Chemical Science

Switchable {beta}-barrel-type fluorescent proteins are essential genetically encoded probes for super-resolution imaging. The space required for chromophore cis-trans isomerisation can also provide an opportunity to introduce bulkier chemistry at the 3-position of the phenolic ring. Here, we report, to our knowledge, the first successful genetic encoding of 3-cyano-L-tyrosine (3CNY) into a protein. Using genetic code expansion, the cyano-containing amino acid was incorporated directly into the chromophore of mKate, a pH-dependent switchable red fluorescent protein. In mKate, the chromophore adopts a fluorescent phenolate cis state at physiological pH, transitioning to a phenolic trans state under acidic conditions. Substitution of the native tyrosine with 3CNY yields a functional protein exhibiting hypsochromically shifted spectral properties. Time-dependent density functional theory (TD-DFT) calculations indicate that 3CNY incorporation results in a trans state at pH 8. Unlike mKate, the trans state is fluorescent. In contrast, incorporation of 3-chloro-L-tyrosine (3ClY) preserves the preference for the cis phenolate state. Molecular modelling suggests that the cyano group can form stabilising hydrogen bonds with residues S143 and S158, promoting the trans configuration. DFT analysis further indicates that the electron-withdrawing cyano group perturbs conjugation across the chromophore, potentially lowering the barrier to cis-trans isomerisation. Conversely, wild-type and 3ClY variants maintain polarised HOMO and LUMO distributions in the cis state, supporting stronger conjugation and a reduced HOMO-LUMO gap. Overall, the introduction of a genetically encoded 3-CNY tyrosine analogue into a fluorescent protein chromophore expands our mechanistic understanding and enables incorporation of a new chemical tag directly into the chromophore.

"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.

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.

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

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.

The MDM2-p53 protein-protein interaction is a validated oncology target, yet no food-derived linear peptide has been documented to engage the canonical three-anchor MDM2-p53 interface. We developed a multi-stage computational pipeline (PepVeg) to screen 22 plant and fungal proteomes (337,646 proteins) for MDM2-binding peptides, applying sequential in silico hydrolysis, physicochemical filtering, ESM-2 embedding-based dimensionality reduction, and pharmacophore-driven selection. Twenty-six candidates were evaluated by AlphaFold 3 (AF3) co-folding against MDM2(25-109), yielding 15 binders (iPTM >= 0.75; 58% of evaluated). A 36-peptide benchmark with 29 hard negatives confirmed AF3 discriminative power (Cohens d = 3.41; 95% CI: 1.94-4.88; Hedges g = 3.32; zero overlap). The lead candidate, SPAFESTWDILK -- a tryptic fragment of Zingiber officinale histone deacetylase (UniProt A0A8J5FLH2) -- was evaluated by eight computational assessments: AF3 Server (iPTM 0.83, SD 0.01), Protenix (iPTM 0.923), Chai-1 (iPTM 0.891), EvoEF2 (-55.57 EEU), two GROMACS simulations (no dissociation across two force fields), and two MM-PBSA calculations (-75.30 (SD 4.92) and -55.07 (SD 2.86) kcal/mol). The W8A point mutant produced an iPTM drop of 0.201, closely paralleling the p53 W23A drop of 0.193; we predict W8A substitution will abolish binding. SPAFESTWDILK ranked only #890/2,000 by ESM-2 similarity and was recovered solely through pharmacophore matching, demonstrating that no single pipeline stage alone is sufficient. To our knowledge, this is the first food-database-derived linear peptide with multi-convergent computational evidence supporting engagement of the canonical three-anchor MDM2-p53 interface. Experimental validation by SPR/ITC is warranted.

We present BoltzMol-1, a small-molecule hit discovery pipeline, centered on an optimized version of Boltz-2, explicitly adapted for prospective discovery. Reliable hit discovery that generalizes across target classes (rather than only the well-characterized families that dominate existing ligand data) would broaden the range of biology accessible to small-molecule intervention and reduce reliance on resource-intensive high-throughput screening. Towards this goal, the system prioritizes compounds for rapid experimental validation by coupling model-driven ranking with streamlined procurement from commercial catalogs. To improve developability at the point of selection, we introduce a suite of ADMET models for kinetic solubility (logS), lipophilicity (logD), and Caco-2 permeability. These models act as an early triage layer, systematically filtering out compounds with unfavorable physicochemical and absorption properties prior to synthesis or purchase. Across a panel of ten targets (most with no representation in the underlying affinity training data) we observe strong prospective performance on challenging systems. Functional actives or binders were identified for 6 of 10 targets, despite modest experimental budgets of 28-96 compounds per target. These results include successes on receptors and enzymes traditionally considered difficult for structure- or ligand-based approaches. Collectively, this work establishes a practical framework for low-throughput, cost constrained discovery campaigns capable of delivering chemically tractable binders with favorable property profiles.

