Hit me with your best shot: Integrated hit discovery for the next generation of drug targets.

Identification of high-quality hit chemical matter is of vital importance to the success of drug discovery campaigns. However, this goal is becoming ever harder to achieve as the targets entering the portfolios of pharmaceutical and biotechnology companies are increasingly trending towards novel and traditionally challenging to drug. This demand has fuelled the development and adoption of numerous new screening approaches, whereby the contemporary hit identification toolbox comprises a growing number of orthogonal and complementary technologies including high-throughput screening, fragment-based ligand design, affinity screening (affinity-selection mass spectrometry, differential scanning fluorimetry, DNA-encoded library screening), as well as increasingly sophisticated computational predictive approaches. Herein we describe how an integrated strategy for hit discovery, whereby multiple hit identification techniques are tactically applied, selected in the context of target suitability and resource priority, represents an optimal and often essential approach to maximise the likelihood of identifying quality starting points from which to develop the next generation of medicines.

Unveiling a Hidden Pocket in HIV-1 Protease: New Insights Into Retroviral Protease Cantilever-Tip Region Characteristics.

The HIV-1 protease is critical for the process of viral maturation and as such, it is one of the most well characterized proteins in the Protein Data Bank. There is some evidence to suggest that the HIV-1 protease is capable of accommodating small molecule fragments at several locations on its surface outside of the active site. However, some pockets on the surface of proteins remain unformed in the apo structure and are termed "cryptic sites." To date, no cryptic sites have been identified in the structure of HIV-1 protease. Here, we characterize a novel cryptic cantilever pocket on the surface of the HIV-1 protease through mixed-solvent molecular dynamics simulations using several probes. Interestingly, we noted that several homologous retroviral proteases exhibit evolutionarily conserved dynamics in the cantilever region and possess a conserved pocket in the cantilever region. Immobilization of the cantilever region of the HIV-1 protease via disulfide cross-linking resulted in curling-in of the flap tips and the propensity for the protease to adopt a semi-open flap conformation. Structure-based analysis and fragment-based screening of the cryptic cantilever pocket suggested that the pocket may be capable of accommodating ligand structures. Furthermore, molecular dynamics simulations of a top scoring fragment bound to the cryptic pocket illustrated altered flap dynamics of the fragment-bound enzyme. Together, these results suggest that the mobility of the cantilever region plays a key role in the global dynamics of retroviral proteases. Therefore, the cryptic cantilever pocket of the HIV-1 protease may represent an interesting target for future in vitro studies.

BuildAMol: a versatile Python toolkit for fragment-based molecular design.

In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques.Scientific contributionBuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation.

Repurposing Anidulafungin for Alzheimer's Disease via Fragment-Based Drug Discovery.

The misfolding and aggregation of beta-amyloid (Aß) peptides have been implicated as key pathogenic events in the early stages of Alzheimer's disease (AD). Inhibiting Aß aggregation represents a potential disease-modifying therapeutic approach to AD treatment. Previous studies have identified various molecules that inhibit Aß aggregation, some of which share common chemical substructures (fragments) that may be key to their inhibitory activity. Employing fragment-based drug discovery (FBDD) methods may facilitate the identification of these fragments, which can subsequently be used to screen new inhibitors and provide leads for further drug development. In this study, we used an in silico FBDD approach to identify 17 fragment clusters that are significantly enriched among Aß aggregation inhibitors. These fragments were then used to screen anti-infective agents, a promising drug class for repurposing against amyloid aggregation. This screening process identified 16 anti-infective drugs, 5 of which were chosen for further investigation. Among the 5 candidates, anidulafungin, an antifungal compound, showed high efficacy in inhibiting Aß aggregation in vitro. Kinetic analysis revealed that anidulafungin selectively blocks the primary nucleation step of Aß aggregation, substantially delaying Aß fibril formation. Cell viability assays demonstrated that anidulafungin can reduce the toxicity of oligomeric Aß on BV2 microglia cells. Molecular docking simulations predicted that anidulafungin interacted with various Aß species, including monomers, oligomers, and fibrils, potentially explaining its activity against Aß aggregation and toxicity. This study suggests that anidulafungin is a potential drug to be repurposed for AD, and FBDD is a promising approach for discovering drugs to combat Aß aggregation.

Investigating the Catalytic Site of Human 15-lipoxygenase-1 via Marine Natural Products.

