Searching for the global-minimum (GM) structure of clusters is a fundamental challenge in computational chemistry, as the potential energy surface (PES) of clusters exhibits a vast number of local minima that increase exponentially with cluster size. This work presents GMMLP (Global-Minimum Search of Clusters Accelerated by Machine Learning Potentials), an efficient software package developed for identifying the GM structures of clusters. GMMLP integrates the atom-in-molecules neural network potential (AIMNet2) with an improved genetic algorithm (GA), leveraging the high accuracy of AIMNet2 trained at the ωB97M-D3/def2-TZVPP level of theory and the global search capability of the optimized GA. To validate GMMLP, benchmark tests were performed on nine types of clusters, including (CH2O)n, (CH3NH2)n, (CH3OH)n, (CH4)n, (H2O)n, (H2SO4)n, (HNO3)n, (NH3)n, and [CO(NH2)2]n (n = 1-10). Computational results show that GMMLP efficiently explores the PES, searching a total of 9869 isomers across all benchmarked clusters with a total wall time of 39,055.14 s (~10.8 h). The average computational time per isomer ranges from 0.22 s for (CH4)n to 10.01 s for (H2SO4)n, demonstrating remarkable efficiency. Additionally, the evolution of relative energy and optimized structures of low-lying isomers are analyzed to illustrate the reliability of the search process. GMMLP provides a powerful tool for cluster research, enabling fast and accurate GM structure identification for a wide range of clusters, which is crucial for understanding cluster properties and their applications in chemistry, materials science, and related fields.
The design of the metal-support interface is fundamental to the performance of single-atom catalysts (SACs). Here, we present a comprehensive computational study by using Density Functional Theory (B3LYP-D3/LANL2DZ for zinc atoms and 6-31+G(d) for other nonmetal atoms) in Zinc-embedded Lantern Organic Frameworks (Zn@LOFs) to resolve this design. By systematically comparing sp- and sp3-bridging motifs, we reveal a critical structural dichotomy: while flexible sp3-frameworks favor endo-configuration, rigid sp-bridging systems introduce a unique kinetic trap that stabilizes accessible exo-configuration. Crucially, agglomeration analysis confirms that the sp-LOF architecture could suppress ZnZn clustering. Beyond stability, we demonstrate precise electronic tunability; endo-Znn@sp-LOFs leverage metal-ligand π-conjugation to significantly narrow the HOMO-LUMO gap, whereas endo-Znn@sp3-LOF affects the gap slightly. Furthermore, electron transport analysis based on Yoshizawa's orbital rules confirms that these constructs maintain good molecular conductance between two ends of the framework-protected Zn-Zn metal chains (10-2 to 10-3 G0; Log[G0] from -2 to -3), despite smaller values in comparison to the free-standing zinc metal chains. Moreover, the predicted gas adsorption behavior indicates that CO2 is predicted to interact more strongly than CO, with adsorption strength modulated by the LOF motifs and the presence and position of Zn centers. These findings establish Zn@LOFs not merely as passive supports, but as tunable, dual-function platforms capable of integrating potential single-atom catalysis with efficient charge transport.
We present PES-trotter, a cross-platform application for the exploration and analysis of 3D potential-energy landscapes generated from multi-dimensional potential-energy surfaces (PESs) of molecular systems. Inspired by the main ideas behind the previously released software AVATAR (Martino et al., Journal of Computational Chemistry 41 (2020): 1310-1323) relying on virtual-reality technology and third-party commercial software, PES-trotter is based on the open-source game engine Godot and related open-source assets, and introduces radically new important features for the navigation and topological analysis of the PES, among which the possibility of (1) navigating in walk, fly or free-mouse mode, (2) plotting energy profiles from custom curvilinear paths, (3) computing critical points and minimum-energy paths, and (4) playing back dynamical trajectories in first-person ride or spectator mode. Designed to match high portability and adaptability, PES-trotter can be run either as a stand-alone application on Windows, Linux, Android and macOS operating systems or in the window of a simple web browser on devices as widespread as mobile phones. In the article, the main features of PES-trotter are thoroughly described through two illustrative applications (the conformational analysis of a functionalized glycine and the analysis of classical trajectories for the H + LiH+ → $$ \to $$ H2 + Li+ reactive process) highlighting the versatilty of PES-trotter as an innovative and accessible tool supporting chemical research and education.
