Chemical Science

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.

Allosteric and cryptic binding sites provide unique opportunities in drug discovery for achieving selectivity, reducing toxicity and unlocking challenging therapeutical targets. Yet, cryptic pockets are particularly elusive in that they are difficult to capture with experimental methodologies, and therefore often remain undetected. Computational approaches for flexible cryptic site detection traditionally involve extensive unbiased molecular dynamics simulations, or more efficient but computationally still highly demanding biased MD alternatives that make use of enhanced sampling along low-frequency motions of the protein structure. Here we utilize a new variant of the recently introduced and orders of magnitude faster, MD-free COVIB algorithm to identify cryptic pockets efficiently in two challenging targets, the KRAS oncoprotein and the N-terminal domain of the STAT5B transcription factor. Using experimental and AI-modelled structures, respectively, we identify numerous cryptic sites, some of which have not yet been reported. The sites are prioritized and validated with our pharmacophore optimized photoaffinity-tagged fragment library via MS-based peptide mapping and NMR spectroscopy. Besides identifying new, targetable regions on the surface of these important oncotargets, we have established a protocol combining COVIB-based cryptic pocket detection and photoaffinity-based experimental peptide mapping as a robust approach for exploring unique and tractable cryptic pockets.

The aggregation of Tau protein into straight filaments (SFs) and paired helical filaments (PHFs) is central to Alzheimers disease (AD) pathology and a key target for therapeutic inhibition. Graphene quantum dots (GQDs) are biocompatible nanomaterials that have shown promise in inhibiting amyloidogenic protein aggregation across related neurological pathologies. The effect of GQD functionalization on interactions with Tau aggregates (TAs) is poorly understood, though recent evidence suggests that anionic GQDs are effective TA inhibitors. In this study, we survey how GQD functionalization influences binding to SFs and PHFs to guide future development of therapeutic GQDs. We identify binding sites in SFs and PHFs, dock our GQD library to these sites, and perform molecular dynamics simulations on promising complexes, totaling 28 {micro}s of sampling. We discover that anionic GQDs preferentially bind to the positively charged SF large protofilament interface, whereas in PHFs, anionic GQDs have a modest binding preference for the C-shaped curve region. Binding of GQDs at the C-shaped curve in both TAs induces distinct protofilament conformational dynamics resembling a pinching motion to capture the GQD. Together, these binding modes may represent early intermediates of the TA disaggregation mechanism. We find that functional groups capable of possessing a negative charge (e.g., COO-, O-, and S-) produce impressive binding affinities. We propose that enriching these functionalizations during GQD synthesis and preparation, particularly sulfur as it is less studied, may yield more potent TA inhibitors and generalize to other amyloid pathologies with positively charged fibril cores. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/741532v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@78f560org.highwire.dtl.DTLVardef@135834eorg.highwire.dtl.DTLVardef@3f9025org.highwire.dtl.DTLVardef@110ade3_HPS_FORMAT_FIGEXP M_FIG C_FIG

Selenocysteine (Sec), the 21st amino acid, is a rare non-canonical amino acid that represents an attractive target for protein engineering due to its desirable chemical properties such as high affinity for metals, strong nucleophilicity, and reversible covalent bond formation. To bypass the natural constraints on Sec placement within proteins, several strategies have been developed to rewire the native translational machinery to enable site-specific incorporation. However, these usually abolish the quality control mechanism that excludes the serine-charged selenocysteinyl-tRNA (Ser-tRNASec), the immediate biosynthetic precursor, from translation resulting in heterogenous protein species. This challenge is confounded by a lack of genetic tools to accurately report the selenylation state of the tRNA pool as most are blind to competing process of Ser incorporation, which can only be observed using analytical methods. To resolve this issue, we have developed a new fluorescent reporter, Selenocysteine Adjusted Ratiometric Chromophore (SeARCh), which exhibits two distinct spectral outputs dependent on the incorporation of either Ser (red) or Sec (green). Using SeARCh, we define several factors which influence the observed Sec:Ser ratio and construct a new hybrid biosynthetic pathway with improved performance, achieving 90% Sec incorporation. Furthermore, SeARCh displays unusually complex mass spectra due to the isotope distribution of selenium and heterogenous nature of the protein in solution and we report specific methods to account for this behaviour and precisely quantify the rare Ser-containing species found at high Sec incorporation efficiencies. Our findings suggest that the equilibrium between selenoprotein and tRNASec expression levels is a key driver of incorporation efficiency and implies a process that is broadly biosynthetically constrained. Collectively these tools represent a significant advance in the metrology of selenocysteine biosynthesis and incorporation and can be used to inform and standardize future engineering efforts.