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

Mitochondrial complex I is a major source of reactive oxygen species (ROS), but whether radical pair spin dynamics contribute to the regulation of ROS-associated reactions remains unknown. Here we integrate cryo-electron microscopy structure-guided oxygen sampling with radical pair quantum dynamics to determine how the molecular architecture surrounding flavin mononucleotide (FMN) shapes modelled spin-dependent radical-pair reaction yields. Monte Carlo sampling revealed a broad ensemble of sterically accessible oxygen configurations, whereas spin sensitivity was concentrated within a restricted near-contact region centred at approximately 3.3-3.4 [A] from the FMN reference centre. This localisation was defined by the integration of structural accessibility with magnetic field and spin dephasing sensitivities and spatially overlapped with an exchange-hyperfine crossover regime favourable for singlet-triplet interconversion. Simulations of structural fluctuations further show that equivalent perturbations generated greater variability in singlet reaction yields within a hotspot than outside it, identifying a localized regime of enhanced structural responsiveness. These results suggest that the FMN binding pocket may act as a structure-dependent amplification layer that converts small changes in radical pair geometry into heterogeneous spin-dependent reaction outcomes. Our findings establish a framework linking experimentally resolved protein architecture to radical pair spin dynamics and identify structural constraints that may shape spin-dependent ROS chemistry in mitochondrial complex I.

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.

Mechanistic elucidation and inhibition of the pathogenic aberrant mitochondrial localization of the RNA/DNA-binding protein, TDP-43, can help in the therapeutics of the neurodegenerative disease amyotrophic lateral sclerosis (ALS). A mitochondrial localization sequence of TDP-43, M1, is largely solvent inaccessible, therefore, how it interacts with the mitochondrial import machinery to facilitate TDP-43s transit to mitochondria is unclear. Towards this, we examined the unfolding TDP-43s N-terminal domain (NTD) that hosts M1, using equilibrium all-atom molecular dynamics (MD) simulations, and observed an early loss of the hydrogen-bonded interactions between {beta}4-{beta}5 bridge and the interactions involving residues Phe-35 and Gly-40 of M1, indicating structural lability of M1 to become solvent-accessible that may enhance its interaction with the mitochondrial receptor(s) for import. Furthermore, via virtual screening of 2,115 FDA-approved and 515,545 non-FDA-approved small molecules from ZINC15 database towards binding to M1 and inhibiting TDP-43s mitochondrial import, we identified a molecule, ZINC73240059, that was previously characterized as an inhibitor of MAP kinase-activating protein kinase 2 (MAPKAPK2). ZINC73240059 remains stably bound to M1 of NTD during MD simulations manifesting negative Gibbs free energy ({Delta}G) with significant contribution from Pro-36 of M1. Overall, ZINC73240059 can be a molecule of interest towards thwarting TDP-43s pathogenic mitochondrial localization in ALS.

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.