Dipeptidyl peptidase-1 (DPP1) inhibitors prevent the activation of neutrophil serine proteases and reduce exacerbations in people with bronchiectasis. We previously identified a novel effect of DPP1 inhibitors in reducing the neutrophil pseudoenzyme azurocidin-1 (AZU1). The aim of this study was to investigate the role of AZU1 in the pathophysiology of bronchiectasis. Sputum AZU1 concentrations were analysed in multiple cohorts. These consisted of two observational cohorts of patients with bronchiectasis (EMBARC BRIDGE cohort 1 and cohort 2) and a cohort of patients with chronic obstructive pulmonary disease (COPD; TARDIS COPD cohort) to correlate AZU1 with disease severity and exacerbations. A rhinovirus challenge study was used to investigate AZU1 concentrations during experimental exacerbation in COPD, people who smoke, and controls. A post-hoc analysis of the phase 2 WILLOW trial of brensocatib versus placebo was used to assess the effect of DPP1 inhibition on airway AZU1. Higher AZU1 sputum concentration was associated with increased bronchiectasis disease severity index (p<0·0001), decreased percentage predicted forced expiratory volume in 1 second (r=-0·4662, p<0·001), and increased exacerbation frequency (p<0·0019; EMBARC cohort 1, n=197). AZU1 was associated with radiological severity (Reiff score), symptoms (quality of life bronchiectasis respiratory symptom score), and bacterial infection (sputum microbiology and 16S microbiome alpha diversity; highest levels of AZU1 were found in airway samples with Pseudomonas aeruginosa; p<0·0001; EMBARC cohort 2, n=144). Bronchiectasis patients with bacterial and viral exacerbations had increased concentrations of AZU1 (p=0·0003; n=96). These findings were extended to COPD, in which AZU1 was related to COPD severity (COPD cohort, n=101), and in patients with COPD challenged with rhinovirus A16, AZU1 was increased at day 9 post-challenge (p<0·001; n=9). In-vitro AZU1 impaired ciliary function and epithelial integrity, suggesting a mechanism by which AZU1 drives disease pathogenesis. In a post-hoc analysis of the WILLOW trial, AZU1 was the most downregulated protein with brensocatib treatment (brensocatib 10 mg, n=71; brensocatib 25 mg, n=73; and placebo, n=71). Over 24 weeks, AZU1 was significantly reduced by DPP1 inhibition (p<0·0001). AZU1 was identified as a novel marker of disease severity in bronchiectasis, associated with bacterial infection and exacerbation, and targeted by DPP1 inhibition. EMBARC3 and Insmed.
High-entropy alloys (HEAs) have emerged as a revolutionary class of materials with exceptional mechanical, catalytic, and corrosion-resistant properties, attributed to their unique multi-component equiatomic or near-equiatomic compositions. However, the vast configurational space of HEAs poses unprecedented challenges for identifying the structure through experimental or conventional computational methods. This work presents GMHEA, a novel software package developed for multi-variable optimization of HEAs, integrating an improved genetic algorithm (GA) with the effective medium theory (EMT) for rapid energy calculations. The software is designed to handle multi-component HEAs exemplified by NixCuxPdxAgxPtxAux (x = 1~10) and addresses key limitations of existing methods, including low search efficiency, high computational cost, and inadequate sampling of configurational space. GMHEA incorporates adaptive genetic operators (cut-and-splice pairing, soft mutation, strain mutation), structure comparison based on fingerprint, and variable-cell relaxation to ensure robust convergence to the stable structures. Comprehensive computational tests on HEAs demonstrate that GMHEA achieves a balance between accuracy and efficiency: the average computational time per atom ranges from 23.13 to 52.19 s. Structural analysis reveals that the configurations of HEAs exhibit short-range order (SRO) with optimized interatomic distances, correlating strongly with thermodynamic stability. This software provides a powerful tool for accelerating the design and development of HEAs by enabling rapid identification of stable atomic structures, with broad implications for applications in catalysis, energy storage, and advanced manufacturing.