As a consequence of the overuse of conventional antibiotics, there is currently an unprecedented increase in antibiotic resistance in newer generations of pathogenic bacteria. This growing problem has led scientists to discover novel medications that could potentially reduce the usage of antibiotics, such as antimicrobial peptides (AMPs). Recent study has demonstrated that pardaxin could bind deeply into the surface of a lipid model membrane and inhibit the growth of pathogenic bacteria, including Staphylococcus aureus and Escherichia coli. In this work, the antibacterial efficacy of pardaxin was extended further by performing selective in silico substitution, driven by deep learning, Evolutionary-Scale Cambrian (ESMC) combined with conventional AMP design principles. Principal component analyses of the ESMC embeddings combined with conventional design models produced a set of single-and multiple-mutant analogues of pardaxin, which were predicted and validated with experimental data to exhibit selective antimicrobial properties against either E. coli or S. aureus, respectively. Atomistic molecular dynamics simulations further supported the notion that alpha-helical stability is a critical predictor of inner membrane activity, strongly correlated with their selective antimicrobial action tested in the lab. Overall, the findings highlighted a promising application of deep evolutionary machine learning techniques for screening a range of novel AMPs for selective antimicrobial agents.

Designing photochemical properties in proteins is a frontier challenge in biomolecular design, which, when realised, will open new avenues in catalysis and biomolecular electronics. Here we demonstrate an integrated computational design framework, supported by ultrafast spectroscopy, combining protein design, quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) and electronic structure calculations, to control flavin excited states in de novo proteins. Iterative design yields FC-4D2, a flavocytochrome with covalently bound flavin and non-covalently bound heme B and Fl-4D2, where the heme-binding site is removed. Both proteins are highly thermostable, and access all physiologically relevant flavin redox and protonation states, while high-resolution X-ray crystallography confirms the near-[A]ngstrom-level accuracy in design. Incorporating QM/MM MD and linear-response time-dependent density functional theory (LR-TDDFT) into the design workflow enables engineering of a flavin-tryptophan charge-transfer variant, Fl-4D2CT, that modifies the flavin absorption spectrum, substantially alters its redox properties and suppresses fluorescence. Ultrafast studies reveal how protein design reshapes the excited state relaxation timescale and mechanism. Our study establishes a modular strategy for programming flavin photochemistry in designed proteins.

Computational modeling tools have enabled detailed exploration of the structural dynamics of nucleic acids at the nanoscale. Despite these developments, a unified platform for creating multiscale models of topological DNA structures, which are of fundamental importance across rapidly converging biological disciplines, is lacking. Here we present a GUI-enabled topological DNA design tool, SketchDNA (SDNA), that facilitates a user-friendly and quick interface to create all-atom and coarse-grained models of DNA structures with tunable topological parameters. Building on the modular, open-source framework of SDNA, the software can be readily integrated with both emerging and existing DNA design platforms, such as oxDNA, mrDNA etc. We demonstrate the utility of SDNA computational framework by simulating conformational dynamics of three representative topological DNA structures, minicircles, catanenes, and Borromean rings. Analyzing the multiscale molecular dynamics (MD) simulation trajectories of these topological DNA systems, we characterize their equilibrium structure, fluctuations, and topological properties. The multiscale SDNA toolkit broadens the applications of MD simulations by enabling in-situ characterization of the biophysical properties of topological DNA nanostructures and is expected to serve as a valuable tool for the broader research community in molecular biophysics and DNA nanotechnology. SDNA is available as a free-to-use webserver at https://sdna.biotech.iith.ac.in while the source code is also available at GitHub repository.

