Search Results (2,570)

Background: Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by impaired differentiation, apoptosis resistance, and metabolic reprogramming, which collectively contribute to therapeutic resistance and poor clinical outcomes. While targeted agents—such as LSD1 inhibitors, the BCL-2 inhibitor venetoclax, and IDH1 inhibitors—have provided clinical benefit, their efficacy is often limited by compensatory signaling and clonal evolution. This study aimed to identify FDA-approved compounds with multitarget potential to simultaneously modulate key epigenetic, apoptotic, and metabolic pathways in AML. Methods: Structure-based virtual screening of 3957 FDA-approved molecules was performed against three AML-relevant targets: lysine-specific demethylase 1 (LSD1), BCL-2, and mutant IDH1 (R132H). Top-ranked hits were evaluated using ADMET prediction and molecular dynamics (MD) simulations to assess pharmacokinetic properties, toxicity, and ligand–protein complex stability over 100 ns trajectories. Results: Three compounds—DB16703, DB08512, and DB16047—exhibited high binding affinities across all three targets with favorable pharmacokinetic and safety profiles. MD simulations confirmed the structural stability of the ligand–protein complexes, revealing persistent hydrogen bonding and minimal conformational deviation. These findings suggest that these repurposed drugs possess a promising multitarget profile capable of addressing AML’s multifactorial pathophysiology. Conclusions: This computational study supports the feasibility of a polypharmacology-based strategy for AML therapy by integrating epigenetic modulation, apoptotic reactivation, and metabolic correction within single molecular scaffolds. However, the identified compounds (Belumosudil, DB08512, and Elraglusib) have not yet demonstrated efficacy in AML models; further preclinical validation is warranted to substantiate these predictions and advance translational development. Full article

This work explores the impact of ergosterol (ERG) addition (0%, 0.5%, 1.0%, 1.5%, and 2.0%) on the physicochemical properties, conformational changes, and digestive characteristics of soy protein isolate (SPI) and wheat gluten (WG) processed by high-moisture extrusion. The results demonstrated that the incorporation of ERG significantly reduced the apparent viscosity and dynamic moduli of the feedstock system, enhancing melt fluidity and consequently reducing extrusion torque, die pressure, and specific mechanical energy. An appropriate amount of ERG (1.0%) effectively facilitated the development of a distinct fibrous morphology, increased the fibrous degree, lightened the color, and softened the texture. However, excessive addition weakened the fibrous structure due to excessively high fluidity. ERG influenced protein aggregation behavior through hydrophobic interactions, reduced thermal stability, and induced a transition in secondary structure from β-turns to α-helices. The in vitro digestibility initially decreased and then increased, with the lowest value observed at 1.0% ERG. This study indicates that ERG can elevate the performance and value of extruded products by modulating protein structure and rheological behavior, providing a theoretical basis for its application in plant-based meat analogue products. Full article

The SARS-CoV-2 pandemic has driven the emergence of many viral variants carrying multiple mutations, particularly in the spike glycoprotein, which enhance viral adaptability and may alter the structure and functionality of the protein. Here, we present, to the best of our knowledge, the first systematic and comparative structural analysis of monomeric spike protein subunit 1 from three distinct SARS-CoV-2 variants at physiological pH (7.4). A multimodal approach was employed, integrating experimental techniques, including Attenuated Total Reflection Infrared and circular dichroism spectroscopies, with computational methods such as molecular dynamics simulations and surface polarity analyses. This combined approach allowed us to characterize the secondary structure composition, three-dimensional conformational organization, and solvent interaction profiles of each variant. Our findings reveal how the structural and functional properties of the spike protein subunit 1 are influenced by specific amino acid mutations. Indeed, the observed conformational changes and variations in solvent interactions have significant implications for viral infectivity and immune evasion. These findings contribute to the broader understanding of the evolution of SARS-CoV-2 variants and offer valuable insights for drug development, targeted prevention strategies, and biosensor design. Full article

