Communications Chemistry

Developing novel drugs against membrane proteins is a major challenge in drug discovery due to the difficulty of stabilizing these targets for high-throughput screenings. Pannexin 1 (PANX1) is a membrane channel protein involved in various physiological and pathological processes, making it a promising target for drug discovery. However, efforts to develop PANX1-targeting therapeutics have been hindered by the inherent challenges of stabilizing the protein channel and conducting effective pharmacological screening. Here, we report a proof-of-concept workflow that integrates the Salipro lipid nanoparticle platform with DNA-Encoded Library screenings in a detergent-free format. In this case study, the Salipro DirectMX method was used to generate functional PANX1 nanoparticles for drug discovery and characterisation. Using a high-stringency selection strategy and computational approaches, we identified a specific set of candidate compounds with selective PANX1 enrichment. Surface Plasmon Resonance analysis confirmed the identification of hit compounds. Cryo-Electron Microscopy of the Salipro-PANX1-Compound complex provided structural insights into a potential compound binding site. Electrophysiological recordings in PANX1-expressing Xenopus laevis oocytes demonstrated dose-dependent inhibition of PANX1-mediated ion conductance by the compounds. These findings establish a robust workflow for ligand discovery against challenging membrane protein targets and provide novel chemical starting points for the development of PANX1 modulators.

Biocompatible fluorosurfactants are essential for many droplet microfluidic workflows but are often obtained from commercial sources because published syntheses of perfluoropolyether (PFPE)-based surfactants typically require acid chloride intermediates and chemistry-oriented purification methods. These requirements can limit access for biology and clinical laboratories seeking low-cost or customizable surfactant systems. Here we describe a practical method for preparing functional PFPE-based fluorosurfactant materials by direct carbodiimide coupling of functionalized PFPE carboxylic acids(Krytox 157 FSH) to amine-containing head groups under laboratory-accessible conditions. Using this approach, we prepared a PFPE-polyethylene-glycol (PFPE-PEG) material from Jeffamine ED900 and a PFPE-Tris material from Tris base. Because these products were not fully structurally characterized, we present them as functional reaction products and evaluate them by use in biomicrofluidic workflows rather than by definitive compositional assignment. PFPE-Tris was useful for generating relatively uniform small droplets, whereas the PFPE-PEG preparation supported a broader range of biological applications. These materials were used in genomic library screening for {beta}-glucosidase activity, thermocycling-associated droplet workflows, and protein crystallization experiments. In addition, the PFPE-PEG preparation improved emulsion behavior in many protein crystallization screens that were unstable with a commercial droplet oil used in our laboratory. This method reduces the practical barrier to in-house fluorosurfactant preparation and allows biology-focused laboratories to explore head-group chemistry, oil composition, and operating conditions without complete reliance on commercial reagents. The results support this workflow as a useful entry point for biomicrofluidics laboratories, while also highlighting the need for careful interpretation of thermocycled droplet assays and for future analytical characterization of the resulting materials. Significance statementDroplet microfluidics relies on fluorosurfactants that are often costly and difficult to synthesize outside of chemistry-focused settings. We describe a simple, biology-laboratory-compatible approach for generating functional perfluoropolyether-based fluorosurfactant materials using direct carbodiimide coupling and straightforward cleanup. The resulting materials supported multiple biomicrofluidic workflows in our laboratory, including enzymatic screening and protein crystallization, and provide a practical route for groups seeking lower-cost and more customizable surfactant systems.