The de novo design of proteins that bind chemically complex small molecules has broad chemical and biological implications, but strategies typically rely on a small set of protein scaffolds and require extensive experimental screening. Here, we computationally designed proteins around a minimal aromatic {pi}-stacking motif to bind the anthracycline anticancer drug doxorubicin. Experimental characterization of twelve proteins revealed a {micro}M doxorubicin binder; two additional design cycles improved scaffold stability and binding affinity to yield an 85-residue protein that binds doxorubicin with a dissociation constant of 85 nM. An X-ray crystal structure of the protein-drug complex confirmed the accuracy of the designed {pi}-{pi} stacking interactions. The designed protein could act to protect cultured cells from doxorubicin-induced cytotoxicity. Unlike previous ligand-binding protein designs based on repeat proteins or naturally occurring folds, the designed protein adopts a previously unobserved 5-helix globular fold, indicating that a broader space of folded, functional proteins exists even for compact tertiary structures smaller than 100 residues. These results demonstrate that motif-guided generative protein design can discover compact de novo protein folds capable of high-affinity recognition of chemically complex small molecules.

Antibiotic resistance driven by {beta}-lactamases has become a major threat to public health. Here, we propose a computational protocol for rapid estimation of {beta}-lactamase activity towards carbapenem antibiotics from sequence alone. Starting from AI-predicted acylenzyme complexes, only approximate transition state ensembles are sampled to compute catalytic electric fields, which strongly correlate with experimental activation barriers (R2 = 0.8). Compared with full quantum mechanics/molecular mechanics (QM/MM) reaction simulations, the protocol substantially reduces the amount of configurational sampling and thus computational cost. Our protocol enables efficient identification of carbapenem breakdown efficiency, further highlighting the important role of electric fields in enzyme catalysis.

Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost, leaving the field heavily dependent on expert intuition. Here we present a scalable physics-based framework that combines field-aware machine learning with molecular mechanics to capture enzyme electrostatics at quantum-mechanical accuracy while enabling efficient, atomistic exploration of reaction free-energy landscapes. Coupled with microkinetic modelling, the framework translates molecular free-energy landscapes into catalytic rates and selectivity across competing, multistep reaction pathways. Applied to a newly engineered oxidative amidase (OxiAm), the framework predicts catalytic rate constants with near-experimental accuracy, quantitatively resolves the selectivity between hydrolysis and aminolysis, and generalizes across substrates, mutations and enzyme homologues. Transition-state ensemble analysis further reveals the reaction mechanism and guides the design of enzyme variants for pharmaceutical synthesis. By bringing chemical accuracy and high-throughput sampling to enzyme catalysis, this approach shifts rational design from static, empirical practice toward dynamic, free-energy-driven design, and should accelerate the engineering of biocatalysts.

Gaussia luciferase (GLuc) has broad biotechnological applications owing to its bioluminescence, yet its catalytic mechanism remains poorly understood and rational engineering efforts are hindered by the absence of a structural model. This gap stems from GLuc's highly dynamic nature, which allows it to populate a wide range of conformations rather than adopt a single, well-defined fold. Aiming to bridge this gap, in this work we employ structure predictors and conformational sampling techniques to prod GLuc's structure. Our results indicate that structure predictors such as AlphaFold, as well as ensemble predictors such as BioEmu, fail to adequately sample this conformational space, and even extended molecular dynamics simulations do not yield converged conformational ensembles. However, enhanced sampling via PT-WTE provides a more satisfactory description of GLu's conformational landscape, allowing us to cluster distinct conformational basins, identify loosely defined substrate-binding pockets, and characterize the conformational changes induced by substrate binding. These insights advance our understanding of GLuc's catalytic mechanism and inform future efforts to engineer improved variants.