Human 15-lipoxygenase-1 (15-LOX-1) is a key enzyme that possesses an important role in (neuro)inflammatory diseases. The pocket of the enzyme plays the role of a chiral catalyst, and therefore chirality could be an important component for the design of effective enzyme inhibitors. To advance our knowledge on this concept, we developed a library of the identified chiral 15-LOX-1 inhibitors and applied cheminformatic tools. Our analysis highlighted specific structural elements, which we integrated them in small molecules, and employed them as "smart" tools to effectively navigate the chemical space of previously unexplored regions. To this purpose, we utilized the marine derived natural product phosphoeleganin (PE) among with a small library of synthetic fragment derivatives, including a certain degree of stereochemical diversity. Enzyme inhibition/kinetic and molecular modelling studies has been performed in order to characterize structurally novel PE-based inhibitors, which proved to present a different type of inhibition with low micromolar potency, according to their structural features. We demonstrate that different warheads work as anchor, and either guide specific stereochemistry, or causing a time-depended inhibition. Finally, we prove that the positioning of the chiral substituents or/and the favorable stereochemistry can be crucial, as it can lead from active to completely inactive compounds.

Dataset for discovering new hypertension small molecules using machine learning-aided computational fragment-based design.

This dataset demonstrates the use of computational fragmentation-based and machine learning-aided drug discovery to generate new lead molecules for the treatment of hypertension. Specifically, the focus is on agents targeting the renin-angiotensin-aldosterone system (RAAS), commonly classified as Angiotensin-Converting Enzyme Inhibitors (ACEIs) and Angiotensin II Receptor Blockers (ARBs). The preliminary dataset was a target-specific, user-generated fragment library of 63 molecular fragments of the 26 approved ACEI and ARB molecules obtained from the ChEMBL and DrugBank molecular databases. This fragment library provided the primary input dataset to generate the new lead molecules presented in the dataset. The newly generated molecules were screened to check whether they met the criteria for oral drugs and comprised the ACEI or ARB core functional group criterion. Using unsupervised machine learning, the molecules that met the criterion were divided into clusters of drug classes based on their functional group allocation. This process led to three final output datasets, one containing the new ACEI molecules, another for the new ARB molecules, and the last for the new unassigned class molecules. This data can aid in the timely and efficient design of novel antihypertensive drugs. It can also be used in precision hypertension medicine for patients with treatment resistance, non-response or co-morbidities. Although this dataset is specific to antihypertensive agents, the model can be reused with minimal changes to produce new lead molecules for other health conditions.

Evaluation of a Covalent Library of Diverse Warheads (CovLib) Binding to JNK3, USP7, or p53.

Purpose: Over the last few years, covalent fragment-based drug discovery has gained significant importance. Thus, striving for more warhead diversity, we conceived a library consisting of 20 covalently reacting compounds. Our covalent fragment library (CovLib) contains four different warhead classes, including five α-cyanoacacrylamides/acrylates (CA), three epoxides (EO), four vinyl sulfones (VS), and eight electron-deficient heteroarenes with a leaving group (SNAr/SN). Methods: After predicting the theoretical solubility of the fragments by LogP and LogS during the selection process, we determined their experimental solubility using a turbidimetric solubility assay. The reactivities of the different compounds were measured in a high-throughput 5,5'-dithiobis-(2-nitrobenzoic acid) DTNB assay, followed by a (glutathione) GSH stability assay. We employed the CovLib in a (differential scanning fluorimetry) DSF-based screening against different targets: c-Jun N-terminal kinase 3 (JNK3), ubiquitin-specific protease 7 (USP7), and the tumor suppressor p53. Finally, the covalent binding was confirmed by intact protein mass spectrometry (MS). Results: In general, the purchased fragments turned out to be sufficiently soluble. Additionally, they covered a broad spectrum of reactivity. All investigated α-cyanoacrylamides/acrylates and all structurally confirmed epoxides turned out to be less reactive compounds, possibly due to steric hindrance and reversibility (for α-cyanoacrylamides/acrylates). The SNAr and vinyl sulfone fragments are either highly reactive or stable. DSF measurements with the different targets JNK3, USP7, and p53 identified reactive fragment hits causing a shift in the melting temperatures of the proteins. MS confirmed the covalent binding mode of all these fragments to USP7 and p53, while additionally identifying the SNAr-type electrophile SN002 as a mildly reactive covalent hit for p53. Conclusion: The screening and target evaluation of the CovLib revealed first interesting hits. The highly cysteine-reactive fragments VS004, SN001, SN006, and SN007 covalently modify several target proteins and showed distinct shifts in the melting temperatures up to +5.1 °C and -9.1 °C.

Fragment-based discovery of small molecule inhibitors of the HDGFRP2 PWWP domain.