The estimation of the biological sex of archeological remains is crucial information in bioarchaeology and forensic anthropology. In recent years, proteomics based on molecular sexual dimorphism have emerged as a preferred method, particularly because of its minimally-invasive approach to extracting amelogenin X and Y proteins from tooth enamel. However, there is an increasing demand to accelerate this process while facilitating the analysis of large archaeological assemblages. This study presents a novel high-throughput targeted paleoproteomics method for biological sex estimation using MALDI-CASI-FTICR mass spectrometry. This approach combines the strengths of existing methods, including ultra-high resolution, significantly reduced processing times, targeted analysis, and scalability to large archaeological sample sets. The method was initially validated on modern individuals with known sex and subsequently applied to 130 adult and juvenile individuals from medieval Great Moravia (present-day Czech Republic). Biological sex was successfully estimated for all but one of the individuals. The results not only provide a more efficient biological sex estimation but also help to resolve a few errors in sex assessment previously encountered with osteomorphological and tooth morphometric techniques. The implementation of this method significantly improves the accuracy and efficiency of biological sex estimation, offering a powerful tool for anthropological research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/706309v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1ede7e6org.highwire.dtl.DTLVardef@13d2f5org.highwire.dtl.DTLVardef@17ee44dorg.highwire.dtl.DTLVardef@1be9dd9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Chemically modified peptides, including cyclic peptides, have emerged as promising candidates for oral delivery yet they face the challenge of low membrane permeability. In this study, the datasets were sourced from CycPeptMPDB, a database for membrane permeability of peptides obtained from different assays. Our quantitative analysis showed a clear discordance between permeability measured using PAMPA and cell-based assays (Caco-2, MDCK, and, RRCK), thereby explaining its limits as surrogate for cell-based assays. Therefore, we developed assay-specific predictive models to more accurately capture permeability determinants in each system. We systematically compute diverse features of modified peptides using open-source software and used fine-tuned peptide embeddings generated using pretrained chemical language models. Baseline models were developed using the generated multi-hierarchical molecular features. We also developed a stacked ensemble architecture, which utilizes multi-hierarchical features in models as base learners. The ensemble model achieved the best PAMPA test set performance with an MSE of 0.200, R2 of 0.685, and PCC of 0.830; and a R2 of 0.783 on Caco-2 test set. Model trained on 2D Mordred descriptors attained the highest performance on the Caco-2 test-set with MSE of 0.129, R2 of 0.793, and PCC of 0.892, surpassing state-of-the-art approaches such as CPMP. To support widespread adoption, we developed an open-access web-server (https://webs.iiitd.edu.in/raghava/pcppred/) for users to design modified peptides using human comprehensible MAP (Modifications and Annotations of Proteins) format, converting MAP to SMILES format, and predict permeability across assays with result visualization. To ensure widespread adoption, and reproducibility, we also provided a standalone on GitHub (https://github.com/raghavagps/pcppred).

The cellular thermal shift assay (CETSA) is an invaluable tool for target identification and validation in early drug discovery efforts. It relies on thermal melting curves to indicate drug binding and is typically performed in whole cells, cell lysates, or purified protein as validation of direct interaction. However, these approaches can result in disruption of the structural integrity of membrane proteins, hindering downstream analysis and drug-target engagement. Here, we describe the first application of CETSA in isolated mitochondria and show the effects of this approach on the analysis of the compound UK5099 and its known binding target, the mitochondrial pyruvate carrier (MPC), a mitochondrial inner membrane-localized protein complex. Our analysis supports a model in which the MPC must remain structurally intact for UK5099 binding. We demonstrate that the binding of UK5099 to the MPC is disrupted in whole cells and cell lysates, whereas isolating mitochondria maintains the binding interaction between drug and target observable using CETSA. These data suggest that isolating membrane-bound organelles through subcellular CETSA stabilizes membrane-bound proteins in their native conformation, allowing the identification of membrane-localized drug binding targets that might otherwise be missed.

Drug promiscuity is a double-edged sword where a small molecule acts on multiple biological targets to induce toxicological or therapeutic benefits. It is possible to exploit promiscuity to expand treatment options without the prohibitive costs of designing a new drug. Imatinib is a representative case, exhibiting varied affinities and inhibitions to different kinases. It binds most favorably to Abl and Kit kinases, intermediately to Chk1 and Lck kinases, and least favorably to p38 and Src kinases. The strongly conserved features of the ATP-binding site render imatinibs molecular binding determinants unclear despite over 25 years of interrogation. To address this question, molecular thermodynamics, force distribution analysis, residue sidechain dihedral correlations, and principal component analysis were computed using trajectories from all-atom molecular dynamics simulations in explicit solvent. The results of these simulations agree with experimental affinity and binding data, enabling highly predictive factors for imatinibs binding specificity from free- and bound-state simulations through a global protein network of protein-ligand interactions, changes in sidechain dihedral correlations, and shifts in the secondary motifs modulating binding site access corresponding with well-characterized kinase "breathing motions." The sidechain dihedral correlation network also identifies distal mutants known to reduce patients imatinib sensitivity. Higher imatinib-kinase affinity trends with a loss in sidechain dihedral correlations and diminished secondary motif migration following binding, corresponding with more restricted configurations, to reduce solvent approach and ATP competition. Lower-affinity proteins show enhanced sidechain dihedral correlation and exaggerated secondary motif motions. This is consistent with a tendency to expose the protein pocket, facilitate solvent entrance, and increase ATP competition. Using imatinib as a model system, this study shows residue correlation, force interaction, and essential principal components can effectively forecast imatinib-kinase binding specificity and introduces an effective approach to repurpose and design high-affinity binders for off-target applications more generally.