Virtual screening promises access to tens of billions of synthetically accessible, diverse compounds, yet it is rarely used as a primary hit-discovery strategy in contemporary drug-discovery campaigns. We argue that this gap reflects the real-world underperformance of the underlying docking and scoring methods: classical docking is generalizable but limited in accuracy by simple functional forms and parsimonious parameterization, whereas recent machine-learning approaches are highly expressive but do not generalize well to novel molecules and pockets, their reported accuracy often inflated by train-test leakage. To address these challenges, we introduce DODock and DOScore, docking and scoring ML/physics hybrid frameworks that are also highly expressive, yet generalize much better out of distribution compared with the prior ML approaches. This generalization has been prospectively tested in several ways. First, DODocks blind prediction of a drug candidate binding to PCSK9 was compared to the crystal structure that was subsequently solved, recovering the binding pose to 1.2 [A] RMSD. We also used DODock and DOScore in prospective virtual screening campaigns against four therapeutic targets, spanning an ectoenzyme (CD73), a kinase (IRAK4), an extended-substrate protease (FXI), and an allosteric inhibition of protein-protein interface (IL17). These screens yielded many chemically novel, biochemically and cellularly active inhibitors. In the case of CD73, which is a historically challenging target for virtual screening, our screen resulted in a roughly hundredfold improvement in hit rate over a recent machine-learning screen. Our results indicate that the apparent ceiling in the accuracy of virtual screening that seemed to have somewhat plateaued in the last two decades is not fundamental, and that structure-based interrogation of ultralarge chemical space may eventually become a credible primary route to novel chemical matter.

Doxorubicin (DOX) is a clinically important anthracycline whose anticancer activity is closely associated with DNA binding. However, the structural basis by which metal coordination modulates DOX-DNA association remains poorly understood. Here, an integrated computational and spectroscopic approach was employed to elucidate the role of Cu(II)-coordination in modulating the structure of DOX and its DNA binding. DFT, molecular docking, and molecular dynamics simulations established a stable intercalative association of DOX with DNA. At the same time, DFT-based characterization of Cu(II)-DOX revealed coordination induced structural and electronic changes consistent with reduced conformational freedom and increased rigidity. This led to the hypothesis that Cu(II) coordination may favor an intercalation-compatible configuration of DOX. UV-Vis and competitive fluorescence studies experimentally supported Cu(II)-dependent modulation of DOX-DNA association, with UV-Vis analysis indicating a substantial tenfold increase in apparent DNA-binding affinity and fluorescence measurements confirming pronounced perturbation of DNA-associated ethidium bromide. Collectively, the findings establish a coherent relationship between Cu(II) coordination, DOX conformational modulation, and DNA recognition, highlighting metal coordination as a potential strategy for tuning the biomolecular interactions of anthracycline therapeutics.

Transition state (TS) generation is typically evaluated by geometric similarity, yet low root-mean-square deviation (RMSD) does not establish first-order saddle-point character or reaction-path connectivity. Here we introduce Kairos-GFM, a deterministic equivariant flow-matching model that combines a two-dimensional reaction graph with the Kabsch-aligned mean of three-dimensional reactant and product geometries. Using 881 Transition1x reactions whose reference TSs passed strict saddle-point and bidirectional intrinsic reaction coordinate validation, Kairos-GFM generates one candidate in 0.0118 s with a median RMSD of 0.14 [A]. A hierarchical physical-validation framework--spanning reaction-center geometry, raw single-point energy, DFT saddle-point optimization, harmonic frequency analysis, and IRC endpoint matching reveals that low RMSD and saddle-point convergence do not guarantee the intended reaction channel. Kairos-GFM recovers the target channel in 75.94% of reactions, outperforming diffusion-based baselines but remaining below transport-based methods. These results position Kairos-GFM as a rapid initial-geometry generator for quantum-chemical refinement and provide a transferable validation standard that distinguishes geometric similarity from physical fidelity.