Protein–protein docking is a cornerstone of computational structural biology, yet its reliability for large, multimeric assemblies remains uncertain. Standard workflows typically include geometry optimization or molecular dynamics equilibration to relieve local strains and improve input quality, but the extent to which these preparatory steps alter docking outcomes has not been systematically evaluated. Here, we address this question using the mitochondrial chaperonin Hsp60, a dynamic double-ring complex essential for protein folding, and MIX, a kinetoplastid-specific protein with unresolved function, as a stress test system. By comparing docking predictions across minimized, equilibrated, and ensemble-refined structures of Hsp60 in three conformational states (apo, ATP-bound, and ATP–Hsp10), we show that structural relaxation profoundly reshapes the docking landscape. Minimization alone often yielded favorable scores but localized binding, while longer MD trajectories exposed alternative sites, including central cavity, equatorial ATP pocket, and apical domain, each consistent with distinct regulatory hypotheses. These findings reveal that docking outcomes are highly sensitive to receptor preparation, especially in complexes undergoing large conformational transitions. More broadly, our study highlights an underappreciated vulnerability of docking pipelines and calls for ensemble-based and dynamics-aware approaches when predicting interactions in large biomolecular machines. Full article

Background/Objectives: Naegleria fowleri is a free-living protozoan that causes primary amoebic meningoencephalitis, a rapidly progressing central nervous system infection with high mortality rates and limited treatment options. Targeting virulence-associated proteins is essential for effective drug development. Fowlerpain-1 (FWP1), a papain-like cysteine protease (CP) implicated in extracellular matrix degradation and host–cell cytotoxicity, has been investigated as a therapeutic target. This study aimed to evaluate the FWP1 pocket geometry and stefin binding using an integrated in silico structural biology approach. Methods: A computational pipeline was used, including AlphaFold2-Multimer modeling of FWP1–stefin complexes, 20-ns molecular dynamics simulations under NPT conditions for conformational sampling, and molecular mechanics Poisson–Boltzmann surface area free energy calculations. Three natural CP inhibitors (stefins) were investigated. Structural stability was assessed using root mean square deviations, and binding profiles were characterized using protein–protein interaction analysis. Results: Stable FWP1–stefin interaction interfaces were predicted, with human stefin A showing favorable binding free energy. Two conserved motifs (PG and QVVAG) were identified as critical mediators of active-site recognition. Druggability analysis revealed a concave pocket with both hydrophobic and polar characteristics, consistent with a high-affinity ligand-binding site. Conclusions: This computational study supports a structural hypothesis for selective FWP1 inhibition and identifies stefins as promising scaffolds for developing structure-guided protease-targeted therapeutics against N. fowleri. Full article

Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as $q_{d0}$ and $q_{d1}$, yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates $q_{d0}$, $q_{d1}$, and $Z_c$ directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of $n=360$ observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W, ${\gamma }_{d,\mathrm{field}}$, and $R{C}_{\mathrm{SPC}}$), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ($R^2$, RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for $q_{d1}$ ($R^2=0.794$, RMSE $=5.866$), with XGBoost close behind ($R^2=0.773$, RMSE $=6.155$). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables (${\gamma }_{d,\mathrm{field}}$, $R{C}_{\mathrm{SPC}}$, and W) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Full article

The antimalarial combination of sulfadoxine–pyrimethamine is used as a preventive treatment in pregnant women and children in Africa. Sulfadoxine inhibits the Plasmodium falciparum dihydropteroate synthase (PfDHPS), but resistance has emerged through point mutations in this enzyme. In this study, we investigate the impact of mutations on the structural and dynamic properties of PfDHPS using molecular dynamics simulations. Our results show that PfDHPS maintains overall structural integrity across various combinations of resistance-associated mutations. However, significant differences emerge in ligand binding. Sulfadoxine binding is particularly impacted and shows reduced stability in the mutant systems compared to the wild-type enzyme, while the natural substrate generally maintains stable or even enhanced binding affinity. A key finding is the critical role of the D2 loop, whose conformational flexibility influences ligand retention. In mutant enzymes, the disruption of interactions between the D2 loop and the natural substrate correlates with decreased affinity. In contrast, specific mutations in the loop are associated with an increased affinity. Conversely, sulfadoxine binding is associated with an open D2 loop conformation, facilitating its release from the active site. Finally, the intrinsic flexibility of sulfadoxine emerges as an important determinant of this process. Together, these results provide molecular-level insights into the mechanisms of resistance in PfDHPS and establish a structural and dynamic framework for future investigations into its catalytic function and inhibitor design. Full article