The PWWP domain of hepatoma-derived growth factor-related protein 2 (HDGFRP2) recognizes methylated histones to initiate the recruitment of homologous recombination repair proteins to damaged silent genes. The combined depletion of HDGFRP2 and its paralog PSIP1 effectively impedes the onset and progression of diffuse intrinsic pontine glioma (DIPG). Here, we discovered varenicline and 4-(4-bromo-1H-pyrazol-3-yl) pyridine (BPP) as inhibitors of the HDGFRP2 PWWP domain through a fragment-based screening method. The complex crystal structures reveal that both Varenicline and BPP engage with the aromatic cage of the HDGFRP2 PWWP domain, albeit via unique binding mechanisms. Notably, BPP represents the first single-digit micromolar inhibitor of the HDGFRP2 PWWP domain with a high ligand efficiency. As a dual inhibitor targeting both HDGFRP2 and PSIP1 PWWP domains, BPP offers an exceptional foundation for further optimization into a chemical tool to dissect the synergetic function of HDGFRP2 and PSIP1 in DIPG pathogenesis.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies.

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

A Fragment-Based Competitive 19F†LB-NMR Platform For Hotspot-Directed Ligand Profiling.

Ligand binding hotspots are regions of protein surfaces that form particularly favourable interactions with small molecule pharmacophores. Targeting interactions with these hotspots maximises the efficiency of ligand binding. Existing methods are capable of identifying hotspots but often lack assays to quantify ligand binding and direct elaboration at these sites. Herein, we describe a fragment-based competitive 19F Ligand Based NMR (LB-NMR) screening platform that enables routine, quantitative ligand profiling focused at ligand-binding hotspots. As a proof of concept, the method was applied to 4'-phosphopantetheine adenylyltransferase (PPAT) from Mycobacterium abscessus (Mabs). X-ray crystallographic characterisation of the hits from a 960-member fragment screen identified three ligand-binding hotspots across the PPAT active site. From the fragment hits a collection of 19F reporter candidates were designed and synthesised. By rigorous prioritisation and use of optimisation workflows, a single 19F reporter molecule was generated for each hotspot. Profiling the binding of a set of structurally characterised ligands by competitive 19F LB-NMR with this suite of 19F reporters recapitulated the binding affinity and site ID assignments made by ITC and X-ray crystallography. This quantitative mapping of ligand binding events at hotspot level resolution establishes the utility of the fragment-based competitive 19F LB-NMR screening platform for hotspot-directed ligand profiling.

Accessing Active Fragments for Drug Discovery Utilising Nitroreductase Biocatalysis.

Biocatalysis has played a limited role in the early stages of drug discovery. This is often attributed to the limited substrate scope of enzymes not affording access to vast areas of novel chemical space. Here, we have shown a promiscuous nitroreductase enzyme (NR-55) can be used to produce a panel of functionalised anilines from a diverse panel of aryl nitro starting materials. After screening on analytical scale, we show that sixteen substrates could be scaled to 1 mmol scale, with several poly-functional anilines afforded with ease under the standard conditions. The aniline products were also screened for activity against several cell lines of interest, with modest activity observed for one compound. This study demonstrates the potential for nitroreductase biocatalysis to provide access to functional fragments under benign conditions.

Fragment Screening to Identify Inhibitors Targeting Ribosome Binding of Shiga Toxin 2.

Shiga toxins are the main virulence factors of Shiga toxin producing E. coli (STEC) and S. dysenteriae. There is no effective therapy to counter the disease caused by these toxins. The A1 subunits of Shiga toxins bind the C-termini of ribosomal P-stalk proteins to depurinate the sarcin/ricin loop. The ribosome binding site of Shiga toxin 2 has not been targeted by small molecules. We screened a fragment library against the A1 subunit of Shiga toxin 2 (Stx2A1) and identified a fragment, BTB13086, which bound at the ribosome binding site and mimicked the binding mode of the P-stalk proteins. We synthesized analogs of BTB13086 and identified a series of molecules with similar affinity and inhibitory activity. These are the first compounds that bind at the ribosome binding site of Stx2A1 and inhibit activity. These compounds hold great promise for further inhibitor development against STEC infection.

Optimizing NMR fragment-based drug screening for membrane protein targets.

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

High-confidence placement of low-occupancy fragments into electron density using the anomalous signal of sulfur and halogen atoms.