Understanding protein structural dynamics is central to elucidating biological function and guiding therapeutic discovery. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) typically offers peptide-level, and sometimes residue-level, time-dependent insights into protein structure, conformational dynamics and/or ligand binding. Yet, translating HDX-MS data into atomic-resolution insights and deriving mechanistic understanding remains a key challenge. Integrative strategies which utilize HDX-MS to inform computational modeling or simulations, traditionally leverage HDX-MS data with physics-based approaches through the calculation of protection factors models. Here, we developed MInt-HDX, a hybrid physics-based, machine- learning framework trained on differential HDX-MS signatures across 11 protein-ligand systems or 1032 individual peptides, using eXtreme Gradient Boosting (XGBoost) to guide small-molecule ligand docking and pose selection. By leveraging XGBoost-predicted interacting residues with three-dimensional clustering and convex-hull geometric algorithms, MInt-HDX first generates HDX-guided candidate docking sites in 3D for physics-based molecular docking and then, following docking, employs HDX-MS-informed XGBoost filtering and scoring functions for ligand- pose ranking. MInt-HDX was validated across 3 protein-ligand systems, consistently resulting in Ligand-RMSD within 3 [A] of the crystallographic ligand conformation, individual steps of MInt- HDX were optimized and its overall performance was assessed against HDX-MS data quality factors and benchmarked against common physics-based and machine learning based docking approaches. Together, this work highlights how machine learning, informed by HDX-MS and aided by physics-based approaches, can bridge the gap between solution-phase HDX-MS data and structural modeling to accelerate protein-ligand discovery pipelines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/738285v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1f7d1dorg.highwire.dtl.DTLVardef@14f074aorg.highwire.dtl.DTLVardef@167a9b1org.highwire.dtl.DTLVardef@b5c502_HPS_FORMAT_FIGEXP M_FIG C_FIG

Stereochemistry underlies structure-function relationships across biology and materials science, ranging from proteins to electronic and spintronic materials. In this work, we investigate the electron transport properties of different oligopeptide stereoisomers using experiments and computational modeling. Single-molecule electronic experiments show that stereochemical modifications in tyrosine-based peptides lead to significant variations in molecular conductance along the peptide backbone due to enhanced stacking interactions and electronic coupling of aromatic side chains. In addition, stereochemical variations in alanine-based peptides give rise to changes in conductivity due to secondary structure interactions arising from {beta}-turn conformations. All-atom molecular dynamics (MD) simulations and quantum mechanical calculations are used to understand the molecular origins of the effect of stereochemistry on the structural and electronic properties of peptides. Overall, this work shows that stereochemical modification of non-terminal amino acids effectively controls electron transport due to aromatic side chain interactions or secondary structure effects. These insights open new avenues for the molecular design of peptide-based electronic materials with enhanced function.

Flavins are organic redox cofactors central to metabolism and uniquely capable of acting as extracellular electron shuttles. For life to have emerged, it must have disengaged itself from its stationary geochemical environment, a step requiring mobile redox-active components. The role of flavins at life's origin has been debated for decades, centered on their capacity for both one- and two-electron chemistry, distinguishing them from nicotinamides and iron-sulfur clusters. Here we chart the abiotic reduction of flavin mononucleotide (FMN), flavin adenine dinucleotide (FAD), and riboflavin under hydrothermal conditions (40 {degrees}C, 1 bar N2 or 5 bar H2, pH 6, 8, and 10) by nickel (Ni) and iron (Fe). Flavins show greater environmental versatility than hydride carriers such as NAD and can harvest electrons from metals that would otherwise reduce water's protons to H2. Reduction is favoured under acidic conditions, while increasing molecular charge at higher pH impedes electron transfer. Ni acts as a hydrogenation catalyst, reducing deprotonated flavins via hydride transfer, suggesting mineral composition could have influenced geochemical selection of early electron carriers. Reduced FMNH2 and FADH2 were tested as electron shuttles toward Fe3+-containing minerals, revealing that FMNH2 enables faster mineral dissolution than FADH2. We further demonstrate complete redox cycling of FMN through Ni-assisted H2 reduction and subsequent oxidation by magnetite (Fe3O4) under inert atmosphere, releasing Fe2+. This study highlights the versatility, stability and redox chemical capabilities of flavins in prebiotic context.

-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.

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.

Natural product synthesis by non-ribosomal peptide synthetases (NRPS) is greatly defined by the substrate selectivity of the adenylation (A) domains. Previous assays for specificity determination were mainly performed in vitro and were requiring protein purification. In this work, we developed - based on NRPS engineering - a novel in vivo assay suitable for high-throughput application named ASCR (A domain screening). Using the recently described XUT fusion sites, A domains and their upstream condensation domains were assembled as di-domains to characterized NRPS model system, which allowed detection of defined tripeptide products via mass spectrometry directly after cell culture extraction. We evaluated the assay by screening in total 54 A domains from five known and seven uncharacterized NRPS, covering a broad range organism taxonomy and GC content of the investigated NRPS-encoding genes. Additionally, we applied the assay to elucidate and confirm the structures of novel cyclic pentapeptides derived from three novel NRPS from Photorhabdus temperata K122.

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.