The impact of counterion structure, especially variations in constitutional and stereochemical isomers, on the properties and performance of AABSs remains under-explored. This study investigates how structural variations, particularly the stereochemistry of diammonium cyclohexane (DACH) counterions, influence the self-assembly behavior of AABSs. Four AABSs: undecanoyl-glycine, -L-alanine, -L-valine, and -L-leucine, were paired with six DACH counterions representing cis/trans isomers of 1,2-, 1,3-, and 1,4-DACH. Critical micelle concentrations (CMCs) were determined via conductimetry, and micellar sizes were measured using dynamic light scattering. The degree of counterion binding (β) was calculated to probe micelle stability, while geometry-optimized structures of the DACH isomers were obtained using density functional theory. Lastly, pH measurements were taken to probe the protonation of DACH counterions at their natural pH, where both the DACH counterion and AABS headgroups intrinsically behave as buffers. Results indicate that while surfactant hydrophobicity primarily dictates CMC in other AABS/DACH combinations, trans-1,3-DACH leads to consistently higher CMCs. This deviation likely arises from its structural conformation, which positions the amine groups an intermediate distance of ~4.4–4.5 Å apart, allowing a small fraction of divalently charged counterions to form strong electrostatic bridging pockets at the micelle interface. These interactions dominate over headgroup effects, leading to elevated and surfactant-independent CMC values. Regarding size and other unusual trends in the systems, cis- isomers formed slightly larger micelles, and trans-1,4-DACH induces abnormal aggregation in undecanoyl-glycine leading to temperature dependent gel formation. These findings highlight the significant influence of counterion structure on AABS behavior and support counterion design as a strategy for enhancing surfactant performance in sustainable applications. Full article

Background: Enterotoxigenic Escherichia coli (ETEC) is a zoonotic pathogen causing diarrhea and mortality in infants and livestock. Its numerous serotypes necessitate the urgent development of multivalent vaccines for effective prevention, thereby reducing public health and economic threats. Methods: Computational bioinformatics analyses were conducted on five major ETEC adhesins structural subunits (FaeG, FanC, FasA, FimF41a, and FedF). Dominant epitopes were selected and concatenated via flexible linkers, incorporating the PADRE sequence and LTb adjuvant to design a multi-epitope fusion antigen (MEFA). The recombinant MEFA protein was expressed in a prokaryotic system. Furthermore, molecular dynamics simulations, docking, and immune simulations assessed structural stability and immunogenicity. Immunoreactivity was tested by Western blot. Murine immunization evaluated antibody responses, lymphocyte proliferation, cytokine secretion, and protection against ETEC challenge. Results: Structural modeling showed an extended conformation, with docking and simulations indicating strong immune activation. Western blot confirmed MEFA immunoreactivity. MEFA induced high antigen-specific antibody titers, enhanced splenocyte proliferation, and increased IFN-γ and IL-4 secretion, indicating a Th2-biased response in mice. Vaccinated mice survived lethal ETEC challenge and maintained intestinal integrity. Conclusions: The MEFA candidate vaccine effectively induces robust humoral and cellular immune responses and provides protection against ETEC infection, representing a promising strategy for next-generation multivalent ETEC vaccines. Full article