Aquaporins (AQPs) are classical water channels that also conduct small gas molecules such as O 2 $$ {\mathrm{O}}_2 $$ and CO 2 $$ {\mathrm{CO}}_2 $$ across the membrane. The hydrophobic central pore, located at the fourfold symmetry axis of an AQP tetrameric architecture, has been proposed to constitute the most optimal pathway for gas transport, although monomeric water pores can also contribute somewhat to permeation of less hydrophobic species. Here, we report a comparative molecular dynamics (MD) study of gas permeability in a plant AQP and a mammalian AQP1, taking advantage of complementary computational protocols including flooding simulations, umbrella sampling, and implicit ligand sampling. PIP2;1 AQPs, present in plants, are experimentally reported to have lower gas permeability than AQP1, which is present both in plants and animals. Using the spinach PIP2;1 (SoPIP2;1) and bovine AQP1 (bAQP1) as the models, the study unravels the specific structural features controlling the permeability of the central pore to gases. In SoPIP2;1, residue Trp79, which is highly conserved in the plant PIP2;1 family and lines directly the central pore, forms a major constriction region and the main barrier against gas permeation. Notably, the occluding conformation of the four Trp79 residues from the four monomers is stabilized by another conserved residue, Phe207 in the central pore. Sequence and structural comparisons show that both of these residues are replaced by less bulky residues in AQP1, for example, by Leu56 and Ala179, respectively, in bAQP1. The role of Phe207 residues in hindering gas permeation through SoPIP2;1 is confirmed by in silico alanine substitution, which reveals its effect on the local constriction produced by Trp79 residues. Conversely, by mutating Leu56 to tryptophan and Ala179 to phenylalanine in bAQP1, we engineer the protein to a less permeable gas channel.
Electrochemical sensors face critical challenges in achieving ultra-high sensitivity and specificity within complex biological matrices. To address these limitations, this study proposes an integrated electrochemical computational model (IECM), a novel end-to-end framework that couples kinetics, electrochemistry, and signal amplification sub-modules to predict and optimize sensor performance. Unlike previous descriptive reviews, this work validates the IECM through rigorous experimentation using reduced graphene oxide MXene/ anchors gold nanoparticles nanocomposite interfaces. The limit of detection was determined based on the standard definition (3sigma/slope, where sigma is the standard deviation of the blank signal), yielding exceptionally low values of 0.12 fg ml-1for prostate-specific antigen and 5.0 × 10-18M for microRNA-21, with corresponding calibration curve slopes confirming high signal-to-noise ratios. In complex matrices such as serum and saliva, the sensor demonstrated robust anti-interference capabilities, achieving recovery rates of 92.0% ± 3.5% and cross-reactivity rates below 3.5%. Long-term stability tests indicated a signal retention of 68.7% after 8 weeks under moderate storage conditions. Furthermore, double-blind clinical trials against gold-standard assays revealed a high concordance rate (κ = 0.93), confirming the model's predictive accuracy and the sensor's clinical potential. This research establishes a unified computational-experimental paradigm for the rational design of next-generation biosensors.
General anesthetics are widely used to induce reversible unconsciousness, yet their molecular mechanisms remain incompletely understood. Despite their low binding affinities and broad protein-binding promiscuity, general anesthetics still interact with neuronal proteins in a structurally selective manner. Experimentally, chemically modified probes have been used to map their protein targets. However, the biases introduced by the structural modifications of these remain unknown, raising the key question of how reliable such experiments are in capturing true anesthetic-protein interactions. In this study, we present an interactome-scale computational approach to characterize anesthetic-protein interactions using high-throughput molecular docking. We screened two families of anesthetic ligands-propofol and etomidate, as well as chemically modified analogs of each-against a set of 2,388 experimentally determined mouse neuronal protein structures. By comparing parent and modified ligands, we reveal how functional group-specific biases, introduced by chemical modifications, altering ligands engage protein environments across the interactome. Docking poses and energies identify recurrent binding-site features and quantify how small modifications reshape interaction profiles. Using 3D spatial distribution functions, we summarize local amino acid environments surrounding each ligand, providing intuitive visualizations of interaction hotspots. This analysis exposes conserved and variable elements of anesthetic recognition and clarifies how probe modifications shape observed patterns. Our results offer a statistical and structural description of anesthetic binding across an interactome, providing mechanistic insight into affinity-based protein profiling mapping biases and guiding improved probe and drug design.