Douglass Cooperativity and Ciullis Cooperativity in induced-proximity systems, remains controversial with paradoxes such as path-dependent metrics and apparent universal negative Cooperativity. We noticed that in "partial-embedded" model, a substantial portion of giant ligand remains exposed outside and does not engage with the host proteins force field. It incurs an entropic cost due to the restriction of translational/rotational degrees of freedom. This large, mass-dependent unfavorable ligand entropy penalty normally shifts binding affinity to 104[~]108-fold. ITC thermodynamic cycles analysis confirmed the dramatic entropy loss among reaction pair. This reconciles the conflicting Cooperativity definitions, yielding true path-independent positive PPI Cooperativity from observed entropy loss subtracting ligand entropy penalty. ITC data showed rigid linkers appear superior to flexible linkers with respect to both oral bioavailability and safety profile in PROTAC design. "ligand entropy barrier wall/Cooperativity ladder" pair is not only impact induced-proximity systems but also constitute the physical basis for all biosystems.

Lipid nanoparticles (LNPs) are the most successful drug delivery carrier to date, but optimizing lipid formulations to improve membrane fusion capabilities for effective drug release has been challenging due to lack of a quantitative measure for fusogenicity. Here we introduce a new framework based on small angle X-ray scattering to experimentally measure [Formula] for lipids used in LNP formulations such as glycerol monooleate (GMO) and ionizable lipids (SM-102 and ALC-0315). Q intrinsically captures spontaneous curvature (J0), which is traditionally used to assess fusogenicity. The change of cubic lattice parameters with temperature was measured for GMO-containing lipid mixtures, and the Q extracted quantitatively correlated with LNP fusogenicity power validated by fluorescence-based fusion assays and cryogenic electron microscopy. Fusogenicity of SM-102 and ALC-0315 was quantified by adding them to host membranes and assessing change in Q. This framework provides researchers with the ability to optimize the fusogenicity of LNP formulations for potent drug release and enhances understanding of parameters governing fusion in all biomembranes.

Understanding the effects of formulation excipients on protein solubility is a key part of physical chemistry and pharmaceutical sciences. While excipients are routinely employed to reduce the self-association of biologic drugs, their mechanisms of action remain poorly understood and are often assumed to be broadly nonspecific. Using a high-throughput combinatorial droplet microfluidic platform, we systematically survey and quantify how common pharmaceutical excipients affect the solubility of a diverse panel of therapeutic monoclonal antibodies (mAbs). We show that, while excipients are generally solubilizing, their effects vary substantially across different mAbs, with excipient-specific solubilization scores spanning dynamic ranges of approximately 7-fold to >200-fold across the antibody panel. Histidine, arginine and sodium chloride, in particular, engage in interactions characterized by unique molecular specificity, whereas sucrose effects are largely governed by nonspecific, solvent-mediated interactions. Correlating excipient performance with dynamical mAb molecular features from solvated full-length homology models allows us to dissect and quantify the relative contributions of molecular features governing excipient-mediated solubilization. We envision this new physicochemical understanding lays the groundwork for rational excipient selection and bespoke formulation design, with direct implications for accelerating protein therapeutic development for preclinical scenarios.

The structural and functional characteristics of membrane proteins can be influenced by the composition of the membrane. Consequently, native membranes are most relevant for the study of receptors and other membrane proteins. In this study, we investigated two types of cell-derived vesicles: natively shed extracellular vesicles (EVs) and mechanically derived vesicles (MVs). To this end, we utilized the human breast cancer cell line SKBR3, which strongly overexpresses the receptor HER2. We designed a protocol based on designed ankyrin repeat proteins (DARPins) to purify EVs and MVs enriched in HER2, and to ensure the native orientation of the HER2 receptors within the vesicle. The isolated HER2-containing EVs and MVs were characterized by cryo-EM, cryo-electron tomography (cryo-ET) and mass spectrometry (MS), which revealed fundamental differences between the different vesicle types. Our study highlights the greater structural diversity of EVs over MVs. A single particle cryo-EM analysis and classification of all visible receptors on the vesicle surface yielded electron density consistent with HER2 at modest resolution. Taken together, our results suggest that MVs can serve better than EVs as a suitable platform for the structure determination of membrane proteins within their native membrane environments.

