactions, including challenging C-H fluorination. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/737789v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1ad85b2org.highwire.dtl.DTLVardef@1248bd4org.highwire.dtl.DTLVardef@58268dorg.highwire.dtl.DTLVardef@14b2da0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the Restricted Primitive Model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, with quantitative agreement to the more expensive generalized G-RxMC approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein's isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction and magnitude of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.

Cell penetrating thyclotides (CPTs) are synthetic molecules that promote highly efficient cellular uptake and endosomal escape of bioactive peptides. While peptides are valuable as medicinal agents, their translation to therapies is often limited by their inability to cross cell membranes. CPTs have a unique combination of chiral tetrahydrofurans and polar sidechains within a molecular scaffold that can be optimized to efficiently deliver peptide cargo into cells. The cellular uptake and endosomal escape of two peptides with anticancer biological activities but low bioavailabilities were remarkably improved after conjugation to a CPT. Using CPTs to overcome barriers to cellular uptake represents a new direction for the intracellular delivery of bioactive molecules, and will accelerate drug development for new medical therapies.

Deep generative models have become a leading approach for designing therapeutic molecules, yet efficiently exploring vast biomolecular sequence spaces remains difficult, particularly for targets with limited training data. The prior distribution that seeds a generative model shapes which regions of sequence space it explores, and recent work suggests that non-classical distributions sampled from quantum processors can serve as a structured alternative to the factorised Gaussian priors used by default. Whether such priors help on complex biological design tasks has been largely untested. Here we present the first end-to-end hybrid quantum-classical pipeline for de novo design of MHC class I-binding peptides, coupling a generative adversarial network (GAN) to latent vectors sampled from a real photonic quantum processor. Tested in silico across 131 HLA alleles, quantum-derived priors increased the yield of predicted strong binders, with the largest relative gains for understudied alleles where classical baselines perform worst. We selected three understudied alleles for further evaluation, finding that large gains coincided with broader sequence exploration at non-anchor positions while anchor specificity was preserved. On these three alleles, we validated the designs in vitro using peptide-MHC stability ELISAs, confirming that quantum-designed peptides are potent stabilisers of peptide-MHC class I complexes. These results establish structured, hardware-realisable non-classical priors as a useful inductive bias for generative peptide design, with direct relevance to personalised immunotherapies and vaccines.

Predicting enzymatic reaction mechanisms is critical for understanding enzyme function and for designing and dis-covering new enzymes. Current computational predictors rely on deterministic, rule-based dictionaries, which per-form well on in-distribution tasks but fail to generalize to out-of-distribution (OOD) chemistry. To address this limita-tion, we present EZSolver, a template-free, generative framework for polar enzymatic mechanism prediction. Powered by a flow matching predictor (EZFlow) and navigated by an evaluator-guided bidirectional beam search, EZSolver learns the chemistry of electron redistribution instead of memorizing rigid templates. Evaluated across diverse en-zyme classes, EZSolver achieves a 60.0% accuracy and an 84.6% chemical plausibility rate for full mechanism predic-tion of unseen polar enzymatic reactions. While rule-based models collapse without predefined templates, EZSolver successfully extrapolates chemical knowledge to infer uncatalogued pathways, as demonstrated during rigorous OOD benchmarking. By illuminating enzymatic chemical mechanisms, EZSolver helps pave the way for automated predic-tion of enzyme function and discovery and design of novel biocatalysts for sustainable chemistry.

Pyrrolizidine alkaloids (PA) are well-known and widespread natural products from plants, which have also been identified in several different bacteria. In the latter case, the core structure is constructed by a non-ribosomal peptide synthetase (NRPS), which then undergoes oxidative ring contraction catalyzed by a Baeyer-Villiger monooxygenase. By deploying various NRPS engineering strategies, we have successfully generated five novel peptides carrying the unusual PA moiety at their C-terminus. Nonetheless, efforts to obtain a larger library of PAs were unsuccessful. Combined computational modelling and docking experiments suggest that this failure stems from the strict specificity of the thioesterase (TE) domain at the end of the NRPS, which discriminates against peptides carrying more than two amino acids. Our work thus suggests protein design strategies by which this intrinsic limitation to NRPS engineering may be overcome in future.