To provide a guide for selecting appropriate calculation methods for conformational analyses of organic molecules with explicit consideration of solvation effects, the A-values of twenty-two substituents were computed with solvation corrections across multiple solvation models. In addition, conformational equilibrium analyses of 1,4- and 1,2-trans-difluorocyclohexanes were performed as complementary benchmark systems, and a comprehensive evaluation was achieved through integration of both datasets. Comparison with reported experimental data furnished a robust benchmark for identifying the optimal combination of solvation models and theoretical levels for energy calculation and geometry optimization.
The p47ING1a isoform of the ING1 tumor suppressor regulates cellular senescence through Rb-dependent pathways via its plant homeodomain (PHD) zinc-finger, which recognizes the H3K4me3 histone mark. However, the mutational landscape of p47ING1a and the functional consequences of PHD-domain nonsynonymous single-nucleotide polymorphisms (nsSNPs) remain poorly characterized. This study aimed to identify and structurally evaluate the most deleterious nsSNPs in p47ING1a and clarify their potential role in disrupting ING1 tumor-suppressor activity. A total of 347 missense nsSNPs were retrieved from the NCBI dbSNP database and screened using 12 sequence-based computational tools. Variants consistently predicted as deleterious were further evaluated by I-Mutant stability analysis and ConSurf evolutionary conservation profiling. Three-dimensional structural modeling was performed using AlphaFold3, refined through GalaxyRefine, and validated by ERRAT, PROCHECK, and TM-align. Mutation-induced structural and binding effects were assessed using Missense3D, mCSM, and BeAtMuSiC. Post-translational modification sites were predicted via NetPhos 3.1, GPS 3.0, BDM-PUB, and NetOGlyc 4.0. Protein-protein interaction networks were constructed using STRING and Gene MANIA. Pan-cancer expression was analyzed through UALCAN and the Human Protein Atlas. Twelve computational tools converged on six high-priority variants, namely, C358S, C374G, W378G, F379V, S382L, and R400P. All localized exclusively within the PHD zinc-finger domain, residues 353-402. All six mutations were consistently predicted to destabilize the p47ING1a protein across multiple stability analyses. Six nsSNPs in the PHD domain of p47ING1a are predicted to disrupt protein stability, H3K4me3 binding, and Sin3A/HDAC complex interactions, thereby impairing ING1 tumor-suppressor function. These findings provide a computational basis for prioritizing variants for experimental validation through site-directed mutagenesis, chromatin-binding assays, and structure-guided therapeutic targeting of the PHD-H3K4me3 interface.
The rational design of lanthanide-based luminescent systems using purely theoretical approaches remains challenging due to the reliance of existing models on experimental data. Herein, we propose a theoretical protocol for the design of new Eu3+ based luminescent systems, starting from a previously synthesized and experimentally characterized complex. The approach is based on two semiempirical models: (i) a combination of the QDC and BOM models for the calculation of Judd-Ofelt intensity parameters (Ω2 and Ω4) and (ii) a model based on the van Dijk-Schuurmans equation and Fermi's golden rule to estimate nonradiative emission rates (ANR) from radiative rates (AR). The model for calculating the Judd-Ofelt intensity parameters was parameterized using the [Eu2(Ibf)6(bpy)2] complex as a reference and validated with the analogous complexes [Eu2(Ibf)6(4,4'-dmbpy)2] and [Eu2(Ibf)6(5,5'-dmbpy)2]. In contrast, due to its mathematical structure, the model for calculating ANR required the inclusion of all three systems in the parameterization set. The models reproduce the intensity parameters with average errors below 4% and the nonradiative rate with errors below 0.2%. The protocol was applied to eight new complexes obtained by substituting the bipyridine methyl group with NH2, NO2, OCH3, and OH at the 4,4'-and 5,5'-positions. The theoretical predicted luminescent properties indicate that methoxy-substituted systems are predicted to exhibit superior performance, with predicted quantum yields of ~93.8%. This protocol provides a computationally efficient strategy for the rational design of Eu3+ based luminescent materials.