In the context of the study of G-quadruplex (G4), the main field of focus is usually referred to binding molecules able to interact with these non-canonical conformations and stabilizing them leading to diverse biological effects. Although the cellular events triggered by these ligands are useful for potential anticancer applications, the development of innovative molecular tools to gain new information about G4 has become more urgent. The concept of G4-interacting molecular tools refers to chemical entities that can bind and interact with G-rich sequences of the genome—like traditional ligands—but simultaneously provide external outputs that can be interpreted and studied to obtain insights on their dynamics, position in the cellular context and more. Starting from traditional chemical approaches, researchers have worked to produce sophisticated and complex synthetic strategies in order to introduce more accurate instruments for their aims. This review provides a comprehensive and up-to-date overview of this research area by detailing the major classes of molecular tools, describing the latest updates about three main classes: small-molecules fluorescence probes, G4-binding metal complexes, and products of conjugation strategies. Overall, advancements in molecular tools targeting G4s have made the study of G4 formation, dynamics, and functions much easier; thus, increasing the knowledge of G4 biology, and creating new opportunities for biomedical and therapeutic applications, ultimately highlighting the importance of the development of molecular tools in G4 research. Full article

Glycine receptors (GlyRs) are essential for inhibitory neurotransmission in the central nervous system (CNS) and represent promising targets against neurological disorders. Several indole alkaloids from Gelsemium species have been shown to modulate GlyRs. Notably, the anxiolytic and analgesic properties of certain Gelsemium alkaloids appear to depend on GlyR modulation. However, prior studies have focused on only a few indole alkaloids, leaving the activity of other Gelsemium compound classes unexplored. This study employed an integrative in silico approach to investigate the interactions between GlyR α1 and α3 subtypes and 162 structurally diverse Gelsemium compounds. Physicochemical, pharmacokinetic, and toxicological analyses identified compounds with favorable bioavailability in the CNS. Molecular docking revealed that indolic alkaloids bind the GlyR orthosteric site with profiles comparable to the reference Gelsemium compound, gelsemine. Molecular dynamics simulations confirmed the stability and conformational integrity of selected ligand-receptor complexes. Overall, novel potential GlyR modulators were identified, with several compounds showing a promising selectivity profile towards GlyR α1 and α3 subtypes. These findings further support the therapeutic potential of Gelsemium alkaloids and provide a foundation for further pharmacological and toxicological validation. Full article

This paper presents a novel class of conformable integro-differential operators designed to model systems with rapid and ultra-rapid dynamics. This class of local operators enables the design of controllers and observers that induce hyperexponential convergence and provide robustness against bounded disturbances and dynamic uncertainties. The proposed method leverages Nested Exponential Functions (NEFs) and Nested Exponential Factorial Functions (NEFFs) to capture fast dynamics effectively. Additionally, the proposed study examines the Fundamental Theorem of Calculus in the context of Nested Exponential Conformable (NEC) operators, unveiling structural properties, such as stability and robustness, that produce dynamical systems with enhanced hyperexponential convergence and faster dynamics compared to existing approaches. Stability results for NEC systems are established, and some illustrative examples based on numerical simulations are presented to demonstrate the reliability of the proposed approach. Full article

The inactivation of thrombin by antithrombin is highly enhanced by the presence of heparin chains forming “bridges” between the two proteins. X-ray structures for such ternary complexes have been published, but the molecular background of the lower efficiency of smaller heparinoids on thrombin inhibition remains poorly understood. Antithrombin-resistant prothrombin mutants (mutations affecting Arg596 in prothrombin) have been reported that cause severe thrombophilia. Our aim was to study the interactions in the antithrombin–thrombin Michaelis complex both in the presence and the absence of a heparinoid chain and in the presence of pentasaccharide by using molecular dynamics. We also intended to study the complexes of thrombin mutants as well as a known alternative antithrombin conformation at the “hinge” region built using docking. The binding between the proteins was investigated by Gaussian Accelerated Molecular Dynamics (GaMD). We compared the contribution of several amino acids at the binding “exosites” between AT and the wild type and mutant thrombins and between systems containing or not containing a heparinoid. In the docking-based simulations, several of the analyzed amino acid pairs no longer contributed to the interaction, suggesting that the open “hinge” conformation has limited biological relevance. We could identify multiple conformational types using clustering, revealing high flexibility in mutants and systems without heparinoid, probably indicating lower stability. We were also able to detect the allosteric effects of the ligands on the bound thrombin. In summary, we were able to obtain conformations using GaMD that can explain the better protein–protein interactions in the ternary complexes and the impaired AT binding of the thrombin Arg596 mutants at an atomic level. Full article