Actin turnover is a fundamental cellular process essential for cell dynamics, whose control is critical for both medicine and biotechnology. However, conventional small molecules modifying actin turnover scramble the structure of actin filaments and display high cellular toxicity. MICAL enzymes oxidizing methionine (Met) residues in actin can potentially address this challenge, but their large size and multiple required cofactors make MICALs manufacturing and utilization difficult. Here we show that redox-active chiral decavanadate nanoclusters with tartaric acid are capable of site-selective actin modulation, mimicking MICALs, while requiring no cofactors, displaying high biocompatibility and being membrane permeable. Decavanadate nanoclusters serve as atomically precise "nano-enzymes" oxidizing three Met residues in globular actin, including Met-176; the latter inhibits the opening of the backdoor segment and prevents depolymerization of actin filaments. The structure of actin filaments formed after nanocluster treatment revealed no structural disturbances as confirmed by cryo-electron microscopy. The biocompatibility and bioactivity of chiral decavanadate nanoclusters was demonstrated by modulation of actin in living NG108-15 cells. Taking advantage of atomically precise structure of the nanoclusters, we show that their docking into actin can be predicted computationally, indicating the possibility of programmable actin modulation using the tools of nanochemistry.

Non-canonical amino acids (ncAAs) are valuable tools in chemical biology and biochemistry for labeling, probing, and tracking biomolecules. ncAAs that can be recombinantly incorporated using native E. coli machinery are particularly useful because they allow for global protein incorporation and avoid complex genetic code expansion. Here, we demonstrate successful incorporation of a methionine analog, L-cyanohomoalanine (Cha), by the methionyl-tRNA synthetase of E. coli into mutant superfolder GFP (sfGFP) expressed in methionine auxotroph bacterial cultures. We compare to methionine auxotroph bacterial cultures supplemented with L-methionine (Met) or L-azidohomoalanine (Aha). In control prototrophic E. coli, bacterial growth rates are inhibited with high concentrations of Aha but not Cha. However, less sfGFP is produced in auxotrophic cells supplemented with Cha compared to Aha and Met. Thus, while Cha is non-toxic to E. coli it is incorporated less efficiently into proteins than Aha or Met. Mass spectrometry confirmed that N-terminal Cha, Aha, and Met are cleaved, as expected for the sfGFP mutants. Other sites of Cha and Aha incorporation were confirmed by mass spectrometry, with labeling efficiency varying by position. Thermal melts of purified sfGFPs demonstrate that Cha and Aha labeling does not significantly perturb the protein stability. In the future, Cha may be useful for proteome labeling by wild-type methionyl-tRNA synthetase and could be implemented in metabolic pulse-labeling of newly synthesized proteins with other methionine analogs. Additionally, the nitrile moiety of Cha may be used to perform reactions orthogonal to azide/alkyne click chemistry or could serve as a vibrational reporter of the environment.

The iron (Fe) nitrogenase drives bacterial methane (CH4) formation by converting carbon dioxide (CO2) to CH4 in a single enzymatic step. Enhancing the initial CH4 formation activity of Fe-nitrogenase and expanding the product spectrum to hydrocarbon chains could lead to a route for sustainable feedstock chemicals. Here, we performed the first directed evolution campaign on the Fe-nitrogenase aimed at optimizing the hydrocarbon production. We achieved an ~8-fold increase in CH4 formation by Fe-nitrogenase expressing Rhodobacter capsulatus cultures in three rounds of site-saturation mutagenesis. The best performing mutant (F362ManfD, Y85FanfD, T360SanfD) extends the in vivo product spectrum of the nitrogenase to ethane (C2H6) and exhibits 6-fold higher rates for CO production in vitro, whereas the formation of the undesirable byproduct formate was abolished. Electron microscopy-based structural analysis identified a methionine and water potentially stabilizing the transition state and fine-tuning the CO2 reduction mechanism and activity.