This study proposes a fine-scale urban air quality assessment framework to examine how building morphological parameters (BMPs; building surface fraction, BSF, and occlusivity, OCC) modulate near-surface meteorology, and how the combined effects of BMPs and pollutant emissions shape the distributions of CO, NO2, O3, and PM2.5. The framework employs a Fine-scale Air-Quality Simulation Unit (FASU) that integrates a computational fluid dynamics model coupled with a chemistry module, mesoscale background meteorological and concentration fields, and high-resolution top-down emissions. It is applied to a heterogeneous urban area in Incheon, South Korea, where FASU performance is evaluated against observations from a meteorological station and multiple air pollutant monitoring sites. To diagnose spatial controls, the 6 km × 6 km domain is partitioned into 36 subzones, and multiple linear regression is applied to relate subzone-mean surface concentrations to emissions and BMPs. Because near-surface wind speed is strongly correlated with BSF and OCC, it is excluded from the predictor set to avoid multicollinearity, and BMPs are used as morphological proxies for ventilation. Surface concentrations are most strongly associated with emissions for CO, NO2, and O3, whereas OCC exhibits the strongest association with PM2.5. Even with comparable emissions, differences in BMPs yield systematic concentration differences, underscoring the role of urban morphology in modulating near-surface air quality. Results show that wind corridors and open spaces, especially in high-rise districts, enhance ventilation and reduce pollutant accumulation. The framework and findings help identify morphology- and emission-driven hotspots and inform urban planning and air quality management in dense districts.
It has recently been shown [J. Chem. Phys. 157, 074106 (2022)] that the propagator (P), step size (S) and total time (T) required by real-time time-dependent density functional theory (RT-TDDFT) simulation of X-ray absorptions (XAS) can be determined automatically (Auto) for whatever chemical systems described by whatever electronic Hamiltonians and basis sets, by making use only of the ground-state Kohn-Sham core orbital energies. The AutoPST algorithm is improved here in two aspects: (1) a universal bivariate linear relation is established for accurate predication of the time steps for both K $$ K $$ - and L $$ L $$ -edge XAS of any desired spectral accuracy; (2) an automated orbital selection scheme is introduced to pick up only those "active" core and virtual canonical molecular orbitals (CMO), thereby extending AutoPST to AutoSTOP. Such orbital selection is particularly necessary when an uncontracted basis set is used, which generates many high-lying CMOs that have no contributions to near-edge XAS but render the time step exceedingly small. The ratio of the number of active CMOs over the total number of CMOs decreases quickly as the increase of molecular size, thereby ensuring computational efficiency. It is also shown that both singlet and triplet core excited states of a closed-shell system can be obtained by RT-TDDFT with a weak spin-dependent external field. Spin-orbit couplings between the so-obtained singlet and triplet states can then readily be calculated to obtain the L 2 $$ {L}_2 $$ and L 3 $$ {L}_3 $$ spectra. Finally, the linearized variant of RT-TDDFT is briefly discussed.
