Search Results (86)

Precise site-specific ligand design remains a major challenge in structure-based drug discovery. Most existing approaches screen ligands against binding pockets identified from static protein structures obtained by X-ray crystallography, NMR spectroscopy, cryo-electron microscopy, or AlphaFold predictions. However, protein function is governed by a structure–dynamics–function relationship, and ligand screening that does not account for binding competition across the protein surface or the receptor’s dynamic, substrate-dependent conformational states remains incomplete. Substrate-specific conformations are underexplored and may offer new opportunities for selective ligand design, although systematic workflows to identify and exploit such states remain limited. Previously, we showed that collagen alters matrix metalloprotease-1 (MMP1) dynamics and that R405 is a collagen-specific allosteric residue exhibiting strong dynamic correlations with the catalytic site. Here, we present a computational framework for substrate-specific allosteric ligand design using collagen-bound MMP1 as a model system. We characterized the conformational dynamics of free and collagen-bound MMP1 by all-atom molecular dynamics simulations, clustered the resulting conformational ensembles, and identified conformations unique to the collagen-bound state. These conformations were used as structural templates for machine-learning-based generation of approximately 150,000 candidate ligands, which were subsequently docked against both the R405-centered region and all detectable binding pockets on the MMP1 surface. Several candidate ligands were predicted to dock preferentially at the R405 region by at least 0.3 kcal/mol compared with competing surface pockets. Together, these results establish a generalizable computational workflow for identifying candidate ligands predicted to preferentially dock to substrate-specific allosteric conformations and provide a foundation for future experimental validation of selective allosteric modulation. Full article

Selective inhibition of MEK isoforms remains a central challenge in MAPK-targeted drug discovery, largely due to the structural similarity between MEK1 and MEK2. While MEK1 has been extensively characterized, the structural basis of MEK2-specific ligand recognition is not fully understood. Here, we present crystal structures of human MEK2 in complex with the noncompetitive inhibitor U0126 and the allosteric inhibitor refametinib at resolutions of 3.15 Å and 3.30 Å, respectively. Despite a conserved kinase fold, MEK2 exhibits isoform-specific features within the N-lobe β-sheet. Additional differences are observed in the relative orientation of the helix C and activation segment, and the helix F-supported regulatory spine. Structural differences are reflected in micromolar binding affinities for U0126 (K_d = 9.8 μM) and refametinib (K_d = 7.4 μM). Notably, a single N-lobe substitution (Thr87 in MEK2 versus Phe83 in MEK1) selectively enhanced U0126 binding. The MEK2 T87F mutant exhibited an approximately twofold increase in affinity, while refametinib binding remained largely unchanged. SEC–MALS analysis demonstrated that MEK2 predominantly exists as a monomer in solution, contrasting with the reported homodimeric behavior of MEK1. Molecular dynamics simulations supported these findings by revealing isoform-specific differences in oligomeric state-dependent flexibility and inhibitor-induced dynamics. Collectively, our findings define the structural basis underlying the differential inhibitor recognition of MEK2 and MEK1, providing mechanistic insight into isoform-selective MEK-targeted drug design. Full article

Background/Objectives: First-generation CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) target the conserved ATP-binding pocket of CDK4 and, despite clinical success, are limited by acquired resistance and insufficient exploration of alternative regulatory sites. This study aimed to identify a putative allosteric small-molecule candidate at the CDK4 αE-helix–Cyclin D1 α1-helix protein–protein interaction (PPI) interface within the CDK4/Cyclin D1/p21 ternary complex using RapidFunnel-AI, a decision-interpretable virtual-screening pipeline. Methods: Starting from 50,000 ChEMBL 33 molecules, the pipeline sequentially applied a Q-Fold/RapidFunnel topological Tanimoto scan based on clinical CDK4/6 inhibitor motifs, fragment-level electronic-property enrichment, ADMET/PAINS filtering, dry Vina-GPU docking, hydration-mediated AutoDock-GPU (Version 1.6) docking, explicit-solvent molecular dynamics, contact-retention analysis, and MM-GBSA energy decomposition. The Q-Fold Thermo-Core surrogate model provided fragment-level enrichment, predicting the HOMO–LUMO gap (R² = 0.93) and isotropic polarizability (R² = 0.98) on QM9. Candidate selection did not rely on the lowest docking or MM-GBSA score alone, but on pose persistence, contact continuity, and energy-component consistency. Results: The workflow reduced the initial library to 43 topologically prioritized candidates, 25 ADMET/PAINS-filtered ligands, and 9 docking-derived complexes for MD validation. Ligand_020 emerged as the only candidate that preserved a persistent binding mode at Site 2 during a 500 ns simulation—an interface engagement reproduced across three independent 500 ns replicates with no full dissociation in any replicate—with a protein Cα RMSD of 2.88 ± 0.32 Å, a ligand heavy-atom RMSD of 3.56 ± 0.28 Å, and a van der Waals-dominated MM-GBSA profile (ΔGbind = −28.23 ± 3.57 kcal/mol). In contrast, palbociclib and ribociclib, forcibly placed at Site 2 as negative controls, lost most initial contacts within 5 ns and tended to detach despite more favorable MM-GBSA values. Conclusions: These results suggest that single-score docking or MM-GBSA ranking can generate false positives at shallow PPI interfaces. By integrating AI-assisted prioritization, multipocket docking, explicit-solvent MD, contact-retention analysis, and energy-component consistency, RapidFunnel-AI nominated Ligand_020 as an experimentally testable putative allosteric hit targeting the CDK4/Cyclin D1 interface, offering a reusable platform for PPI-focused oncological drug discovery. Full article