Fragment-based drug design using X-ray crystallography is a powerful technique to enable the development of new lead compounds, or probe molecules, against biological targets. This study addresses the need to determine fragment binding orientations for low-occupancy fragments with incomplete electron density, an essential step before further development of the molecule. Halogen atoms play multiple roles in drug discovery due to their unique combination of electronegativity, steric effects and hydrophobic properties. Fragments incorporating halogen atoms serve as promising starting points in hit-to-lead development as they often establish halogen bonds with target proteins, potentially enhancing binding affinity and selectivity, as well as counteracting drug resistance. Here, the aim was to unambiguously identify the binding orientations of fragment hits for SARS-CoV-2 nonstructural protein 1 (nsp1) which contain a combination of sulfur and/or chlorine, bromine and iodine substituents. The binding orientations of carefully selected nsp1 analogue hits were focused on by employing their anomalous scattering combined with Pan-Dataset Density Analysis (PanDDA). Anomalous difference Fourier maps derived from the diffraction data collected at both standard and long-wavelength X-rays were compared. The discrepancies observed in the maps of iodine-containing fragments collected at different energies were attributed to site-specific radiation-damage stemming from the strong X-ray absorption of I atoms, which is likely to cause cleavage of the C-I bond. A reliable and effective data-collection strategy to unambiguously determine the binding orientations of low-occupancy fragments containing sulfur and/or halogen atoms while mitigating radiation damage is presented.

Fragment-based virtual screening identifies novel leads against Plasmepsin IX (PlmIX) of Plasmodium falciparum: Homology modeling, molecular docking, and simulation approaches.

Despite continuous efforts to develop safer and efficient medications, malaria remains a major threat posing great challenges for new drug discovery. The emerging drug resistance, increased toxicities, and impoverished pharmacokinetic profiles exhibited by conventional drugs have hindered the search for new entities. Plasmepsins, a group of Plasmodium-specific, aspartic acid protease enzymes, are involved in many key aspects of parasite biology, and this makes them interesting targets for antimalarial chemotherapy. Among different isoforms, PlmIX serves as an unexplored antimalarial drug target that plays a crucial role along with PlmV and X in the parasite's survival by digesting hemoglobin in the host's erythrocytes. In this study, fragment-based virtual screening was performed by modeling the three-dimensional structure of PlmIX and predicting its ligand-binding pocket by using the Sitemap tool. Screening identified the fragments with the XP docking score ≤ -3 kcal/mol from the OTAVA General Fragment Library (≈16,397 fragments), and the selected fragments were chosen for ligand breeding. The resulting ligands (≈69,858 ligands) were subsequently subjected to filtering based on the QikProp properties along with carcinogenicity testing performed using CarcinoPred-EL and then docked in the SP (≈14,078 ligands) as well as XP mode (≈3,104 ligands), and compared with that of control ligands 49C and I0L. The top-ranked ligands were taken further for the calculation of the free energy of binding using Prime MM-GBSA. Overall, a total of six complexes were taken further for MD simulation studies performed at 100 ns to attain a better understanding of the binding mechanisms, and compounds 3 and 4 were found to be the most efficient ones in silico. The analysis of compound 3 revealed that the carbonyl group present in position 1 on the isoindoline moiety (Arg554) was responsible for inhibitory activity against PlmIX. However, the analysis of compound 4 revealed that the amide linkage sandwiched between the phenyl ring and isoquinoline moiety (Lys555 and Ser226) as well as carbonyl oxygen of the carbamoyl group present at position 2 of the pyrazole ring (Gln222) were responsible for PlmIX inhibitory activity, owing to their crucial interactions with key amino acid residues.

Isothermal Titration Calorimetry for Fragment-Based Analysis of Ion Channel Interactions.

Ion channels are membrane proteins that may also have intracellular and extracellular domains that interact with other ligands. In many cases, these interaction sites are highly mobile and may undergo changes in the configuration upon binding with regulatory signaling molecules. Isothermal titration calorimetry (ITC) is a powerful technique to quantify protein-ligand interactions of purified samples in solution. This chapter describes a fragment-based analysis method using ITC to quantify the interactions between a domain of the voltage-gated Kv7 channel and the calcium-regulated protein calmodulin. This example can be used to quantify the interactions between specific domains of other ion channels and their regulatory signaling proteins.

14-3-3 Protein-Protein Interactions: From Mechanistic Understanding to Their Small-Molecule Stabilization.