Protein stability arises from a fine balance between stabilizing forces such as hydrophobic interactions, hydrogen bonding, and ionic interactions, and destabilizing contributions from solvent exposure and electrostatics. Although hydrophobic burial is the dominant driving force for folding, intra-chain hydrogen bonds and ionic interactions modulate stability in context-dependent ways, with effects that vary depending on their location and environment within the protein. Most studies of protein stability have focused on perturbations induced by pH, solvent composition, or mutations in protonated water, leaving the influence of solvent isotopes relatively underexplored. Notably, despite stronger hydrogen bonding in D2O, proteins exhibit diverse stability responses upon transfer from H2O to D2O, suggesting that differential hydration of nonpolar groups plays a key role. Here, the solvent isotope effect on protein stability is investigated using double-chain monellin (dcMN), a {beta}-sheet-rich, two-chain protein with well-characterized folding behavior. By combining conventional equilibrium unfolding measurements with hydrogen-deuterium exchange mass spectrometry (HDX-MS), the stability of wild-type and a less hydrophobic mutant (C42A) dcMN was compared in H2O and D2O, revealing greater stabilization of the wild-type protein in D2O and highlighting the importance of hydrophobic interactions in governing isotope-dependent stability.

Peptides, as therapeutic molecules, offer unique advantages in targeting complex protein surfaces, yet their rational design remains limited by the vastness of the sequence space and the constraints of traditional approaches. Here, we propose High-PepBinder, a sequence-only conditional diffusion framework for target-specific peptide generation. Guided by the target protein sequence, High-PepBinder adopts a dual encoder architecture that integrates protein language models (pLMs) with the diffusion model. This approach cascades the peptide generation model with an affinity classifier and enables the generation process to capture affinity-related features of the peptides through lightweight joint optimization. Due to the scarcity of protein-peptide affinity data, we constructed PepPBA, to our knowledge the most comprehensive dataset to date, and established a structure- and physics-based screening pipeline to prioritize top candidates. Results show that High-PepBinder demonstrates competitive performance across multiple peptide generation and affinity-related tasks. For representative targets, including KEAP1, XIAP, and EGFR, the generated peptides preserve key binding geometries and interface patterns of reference peptides in predicted complexes, while maintaining sequence diversity and favorable predicted properties. Overall, High-PepBinder contributes toward a general and sequence-only strategy for peptide design, offering a computational framework for expanding peptide discovery against challenging targets.

De novo protein design often produces thermostable proteins that denature above 100 {degrees}C, which complicates the analysis of their stability. Thermostable proteins can be unfolded by combined chemical and thermal denaturation followed by global analysis of multiple melting curves. Here, we have developed CheMelt, a new online tool for global analysis of unfolding data via an intuitive graphical user interface. We use nanoscale differential scanning fluorimetry followed by CheMelt data analysis to dissect the combined thermal and chemical denaturation of thirty-five de novo designed protein binders. Fifteen present sufficient fluorescence changes to extract thermodynamic parameters of unfolding. These de novo designed proteins have systematically lower {Delta}Cp and m-values than comparable natural proteins, which implies that they expose fewer hydrophobic residues upon unfolding. We show that a high thermostability of a designed protein does not necessarily imply a high equilibrium stability; and demonstrate the potential of CheMelt in dissecting thermodynamic properties for protein design and engineering.

The global rise of antimicrobial resistance has significantly reduced the effectiveness of conventional antibiotics, highlighting the urgent need for alternative and complementary therapeutic strategies. Nanotechnology-based drug delivery systems, particularly lipid nanoparticles, have emerged as promising tools to enhance antibiotic efficacy while limiting toxicity and resistance development. In this study, we evaluated the antimicrobial activity and drug carrier potential of Ohmline, a novel alkyl-ether glycolipid capable of self-assembling into nanotubes and lipid nanoparticles. First, a wide range of Gram-positive and Gram-negative bacteria were used to test Ohmline nanotubes antibacterial activity. All examined strains were partially inhibited, with a more noticeable effect on Gram-positive bacteria. Then, the synergistic potential of Ohmline combined with commercially available antibiotics (ampicillin, ceftriaxone, and ciprofloxacin) was evaluated using two different approaches: binary mixtures of Ohmline nanotubes and antibiotics and microfluidically produced Ohmline:DMPC (75:25) nanoparticles with the antibiotics encapsulated. Binary formulations demonstrated strong, strain-dependent synergistic effects at sub-MIC antibiotic concentrations, particularly against Enterococcus faecalis and Citrobacter braakii. Notably, antibiotic encapsulation within Ohmline nanoparticles further enhanced antimicrobial efficacy compared to non-encapsulated combinations, achieving near-complete growth inhibition in E. faecalis and significant inhibition in Klebsiella pneumoniae and C. braakii. Overall, our findings demonstrate that Ohmline possesses intrinsic antibacterial activity and acts as an effective lipid nanocarrier that potentiates antibiotic action. The dual functionality of Ohmline supports its potential as a versatile building block for next-generation antimicrobial formulations.