Aromatic side chains carry the optical and redox chemistry of peptides, and lengthening one is widely expected to extend its conjugation and narrow its HOMO-LUMO gap. We test that expectation with plane-wave density functional theory on 22 isolated neutral peptides NH2-(X)n-COOH (X = Phe, Tyr, Trp, Cys; n = 1 to 5, and n = 10 for Cys and Trp). The gap does narrow, at the same rate for all three aromatic residues, i.e., -0.17 to -0.18 eV per residue, even though benzene, phenol and indole are very different chromophores. Residue-resolved projections reveal why: nothing delocalises. Every aromatic frontier orbital is 97 to 100 % confined to a single residue and the HOMO participation number saturates at one ring. Each residue instead keeps its own local gap: for Trp it is invariant to within 0.03 eV over n = 2 to 10, while over that same span the global gap falls by 1.19 eV. The global gap is therefore an extreme-value functional of the residue site energies, gap(n) =<g_f>-{Delta}(n), whose alignment penalty accounts for the entire effect: the polar backbone generates a monotonic internal electrostatic ramp that leaves the two gap edges at opposite ends of the chain, 32 [A] apart. We set out the resulting design rules for peptide-based bio-organic semiconductors.

Proteolysis-targeting chimeras (PROTACs) are large, flexible molecules that frequently extend beyond conventional drug-like chemical space, including Lipinski's rule-of-five boundaries. Their membrane permeability has often been linked to molecular chameleonicity and the ability to adopt compact conformations with reduced polar surface exposure; however, the underlying conformational determinants remain unclear. Here, we investigated 20 PROTACs spanning distinct passive-permeability classes using enhanced-sampling molecular dynamics (ES-MD). All permeability classes accessed compact states with low radius of gyration (Rg) and polar surface area (PSA), indicating that compact-state accessibility alone does not distinguish permeable from non-permeable PROTACs. We therefore introduce maximum ring size (MRS), defined as the size of the largest intramolecular hydrogen-bonded ring, as a metric for identifying compact conformations stabilized by long-range intramolecular hydrogen bonding. Like the other descriptors examined, MRS neither separates nor ranks the permeability classes, but helps locate folded states in the large ensemble. Representative compact conformations from selected highly permeable, moderately permeable, and non-permeable compounds were subsequently examined by all-atom unbiased MD in water and chloroform. The highly permeable PROTAC held a closed-flat ensemble in both environments, but in two structurally distinct arrangements, whereas the other two redistributed across heterogeneous ensembles. Passive diffusion therefore appears to depend on the population, stability, and environment-dependent persistence of permeable-like states rather than on compactness alone.

Amino acids and peptides are promising building blocks for aqueous biomolecular CO2 capture systems, yet the coupled thermodynamics governing carbamate formation, proton transfer, carbonate speciation, and hydration remain difficult to resolve experimentally. Here, we establish isothermal titration calorimetry (ITC) as a quantitative platform for characterizing these coupled processes by integrating calorimetry with pH titrations, NMR spectroscopy, and a mechanistic thermodynamic model. Using L-lysine, L-arginine, and a series of Lys- and Arg-containing peptides, global fitting of ITC thermograms yielded thermodynamic parameters describing protonation and carbamate formation that accurately reproduced independent pH titrations and NMR-derived speciation. The analysis revealed that the characteristic biphasic calorimetric response originates from the coupled carbonate-amine equilibrium network and buffer collapse rather than carbamate saturation. Lys formed -, {varepsilon}-, and ,{varepsilon}-dicarbamates and exhibited more favorable apparent carbamate thermodynamics than Arg with the {varepsilon}-carbamate lying among the most favorable carbamate-forming amine sites reported for aqueous amines. Model-guided exploration of the fitted thermodynamic landscape further demonstrated that maximizing total CO2 retention, amine-mediated capture, and carbamate formation are distinct optimization problems governed by different combinations of pH, temperature, and CO2 loading. Extension to systematically spaced Lys-containing peptides showed that inter-amine separation alone does not control carbamate stability, highlighting the dominant role of the local thermodynamic environment in biomolecular CO2 capture. This work establishes ITC as a powerful experimental approach for extracting CO2-amine thermodynamics and provides a predictive framework for the rational design and optimization of amino acid-, peptide-, and protein-based carbon capture systems.