Ribosome-inactivating proteins are a class of toxins that target eukaryotic ribosomes, inhibit protein synthesis, and ultimately induce cell death. Several of these toxins pose significant clinical and public health threats. Among these, ricin, derived from the castor bean plant (Ricinus communis), is a highly potent biotoxin with recognized bioterrorism potential. Other ribosome-inactivating proteins, including Shiga toxin produced by pathogenic Shigella and Escherichia coli, as well as mucoricin from Mucorales fungi, contribute to disease severity and can lead to life-threatening complications. Despite these risks, no approved therapeutics are currently available. The development of effective inhibitors depends on robust and well-defined strategies to identify and validate small molecules that disrupt toxin–ribosome interactions. Efforts to target the catalytic active site have met with limited success, largely due to its broad, shallow, and highly polar architecture, which is not conducive to high-affinity binding by drug-like molecules. In contrast, the ribosome-binding interface represents a more tractable target, as it is essential for toxin recruitment and offers more structurally defined and druggable features. Inhibitors targeting this interface can also exert allosteric effects by disrupting long-range conformational coupling between the ribosome-binding region and the active site, thereby attenuating catalytic activity without directly engaging the catalytic pocket. In this review, we compile and evaluate biophysical and biochemical assays for the discovery and characterization of small-molecule inhibitors that target toxin–ribosome interactions. We examine in vitro binding approaches, including surface plasmon resonance-based fragment screening and fluorescence anisotropy assays for ranking inhibitory activity. We further review biochemical and molecular assays that assess ribosome protection from toxin-mediated depurination, along with complementary cell-based assays that evaluate functional rescue in cellular systems. Collectively, this review consolidates current screening methodologies and highlights opportunities to refine assay strategies, thereby supporting the advancement of targeted therapeutics. Full article

Background: Point mutations to the androgen receptor (AR) ligand-binding domain (LBD) are becoming increasingly recognized as a mechanism of therapeutic resistance in castration resistant prostate cancer (CRPC). The present review explores how point mutations induce molecular changes that contribute to the eventual treatment failure of androgen receptor pathway inhibitors (ARPIs) in CRPC. Methods: The PubMed database was searched for structural studies on the AR LBD. Eligible articles included molecular docking analysis and emphasized changes in ligand–receptor interactions after point mutation. Structural data were obtained from the Protein Data Bank (PDB) using the search parameters “Androgen receptor ligand binding domain”, “Homo sapiens”, and “X-ray diffraction”. PDB files of wild-type and point mutant AR LBDs were accumulated for analysis. Results: A functional shift from inhibiting to activating AR has been documented for multiple ARPIs. Crystallography data and in silico evaluation have deciphered how changes in steric hindrance of the AF-2 domain contribute to ARPI loss of function. To combat therapeutic resistance, discovery efforts have begun to consider combination approaches of orthosteric and allosteric inhibitors, as well as compounds that target other AR domains. Although lead compounds have been identified, none have progressed into the clinic. Conclusions: Questions remain regarding the best approach for rationally designing new AR targeting therapeutics. Understanding how structural changes to the AR LBD lead to the failure of clinical therapeutics is a necessary step that should precede drug discovery campaigns. Moreover, computational modeling is a powerful tool that should be leveraged to streamline therapeutic development. Full article