The importance of human aldehyde oxidase (hAOX1) has increased over the last decades due to its involvement in drug metabolism. Inhibition studies concerning hAOX1 are extensive and a common reducing agent, dithiothreitol (DTT), was recently found to inactivate the enzyme. However, in previous crystallographic studies of hAOX1, DTT was found to be essential for crystallization. To surpass this concern another reducing agent used in crystallization trials. Using tris(2-carboxyethyl)phosphine (TCEP), a sulphur-free reducing agent, it was possible to obtain well-ordered crystals from hAOX1 wild type and variant, hAOX1_6A, which diffracted beyond 2.3 [A]. Instead of the typical star-shaped crystals of hAOX1, at pH 4.7, plates are obtained in the orthorhombic space group (P22121) with two molecules in the asymmetric unit. Activity assays with the enzyme incubated with both reducing agents show that contrary to DTT, TCEP does not lead to irreversible inactivation of the enzyme. The replacement of DTT with TCEP in crystallization of hAOX1 provides a strategy to circumvent enzyme inactivation during crystallographic studies, allowing future applications of new assays, such as time-resolved crystallography.

Cell-free expression systems (CFES) are increasingly used alongside conventional biotechnological approaches to accelerate early-stage prototyping and are particularly valuable in point-of-use settings. However, their broader adoption remains limited by time- and cost-intensive preparation, as well as stringent cryogenic storage requirements. To address this, several studies have explored lyophilization with protective additives to generate stable, solid-state CFES. These approaches had to balance the protection gained with a loss of activity due to the additives. In this study, we present a CFES that contains a tardigrade-derived Cytosolic-Abundant Heat-Soluble (CAHS) protein to protect the biosynthetic machinery in lysates from damages during drying. We show that the CAHS protein, without any other additives, preserves protein synthesis activity during low-cost room temperature desiccation, while unprotected lysates are affected in mRNA synthesis kinetics and translation yields. The diversity of tardigrade-derived protective proteins is a treasure trove for cell-free synthetic biology, in particular for making CFES more accessible and portable. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/715078v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@8ecc2eorg.highwire.dtl.DTLVardef@ff0432org.highwire.dtl.DTLVardef@6c940eorg.highwire.dtl.DTLVardef@6c5390_HPS_FORMAT_FIGEXP M_FIG C_FIG

Liquid-liquid phase separation (LLPS) of biomacromolecules is a key mechanism driving the formation of membraneless organelles (MLOs) within cells, playing a crucial role in fundamental biological processes such as cell proliferation and stress response. Accurately understanding and predicting the phase separation propensity of proteins is essential for unraveling the assembly mechanisms of MLOs and their functions under both physiological and pathological conditions. Traditional research methods primarily rely on biochemical experiments, which are limited by low throughput, high cost, and difficulty in systematically exploring sequence-phase transition relationships. This study proposes and implements a novel three-stage, iterative paradigm based on artificial intelligence (AI) to propel phase separation research towards systematization, predictability, and mechanistic understanding. O_LIBenchmark Model Construction: A preliminary predictive model was established based on a Multilayer Perceptron (MLP) neural network, and the driving effect of phenylalanine/tyrosine (F/Y) residue-mediated {pi}-{pi} interactions on LLPS was validated. C_LIO_LIModel Robustness Enhancement: The model was optimized through adversarial training strategies, which effectively identified and eliminated misclassifications of "highly disordered non-phase-separating" trap sequences. This significantly improved the models generalization capability and reliability when handling complex, real-world sequences. C_LIO_LIPhysical Mechanism Integration and Functional Expansion: Incorporating the Uniform Manifold Approximation and Projection (UMAP) manifold learning method and constraints from non-equilibrium thermodynamics, a "fingerprint space" capable of characterizing the thermodynamic behavior of phase separation was constructed. This space enables cluster analysis of different MLO types, and the model can output a thermodynamic stability score for protein phase separation. Based on this score, we identified 10 high-confidence candidate proteins with the potential to form novel MLOs. The paradigm established in this study upgrades phase separation prediction from the traditional "binary classification" approach to a novel research framework characterized by "physical mechanism analysis + novel MLO discovery." It provides the phase separation field with a computational tool that combines high accuracy, strong robustness, and good physical interpretability. C_LI