The growing demand for sustainable hydrogen production has intensified research into photocatalytic water splitting driven by solar energy. Graphitic carbon nitride (g-C3N4), a metal-free polymeric semiconductor with visible-light activity, tunable electronic structure, low cost, and environmental compatibility, has emerged as a promising photocatalyst. Nevertheless, pristine g-C3N4 is limited by insufficient visible-light absorption, sluggish charge transport, rapid electron-hole recombination, and low surface reactivity. Heterojunction engineering has therefore become a key strategy to overcome these intrinsic limitations and enhance photocatalytic efficiency. Distinct from conventional experimental or performance-oriented reviews, this work presents a computationally driven and methodology-oriented review that explicitly addresses how to model g-C3N4-based heterojunction photocatalysts using density functional theory (DFT). Rather than merely summarizing reported efficiencies, this review provides a step-by-step conceptual and practical framework that enables readers to rationally construct, analyze, and optimize heterojunction photocatalysts at the atomic scale. The review begins with the structural and electronic fundamentals of g-C3N4, followed by a mechanistic overview of photocatalytic water splitting, including light absorption, charge generation, interfacial charge separation, and surface redox reactions. Various heterojunction architectures: Type I, Type II, Z-scheme, S-scheme, p-n junctions, Schottky interfaces, and multicomponent systems are systematically discussed, with particular emphasis on interfacial charge-transfer mechanisms revealed by first-principles calculations. A key contribution of this review is the consolidation of DFT-based modeling protocols and descriptors essential for evaluating and enhancing photocatalytic performance. These include structural stability (lattice mismatch, phonon dispersion, and ab initio molecular dynamics), electronic properties (band structure, density of states, and band-edge alignment), charge-transfer characteristics (work function, charge density difference, planar-averaged charge density, and Bader charge analysis), optical properties (absorption spectra, dielectric function, optical band gap, and solar-to-hydrogen efficiency), and carrier transport descriptors (effective mass and carrier mobility). In addition, DFT-based reaction pathway analysis is discussed to elucidate hydrogen and oxygen evolution mechanisms at heterojunction interfaces. Importantly, this review highlights computational strategies to enhance photocatalytic activity, including strain engineering, external electric fields, defect and dopant engineering, and interface optimization, providing clear guidance on how these approaches can be implemented and interpreted within a DFT framework. Challenges related to computational cost, finite-size effects, and realistic interface construction are critically evaluated, along with practical mitigation strategies. Emerging directions such as beyond-DFT methods (GW and TDDFT), machine learning, and high-throughput screening are also discussed as powerful tools for accelerating heterojunction discovery. By integrating model construction principles, computational descriptors, and activity-enhancement strategies into a unified roadmap, this review serves as a practical guide for researchers seeking to design, model, and optimize g-C3N4-based heterojunction photocatalysts using DFT, thereby bridging the gap between theoretical modeling and experimental realization of efficient, stable, and scalable systems for solar-driven hydrogen production.
Local-hybrid (LH) density functionals admix exact exchange locally in real space and thereby can be powerful tools to ameliorate the usual zero-sum game between reducing self-interaction errors and modeling static correlation. But as with other hybrid functionals the practical use of LHs for large systems is limited by the cost of evaluating exact-exchange quantities self-consistently. Here we introduce an energy-corrected local-hybrid framework, EC(LH)@(m)GGA, in which a computationally expedient semi-local reference density is used for a single post-SCF evaluation with an advanced LH. Using the recent neural-network-based LH25nP LH as a prototype, we show that the EC route based on GGA or meta-GGA orbitals preserves the characteristic accuracy profile of the parent local hybrid and reaches state-of-the-art rung 4 performance on the GMTKN55 test suite (WTMAD-2 around 2.4-2.7 kcal/mol, depending on grid). The top performance of LH25nP for spin-restricted bond dissociation as a strong-correlation measure is retained in this framework. The dominant post-SCF overhead in timing is governed by the EC grid. For the practical gridsize 3, the total EC(LH)@(m)GGA cost is only about ~2-3× that of a GGA single point, typically about an order of magnitude less than a full LH SCF. Overall, EC(LH)@(m)GGA provides a simple post-SCF route to state-of-the-art rung-4 energetics at a cost close to semi-local DFT, applicable to large systems.
Membranes are usually treated as passive containers in origin-of-life models, confining prebiotic chemistry, buffering diffusion, and sustaining gradients. Here, we argue that early membranes provided the physical substrate for candidate informational bodies: boundaries whose physical properties can stabilize, store, and select patterns of interaction with the environment. Within the Membrane Information Organization (MIO) framework, we place protocells in a low-dimensional organizational space defined by a triplet of body parameters: boundary strength, memory, and identity persistence. These parameters quantify sustained inside-outside asymmetry, history dependence beyond instantaneous forcing, and the reproducibility of organizational types across growth-division cycles. In this view, the origin of life is cast as a transition in which amphiphile-based vesicles cross jointly calibrated thresholds in boundary strength, memory, and identity, thereby becoming candidate selectable units prior to fully genetic inheritance. We outline how these quantities can be estimated in fatty-acid and hybrid protocells using microfluidic driving, Laurdan-based lipid-order imaging, curvature reconstruction, and fluorescent internal reporters, and how they can be incorporated into computational models of protocell ecologies. This boundary-centered perspective complements RNA-world, metabolism-first, and autonomy-based scenarios by specifying membrane-level conditions under which replicators, reaction networks, and proto-metabolisms can become evolutionarily stable. The result is not a new chemistry, but a concrete measurement language for identifying when a membrane ceases to be "just a vesicle" and enters a candidate minimal evolvable informational-body regime.