Background: Neurodevelopmental disorders span a wide spectrum of deficits, often with a known or suspected genetic basis. While some genetic determinants may indicate treatment with selective compounds, more often both the molecular cause of the disorder and the mechanism of action for the therapeutic compound are more ambiguously matched. Due to the polypharmacological nature of most neuroactive compounds, measuring gene expression changes following drug perturbation could be an effective strategy to gain insight into shared therapeutic action downstream of diversity in receptor interaction. High-throughput drug discovery platforms have effectively measured changes in gene expression following drug perturbation in cell cultures, but unfortunately, these platforms often lack specificity for neuroactive compounds, fail to capture the developmental influence of cell–cell interactions, and do not accurately model drug metabolism in an intact system. Methods: In this study, we present a high-throughput, low-cost and cell-type-specific approach for capturing transcriptional changes in neural cell populations following neuroactive compound exposure through the combined use of transgenic zebrafish, cell sorting, and bulk RNA-seq. Results: Our system captures unique transcriptional profiles between neuronal and non-neuronal cell populations and demonstrates specific drug responsiveness within our neuronal cell population. We assessed two known positive allosteric modulators (PAMs) of γ-Aminobutyric acid sub-type A receptors (GABA_AR), ivermectin and propofol, as a case study to explore shared pathway and gene expression changes following drug exposure; these chemically distinct agents share a mechanistic signature that dampens the neuronal hyperexcitability characteristic of a broad spectrum of neurodevelopmental disorders. Two shared downregulated genes reflect a core expression module for modulating GABAergic tone: SRC proto-oncogene, non-receptor tyrosine kinase (SRC), and Glutamate decarboxylase 2 (GAD2). Conclusions: We provide this methodology and analysis as a framework for exploring shared changes in gene expression following neuroactive compound exposure in vivo, leading to a more complete and nuanced understanding of therapeutic effects on neurons that can aid in drug repurposing efforts for neurodevelopmental disorders. Full article

Allosteric modulation has emerged as a powerful strategy for achieving superior selectivity and safety in drug discovery and protein function regulation. Unlike highly conserved orthosteric sites, allosteric pockets are structurally diverse and less evolutionarily constrained, making them particularly suitable for modulation by designed miniproteins. Miniproteins can provide extended binding interfaces and high affinity for shallow, dynamic, or cryptic regulatory surfaces that are often inaccessible to small molecules. Recent advances in artificial intelligence (AI) are transforming this field through deep learning-based structure prediction and generative modeling. These AI-driven approaches enable the identification of allosteric hotspots, characterization of conformational ensembles, and de novo design of structured miniprotein binders. They are rapidly expanding the landscape for designing selective modulators across diverse allosteric targets, including GPCRs, receptor tyrosine kinases, nuclear receptors, ion channels, and other protein–protein interaction systems. This review summarizes state-of-the-art AI-driven computational methodologies for designing miniproteins as potential allosteric modulators and discusses their current challenges and future opportunities in allosteric drug discovery. Full article

Aldose Reductase (AR; AKR1B1) is an enzyme that plays a key role in the metabolism of glucose and other carbonyl compounds, and whose hyperactivity contributes to oxidative stress and vascular dysfunction. Despite decades of investigation into this enzyme, inhibitors have failed to translate into clinical application for Diabetic Retinopathy (DR). We argue that these failures might arise from non-selective inhibition, considering the dual roles of AR, which contribute not only to DR pathology but also support retinal health, as AR is an important detoxifying enzyme for aldehydes produced during oxidative stress. Here, we discuss missing structural information, despite more than one hundred crystal structures of AR in complex with inhibitors. Our review bridges this gap by discussing how recent advances in structural biology, e.g., fragment-based drug discovery and MicroED, provide novel ways to selectively modulate AR functions, offering advantages for the detection of weak, allosteric, or conformation-dependent binding events. Despite past challenges, we suggest that therapeutic targeting of AR to find new-generation inhibitors will become more effective once we have a clearer understanding of the requirements for selective inhibition of AR, blocking its pathological impact while preserving its physiological functions. By integrating fragment screening and structural biology, we outline a strategy to reinvigorate AR modulation as a viable retina-specific approach for managing DR, with potentially broader relevance toward multiple diabetic microvascular complications. Full article