The growing prevalence of obesity necessitates innovative treatments. This study investigates a spray-dried konjac glucomannan-montmorillonite (KGM-MMT) hybrid designed to combine the fermentable, satiety-promoting effects of KGM with the lipid-binding and anti-inflammatory properties of MMT. In HFD-fed mice treated for 42 days with 2% w/w KGM-MMT, body weight gain was reduced by 7.6%, with an AUC of 5094[{+/-}[52.95, compared to 5513[{+/-}[81.35 in HFD controls (p < 0.0001). Serum IL-6 concentrations were reduced by 97% (p = 0.0002), while blood glucose decreased by 46% (p < 0.0001), outperforming reductions seen with MMT (24%, p = 0.0271) and KGM (16%, ns). Gut microbiota profiling demonstrated a significant 6.2-log[ fold increase in Lactobacillaceae (p = 0.023) and a 2.4-log[ fold increase in Enterococcaceae (p = 0.015) with KGM-MMT treatment. Predicted functional shifts revealed a 1.9-fold increase in short-chain fatty acid synthesis pathways and a 5.4-fold increase in bile acid deconjugation. Although the KGM-MMT hybrid did not consistently outperform its individual components in all measurements within the current study, it generally consolidated their metabolic benefits within a single dosage form. These findings support the utility of spray-dried KGM-MMT as a gut-targeted dietary strategy with additive effects on metabolic health. Future studies should explore underlying mechanisms and dosage effects of the hybrid formulation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/701163v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@738445org.highwire.dtl.DTLVardef@1f0d465org.highwire.dtl.DTLVardef@86e5aorg.highwire.dtl.DTLVardef@184fba8_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISpray-dried KGM-MMT reduced HFD-induced weight gain by 7.6% in obese mice C_LIO_LISerum IL-6 and glucose levels decreased by 97% and 46%, respectively C_LIO_LI6.2-log[J and 2.4-log[J increases in Lactobacillaceae & Enterococcaceae relative abundance C_LIO_LIBile acid deconjugation and SCFA pathways increased 5.4- and 1.9-fold C_LIO_LIKGM-MMT microparticles offer additive gut-targeted benefits in metabolic disease C_LI

Sirtuins (SIRTs), which remove protein lysine acyl modifications, play crucial roles in diverse cellular processes, including metabolism, gene transcription, DNA damage repair, cell survival, and stress response. Several sirtuins are considered non-oncogene addiction of cancer cells and promising targets for anticancer drug development. High-throughput screening (HTS) methods for sirtuins are critical for the development of potent and isoform-selective sirtuin inhibitors, which are needed to validate the therapeutic potential. Herein, we designed and synthesized a fluorescent polarization (FP) tracer, KP-SC-1. Using this high-affinity tracer, we developed a robust, high-throughput FP competition assay for screening SIRT1-3 inhibitors. The assay was validated by testing known SIRT1-3 inhibitors. The assay can detect NAD+-independent SIRT1-3 inhibitors, as well as NAD+-dependent inhibitors, such as Ex-527 and TM. Finally, our assay showed satisfactory stability and outstanding performance in a pilot library screening. Compared to previous assays, the FP assay uses much less SIRT1-3 enzymes, a feature important for high-throughput library screening. We believe that the FP assay developed here will accelerate the discovery and development of SIRT1-3 inhibitors.

Antibiotic resistance continues to rise, yet most new drug candidates act through long-established targets. Faster mode of action (MoA) assessment would enable more effective prioritization of screening hits and help identify compounds with novel mechanisms. In this study, we aimed to develop a scalable framework for MoA inference from antibiotic-induced cellular response profiles in Escherichia coli. We generated a multimodal dataset spanning more than 50 antibiotics, including proteome profiles, chemical structure descriptors, inhibitory concentrations and growth dynamics, and used it to build MAPPER (Mode of Action Prediction via Proteomics-Enhanced Representation), a framework comprising a fixed multimodal predictor and an uncertainty module. MAPPER accurately classified antibiotics across nine mechanistic classes, flagged compounds with likely novel mechanisms and retained predictive power in proteomics-only transfer experiments across mass spectrometry platforms and external data. Together, these results establish MAPPER as an innovative tool for MoA prediction and novelty detection, enabling prioritization of antibacterial candidates with distinct mechanisms.