Rho guanosine triphosphatases (GTPases) are molecular-switches implicated in neurodegenerative diseases, yet targeting them through competitive inhibition remains a challenge due to their high affinity for guanine nucleotides. Guanine nucleotide exchange factors (GEFs) catalyse GDP-to-GTP nucleotide exchange to activate GTPases, providing an alternative opportunity for GTPase modulation. Herein, we describe a complex-targeted strategy to inhibit nucleotide exchange with covalent molecular glues that engage the Kalirin-Rac1 GEF-GTPase complex at the nucleotide binding site are sequester the GEF Kalirin. Fragment hits were identified through XChem and in silico screening, and a fragment merging approach resulted in the generation of covalent inhibitors RS-009 and MC-278. Multiple analyses demonstrate our compounds inhibit nucleotide exchange both through competition with the nucleotides and by stabilising a ternary inhibitor-Rac1-Kalirin complex, thereby trapping Kalirin in a non-productive state and reducing GEF turnover. Biochemical selectivity screening and cellular activity-based protein profiling (ABPP) show that selectivity can be achieved across distinct GEF-GTPase complexes, which may result in improved spatiotemporal control over targeting the GTPase alone. This work provides evidence for targeting GTPase signalling via stabilization of the GEF-GTPase complex in a unique covalent molecular glue mechanism and provides the basis of a chemical probe or therapeutic.

Thioamides are natural post-translational modifications of the peptide backbone and can be introduced synthetically to probe protein folding or functionalize peptides for translational applications. In this work, we demonstrate that thioamide-containing peptides with C-terminal thioesters can be efficiently generated using Knorr pyrazole activation and used in subsequent native chemical ligation reactions to generate thioamide containing proteins. We compare this method to acyl azide activation and find that both routes provide similar yields. We also investigate ultrasound-mediated desulfurization of the ligation site cysteine for potential advantages over chemical radical initiators. Scaling up our syntheses allows us to study thioamide perturbations to the {beta}-sheet region of the B1 domain of protein G (GB1) as well as {beta}-strand interactions in amyloid fibrils of the Parkinsons disease protein -synuclein. In both contexts, we observe dramatic destabilization of the {beta}-sheet networks, manifested in decreased GB1 thermal stability and altered folding and slowed aggregation of -synuclein. These findings illustrate the impact that a single atom substitution can have on cooperative hydrogen bonding networks and prompt future study of both systems.

Cyclic peptides can engage targets beyond the reach of small molecules, but their discovery typically relies on libraries reused across targets, diversification around a known interaction motif, or computational methods limited to a handful of non-canonical building blocks. Here, we present nCycle-Forge, a simulated annealing framework that designs cyclic peptide binders by iteratively substituting building blocks and scoring each proposal by cofolding with the target. We designed, synthesized, and screened 1,472 cyclic peptides against the active-site pocket of Thrombin and the shallow p53-binding surface of MDM2 from the target sequence alone and without fixing binding mo-tifs. The library yielded chemically diverse hits against both targets in a single round without experimental optimization, with hit rates comparable to or up to 24-fold higher than in larger experimental libraries. Purified designs reached sub- to low-micromolar potency, with the best Thrombin inhibitor at Ki 0.14 M and the best MDM2 binder at EC50 2.4 M. Surface plasmon resonance confirmed direct binding, competition with or-thosteric inhibitors at both sites, and weaker binding under conditions expected to linearize the designs. nCycle-Forge requires no building block-specific parameterization, conformer generation, or retraining, and provides a target-focused route to functional cyclic peptides beyond the canonical amino acid alphabet.