The COVID-19 pandemic exposed critical vulnerabilities in single-target antiviral strategies, highlighting the urgent need for multi-mechanism therapeutic approaches against emerging viral threats. Here, we present an integrated computational framework systematically evaluating natural fatty acids as potential dual ACE2 (Angiotension Converting Enzyme 2)-inflammatory modulators; compounds simultaneously disrupting SARS-CoV-2 viral entry through allosteric ACE2 binding while suppressing host inflammatory cascades; through allosteric binding mechanisms rather than conventional competitive inhibition. Using molecular docking across eight ACE2 regions, 100 ns molecular dynamics simulations, MM/PBSA free energy calculations, and multivariate statistical analysis (PCA/LDA), we computationally assessed nine naturally occurring fatty acids representing saturated, monounsaturated, and polyunsaturated classes. Hierarchical dynamics analysis identified three distinct binding regimes spanning fast (τ < 50 ns) to slow (τ > 150 ns) timescales, with unsaturated fatty acids demonstrating superior binding affinities (ΔG = −6.85 ± 0.27 kcal/mol vs. −6.65 ± 0.25 kcal/mol for saturated analogs, p = 0.002). Arachidonic acid achieved optimal SwissDock affinity (−7.28 kcal/mol), while oleic acid exhibited top-ranked predicted binding affinity within the computational hierarchy (ΔG_bind = −24.12 ± 7.42 kcal/mol), establishing relative prioritization for experimental validation rather than absolute affinity quantification. Energetic decomposition identified van der Waals interactions as primary binding drivers (65–80% contribution), complemented by hydrogen bonds as transient directional anchors. Comprehensive ADMET profiling predicted favorable safety profiles compared to synthetic antivirals, with ω-3 fatty acids showing minimal nephrotoxicity risks while maintaining excellent intestinal absorption (>91%). Multi-platform bioactivity analysis identified convergent anti-inflammatory mechanisms through eicosanoid pathway modulation and kinase inhibition. This computational investigation positions natural fatty acids as promising candidates for experimental validation in next-generation pandemic preparedness strategies, integrating potential therapeutic efficacy with sustainable sourcing. The framework is generalizable to fatty acids from diverse biological origins. Full article

Background: In this study, we report a novel series of proline- and pipecolic acid-based small molecules designed as allosteric inhibitors of the NS2B/NS3 serine proteases from dengue and Zika viruses, key targets in antiviral drug discovery. Results: Enzymatic studies revealed that S-proline derivatives bearing electron-withdrawing substituents on the aromatic ring, particularly that with a trifluoromethyl group in meta position (i.e., compound 3, IC₅₀ = 5.0 µM), were the most potent against DENV NS2B/NS3, while nitro-substituted inhibitors were mostly effective only against the ZIKV protease. R-configured pipecolic acid-based derivatives were the only ones active against DENV NS2B/NS3, even if the mid-micromolar range; however, they demonstrated improved cellular efficacy since inhibitors 24 and 27 exhibiting strong activity in a DENV2 protease reporter gene assay (EC₅₀ = 5.2 and 5.1 µM, respectively). All compounds showed no cytotoxicity (CC₅₀ > 100 µM) and were selective for the viral protease over off-target serine proteases. Structure-based approaches were exploited to map the druggable allosteric site close to Asn152. Conclusions: Our findings led us to identify proline and pipecolic acid-based inhibitors as promising leads for the development of selective flaviviral NS2B/NS3 allosteric inhibitors. Full article

Blind docking predicts binding interactions between two molecular entities without prior knowledge of the binding site. This approach is essential because it explores the entire surface of the receptor to identify potential interaction sites. Blind docking widely works for both protein–protein and ligand–protein interaction studies. In protein–protein blind docking, the method aims to predict the correct orientation and interface of two proteins forming a complex. Protein blind docking is particularly valuable in studying transient interactions, protein–protein recognition, signaling pathways, tentative and significant biomolecular assemblies where structural data is limited. Ligand–protein blind docking discovers potential binding pockets across the entire protein surface. It is frequently applied in early-stage drug discovery, especially for novel or poorly characterized targets. The method helps identify allosteric sites or novel binding regions that are not evident from known structures. Overall, blind docking provides a versatile and powerful tool for studying molecular interactions, enabling discovery even in the absence of detailed structural information. In this scenario, we reported a timeline of attempts to improve this kind of computational approach with ML and hybrid approaches to obtain more reliable predictions. We dedicate two main sections to protein–protein and protein-ligand blind docking, presenting the reliability and caveats for each approach and outlining potential future directions. Full article