Offshore reservoirs in the high water-cut stage present significant development challenges, including declining production, complex remaining oil distribution, and the inadequacy of conventional evaluation methods to capture intricate flow dynamics. To overcome these limitations, this study introduces a novel approach based on flow diagnostics for performance evaluation and potential adjustment. The method integrates key metrics such as time-of-flight (TOF) and the dynamic Lorenz coefficient, supported by reservoir engineering principles and numerical simulation, to construct a multi-parameter evaluation system. This system, which also incorporates injection–production communication volume and inter-well fluid allocation factors, precisely quantifies and visualizes waterflood displacement processes and sweep efficiency. Applied to the QHD32 oilfield, this framework was used to establish specific thresholds for operational adjustments. These include criteria for infill drilling (waterflooded ratio < 45%, remaining oil thickness > 6 m, TOF > 200 days), conformance control (TOF < 50 days, dynamic Lorenz coefficient > 0.5), and artificial lift optimization (remaining oil thickness ratio > 2/3, TOF > 200 days). Field validation confirmed the efficacy of this approach: an additional cumulative oil production of 165,600 m³ was achieved from infill drilling in the C29 well group, while displacement adjustments in the B03 well group increased oil production by 2.2–3.8 tons/day, demonstrating a significant enhancement in waterflooding performance. This research provides a theoretical foundation and a technical pathway for the refined development of offshore heavy oil reservoirs at the ultra-high water-cut stage, offering a robust framework for the sustainable management of analogous reservoirs worldwide. Full article

Occluded person re-identification (Re-ID) faces significant challenges, mainly due to the interference of occlusion noise and the scarcity of realistic occluded training data. Although data augmentation is a commonly used solution, the current occlusion augmentation methods suffer from the problem of dual inconsistencies: intra-sample inconsistency is caused by misaligned synthetic occluders (an augmentation operation for simulating real occlusion situations); i.e., randomly pasted occluders ignore spatial prior information and style differences, resulting in unrealistic artifacts that mislead feature learning; inter-sample inconsistency stems from information loss during random cropping (an augmentation operation for simulating occlusion-induced information loss); i.e., single-scale cropping strategies discard discriminative regions, weakening the robustness of the model. To address the aforementioned dual inconsistencies, this study proposes the unified Multi-Aligned and Multi-Scale Augmentation (MA–MSA) framework based on the core principle of ”synthetic data should resemble real-world data”. First, the Frequency–Style–Position Data Augmentation (FSPDA) module is designed: it ensures consistency in three aspects (frequency, style, and position) by constructing an occluder library that conforms to real-world distribution, achieving style alignment via adaptive instance normalization and optimizing the placement of occluders using hierarchical position rules. Second, the Multi-Scale Crop Data Augmentation (MSCDA) strategy is proposed. It eliminates the problem of information loss through multi-scale cropping with non-overlapping ratios and dynamic view fusion. In addition, different from the traditional serial augmentation method, MA–MSA integrates FSPDA and MSCDA in a parallel manner to achieve the collaborative resolution of dual inconsistencies. Extensive experiments on Occluded-Duke and Occluded-REID show that MA–MSA achieves state-leading performance of 73.3% Rank-1 (+1.5%) and 62.9% mAP on Occluded-Duke, and 87.3% Rank-1 (+2.0%) and 82.1% mAP on Occluded-REID, demonstrating superior robustness without auxiliary models. Full article