Triculamin is a potent antibiotic lasso peptide first isolated in 1967. Previous studies have demonstrated that its biosynthesis follows a non-canonical logic unlike any other lasso peptide. In this study, we investigate the role of the unusual follower peptide and demonstrate that it is essential for efficient biosynthesis. Using structural prediction and targeted mutations of key conserved residues, we hypothesize that the interactions between the follower peptide and the macrocyclase create an enzyme-substrate complex that ensures delivery of the core peptide to the enzyme active site. Moreover, we demonstrate that analogs of the lasso peptide can be produced by modifying the core peptide, highlighting the substrate promiscuity of the lasso macrocyclase and identifying lysine-3 in the lasso peptide ring as the site of acetylation. Lastly, we achieve successful heterologous expression in Burkholderia sp. FERM 3421, which proves to be a superior heterologous host.

A growing body of pre-clinical research has demonstrated the therapeutic potential of chronic, continuous hypoxia (11% FIO2) for treating both rare and common forms of neurodegeneration (1). However, the chronic delivery of hypoxic gas poses both practical challenges and long-term safety concerns. We previously introduced a small molecule, ''hypoxia-in-a-pill'' regimen that combines the hemoglobin affinity enhancer (GBT440) -- which limits oxygen delivery to tissues -- with a HIF-2 inhibitor (PT2399) to prevent compensatory erythropoiesis that can be detrimental. While this regimen extended the lifespan of the Ndufs4 KO mouse model of Leigh syndrome, its efficacy still did not match that of chronic 11% FIO2. Here we report an optimized combination that now utilizes GBT601, a second-generation hemoglobin affinity enhancer with longer half-life and greater hemoglobin occupancy, again with PT2399. Here we report that the GBT601/PT2399 combination achieved therapeutic hypoxia and demonstrated strong efficacy comparable to continuous breathing of 11% FIO2 by halting neurodegeneration and even reversing neurological symptoms in three different mouse models: Leigh syndrome, Friedreich's ataxia, and Parkinson's disease. The dual targeting regimen led to a striking extension in median lifespan in the Leigh syndrome model, from a median of ~62 day to 158 days, when initiated after onset of advanced disease. Importantly, body weight was stable with the combination and it did not induce any signs of pulmonary hypertension, likely due to attenuation of HIF-2. Our findings motivate additional pre-clinical and even clinical studies to evaluate the safety and efficacy of the GBT601/PT2399 combination.

Parkinsons disease (PD) is a movement disorder characterized by the accumulation of alpha-synuclein aggregates leading to dopaminergic neuron loss in the substantia nigra. While PD has been associated with environmental and microbiome changes, our ability to assess the mechanistic impact of these factors on synuclein aggregation in cells has remained limited. Here, we designed and optimized a high-throughput optical screening system to assess the effect of metabolites and small molecules on synuclein aggregation in cell lines expressing a synuclein-fluorescent protein fusion and treated with pre-formed fibrils (PFFs). Using this assay, we identified several compounds that modulate synuclein aggregate accumulation in cells, including harman, a {beta}-carboline that led to reduced synuclein aggregation. We further investigated the transcriptional effect of harman and PFFs and identified changes in peroxiredoxins as a potential mechanism linking harman to aggregate accumulation. Altogether, this work establishes a pipeline to prioritize small molecules that can impact synuclein aggregate formation.

Prion diseases are neurodegenerative disorders associated with the structural conversion of the cellular prion protein (PrPc) into its misfolded infectious isoform (PrPSc). Despite substantial efforts, no disease-modifying therapy or cure is currently available. Here, we present an integrated computational-experimental pipeline for the rational design of cyclic peptides targeting PrPc to inhibit its pathogenic conversion. Starting from crystal structures of antibody-bound mouse PrPc, we develop a rational design strategy combined with iterative molecular dynamics simulations and sequence optimization to generate peptides with enhanced binding and structural impact. Three candidates were selected for experimental validation. Our results show that PH1 (49YGPDPSDSYT58, antibody numbering) that binds stably to the &alpha2-&alpha3 interface most effectively reduced PrPSc levels in GT1-7 cells, essentially by inducing allosteric rearrangements that reinforce the intramolecular helical bundle. PL1 (89GQSNTKPYT97) and PL2 (89RQSNTWPYT97) binding the &beta1-&alpha1/&alpha3 junction exerted more modest effects due to the potential competition of the flexible tail to bind at this site. These results establish a mechanistic link between peptide-induced stabilization of PrPc and inhibition of prion propagation and provide a generalizable framework for designing conformational stabilizers of aggregation-prone proteins.