The mammalian target of rapamycin pathway, mTOR, is a crucial signaling pathway that regulates cell growth, proliferation, metabolism, and survival. Due to its dysregulation it is involved in several ailments such as cancer or age-related diseases. The discovery of mTOR and the understanding of its biological functions were greatly facilitated by the use of rapamycin, an antibiotic of natural origin, which allosterically inhibits mTORC1, effectively blocking its function. In this entirely computational study, we investigated mTOR’s interaction with seven ligands: two clinically established inhibitors (everolimus and rapamycin) and five lignin-derived oligomers, a renewable natural polyphenol recently used for the drug delivery of everolimus. The seven complexes were analyzed through all-atom molecular dynamics simulations in explicit solvent using a high-performance computing platform. Trajectory analyses revealed stable interactions between mTOR and all ligands, with lignin-derived compounds showing comparable or enhanced binding stability relative to reference drugs. To evaluate the stability of the molecular complex and the behavior of the ligand over time, we analyzed key parameters including root mean square deviation, root mean square fluctuation, number of hydrogen bonds, binding free energy, and conformational dynamics assessed through principal component analysis. Our results suggest that lignin fragments are a promising, sustainable scaffold for developing novel mTOR inhibitors. Full article

Clusterin is one of the many known proteins implicated in cancer chemoresistance, which hinders the effectiveness of chemotherapy. This study aimed to design novel inhibitors targeting clusterin using fragment-based drug discovery (FBDD). This approach aims to develop new medicines by identifying small, simple molecules known as “fragments” that can bind to a specific target, such as a disease-causing protein. In this study, a primary ligand-binding site and an allosteric site on the clusterin molecule were identified through hotspot analysis. We screened commercially available fragment libraries for anti-cancer activity and applied the “rule of three” to ensure drug-like properties. The highest-affinity fragment underwent “fragment-growing” to develop potential drug candidates. After docking and toxicity screening, 194 candidate drugs were identified. Quantitative structure-activity relationship (QSAR) analysis revealed that the chemical size and complexity of the fragments significantly contributed to their binding affinity. Pharmacokinetic analyses of candidate drugs from FBDD followed by molecular dynamics simulation of the top 1 final candidate drug precursor demonstrated comparatively better affinity (average = −34.01 kcal/mol) than the reference compound (average = −6.15 kcal/mol) and significant ligand flexibility. This study offers a potential strategy to identify fragments or molecules that may serve as drugs against clusterin-related chemoresistance. Full article

Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challenging because they are often discovered serendipitously when both ligand-binding (holo) and ligand-free (apo) states are experimentally determined. Here, we introduce CrypTothML, a novel framework that integrates mixed-solvent molecular dynamics (MSMD) simulations and machine learning to predict cryptic sites accurately. CrypTothML first identifies hotspots through MSMD simulations using six chemically diverse probes (benzene, dimethyl-ether, phenol, methyl-imidazole, acetonitrile, and ethylene glycol). A machine learning model then ranks these hotspots based on their likelihood of being cryptic sites, incorporating both hotspot-derived and protein-specific features. Evaluation on a curated dataset demonstrated that CrypTothML outperforms recent machine learning-based methods, achieving an AUC-ROC of 0.88 and successfully identifying cryptic sites missed by other methods. Additionally, CrypTothML ranked cryptic sites as the top prediction more frequently than existing methods. This approach provides a powerful strategy for accelerating drug discovery and designing allosteric drugs. Full article

Phosphodiesterase-5 (PDE5) is a potent therapeutic target for the treatment of male erectile dysfunction and pulmonary arterial hypertension with several drugs available on the market. However, most of the reported PDE5 inhibitors lack specificity over PDE6, a holoenzyme in eleven PDE families, which may cause various adverse effects. Targeting a unique allosteric pocket has proved to be an effective approach to designing selective PDE5 inhibitors. In the present study, an integrated virtual screening procedure consisting of pharmacophore modeling screening, molecular docking, molecular dynamics simulations, and binding free energy calculations was applied to the discovery of novel PDE5 inhibitors targeting the allosteric pocket. Seven out of thirty-three molecules purchased from the SPECS database (a hitting accuracy of 21%) with novel scaffolds were PDE5 inhibitors with enzymatic inhibition ratios of more than 50% at a concentration of 10 μM. Predicted binding patterns indicate these hits fit well in the allosteric pocket in PDE5. In particular, compound AI-898/12177002 (IC₅₀ = 1.6 μM) demonstrates over 10-fold selectivity towards PDE6, providing a novel scaffold for the optimization of potent and selective PDE5 inhibitors with less adverse effects. Full article