Protein-protein interactions (PPIs) are of utmost importance for maintenance of cellular homeostasis. Herein, a central role can be found for 14-3-3 proteins. These hub-proteins are known to bind hundreds of interaction partners, thereby regulating their activity, localization, and/or stabilization. Due to their ability to bind a large variety of client proteins, studies of 14-3-3 protein complexes flourished over the last decades, aiming to gain greater molecular understanding of these complexes and their role in health and disease. Because of their crucial role within the cell, 14-3-3 protein complexes are recognized as highly interesting therapeutic targets, encouraging the discovery of small molecule modulators of these PPIs. We discuss various examples of 14-3-3-mediated regulation of its binding partners on a mechanistic level, highlighting the versatile and multi-functional role of 14-3-3 within the cell. Furthermore, an overview is given on the development of stabilizers of 14-3-3 protein complexes, from initially used natural products to fragment-based approaches. These studies show the potential of 14-3-3 PPI stabilizers as novel agents in drug discovery and as tool compounds to gain greater molecular understanding of the role of 14-3-3-based protein regulation.

Peptidic Compound as DNA Binding Agent: In Silico Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G-quadruplex (G4), a secondary structure in guanine-rich regions. G4 plays a crucial role in cellular processes by regulating gene expression and telomerase function. Researchers have recently identified G4-stabilizing binding agents as promising anti-cancer compounds. Additionally, peptides have emerged as effective anticancer pharmaceuticals due to their ability to form multiple hydrogen bonds, electrostatic interactions, and van der Waals forces. These properties enable peptides to bind to specific areas of DNA chains selectively. However, despite these advancements, designing G4-binding peptides remains challenging due to a lack of comprehensive information. OBJECTIVE: In our present study, we employed an in silico fragment-based approach to design G4- binding peptides. This innovative method combines machine learning classification, molecular docking, and dynamics simulation. METHODS: AutoDock Vina and Gromacs performed molecular docking and MD simulation, respectively. The machine learning algorithm was implemented by Scikit-learn. Peptide synthesis was performed using the SPPS method. The DNA binding affinity was measured by applying spectrophotometric titration. RESULTS: As a result of this approach, we identified a high-scoring peptide (p10; sequence: YWRWR). The association constant (Ka) between p10 and the ctDNA double helix chain was 4.45 × 105 M-1. Molecular modeling studies revealed that p10 could form a stable complex with the G4 surface. CONCLUSION: The obtained Ka value of 4.45 × 105 M-1 indicates favorable interactions. Our findings highlight the role of machine learning and molecular modeling approaches in designing new G4-binding peptides. Further research in this field could lead to targeted treatments that exploit the unique properties of G4 structures.

Ligandability at the membrane interface of GPx4 revealed through a reverse micelle fragment screening platform.

While they account for a large portion of drug targets, membrane proteins (MPs) present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of proteins that bind reversibly to membranes, are also difficult targets, particularly those that function only while bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, interfaces are underexplored spaces in inhibitor design and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability in the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase which is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of non-covalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 in bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from > 200 µM to ~15 µM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing non-covalent inhibitors of GPx4.

Nontarget Discovery of Per- and Polyfluoroalkyl Sulfonyl Halides in Soils by Integration of Derivatization and Specific Fragment-Based Liquid Chromatography-High Resolution Mass Spectrometry Screening.

Though long recognized as synthetic precursors to other poly- and perfluoroalkyl substances (PFASs), most poly- and perfluoroalkyl sulfonyl halides (PASXs) cannot be directly measured and have generally received minimal attention. Inspired by the redox reaction between sulfonyl halide groups and p-toluenethiol in organic chemistry, we developed a novel nontarget analysis strategy for PASXs by intergrating derivatization and specific fragment-based liquid chromatography-high resolution mass spectrometry screening for m/z 82.961 [SO2F-] and m/z 95.934 [S2O2-]. By using this strategy, we discovered 11 PASXs, namely, perfluoroalkyl sulfonyl fluorides (5), polyfluoroalkyl sulfonyl fluorides (2), unsaturated perfluoroalkyl sulfonyl fluoride (1), and perfluoroalkyl sulfonyl chlorides (3) in soil samples collected from an abandoned fluorochemical manufacturing park. These average ∑PASXs concentrations were 1120 µg kg-1 (range: 9.7-9860 µg kg-1), which were very likely to be the key intermediates and undesired byproducts of electrochemical fluorination processes. Spatial variation in the mass ratio of ∑PASXs to ∑PFSAs (range: 0.7-795%) also indicates their different transportation pathways. More importantly, the decline of PASXs and increase of perfluoroalkyl sulfonates (when compared to a prior study at this site) suggest the continued hydrolysis of PASXs and the relatively fast environmental transformation rates in the abandoned fluorochemical park soils. Overall, these findings demonstrated the utility of a novel nontarget analysis strategy, which may change most PASXs from inferred precursors to measured intermediates and further could be adapted for structures, distribution, and transformation studies of PFASXs in other matrices.