In this work, two computational approaches for metabolite quantification in serum samples using 1H NMR spectroscopy were evaluated: the spectral matching method (MSM) implemented in MagMet and the non-linear least squares method (MNLLS) implemented in Chenomx. The comparison focused on their underlying methodologies, including deconvolution algorithms and user workflows, to assess their relative performance and suitability for metabolomics data analysis. As various analyses (e.g. pattern recognition, classification, biomarker discovery, and pathway analysis) rely on the precision and consistency of input features (e.g., metabolite concentrations), selecting a robust quantification method is essential. Variability in quantification can introduce noise and impact the stability and comparability of analytical outputs. To validate performance, MSM (MagMet) and MNLLS (Chenomx) were benchmarked against quantitative NMR (qNMR) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), the latter serving as the primary reference due to its high sensitivity and broad metabolite coverage (Gika et al., 2014) [1]. Although LC-MS/MS may be affected by matrix effects and ion suppression; these factors are well characterized and routinely mitigated through isotope-labeled internal standards and validated analytical workflows. Moreover, LC-MS offers substantially higher sensitivity than NMR, typically by two to three orders of magnitude, enabling the detection and quantification of hundreds to thousands of metabolites within a single analysis (Nagana Gowda and Raftery, 2022) [2]. qNMR was included as a complementary technique to provide orthogonal validation rather than serving as the sole benchmark. Ten independent serum control samples from a healthy reference group were analyzed to account for natural biological variability, enhancing the generalizability of the findings. The comparison was structured around four criteria: (i) quantitative performance, (ii) computational stability, (iii) usability and processing time, and (iv) method-based similarity via partial least squares-discriminant analysis (PLS-DA). This work differs from prior studies by integrating statistical validation, repeatability testing, and practical usability assessment, and by benchmarking computational quantification pipelines against experimentally grounded methods such as qNMR and LC-MS/MS [3-5]. The selected approach is expected to demonstrate improved consistency in quantification relative to the alternative, contributing to more reliable biological interpretations and more reproducible analytical outcomes across datasets.
The glycine N-methyltransferase (GNMT) reaction was examined using an integrated workflow combining molecular dynamics (MD), quantum mechanical (QM) cluster calculations, and machine learning (ML) analysis. Instead of relying on a single crystal-like conformation, multiple MD simulations were used to sample diverse reactant (SAM + glycine bound to GNMT) and product (SAH + sarcosine bound to GNMT) state geometries for QM cluster modeling. Across more than 150 QM-cluster models constructed by the Residue Interaction Network ResidUe Selector (RINRUS) from selected MD frames with and without explicit waters, the computed activation and reaction free energies span broad ranges (7 to 25 kcal mol-1 and -36 to +3 kcal mol-1), demonstrating a strong dependence on the initial MD conformation. Product-state consistently yields lower reaction barriers, while explicit water introduces only small shifts in energetics and preserves the relative ordering among frames. The two-coordinate potential energy surface (PES) offers only limited insight and cannot fully account for the observed energetic variability. These QM-cluster models were further analyzed using machine-learning methods to identify structural descriptors that correlate with the observed energy variations and provide insight into their structural origin. ML models trained on multiple feature representations show that the donor-methyl-acceptor distances are the most informative and yield the strongest predictive accuracy, while higher dimensional solvent- or residue-based features contribute comparatively little. Overall, the results highlight the importance of conformational sampling for reliable QM-cluster energetics and point toward more expressive structure-to-property representations for analyzing enzymatic reactions.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive impairment and neuronal loss. Aberrant activation of receptor-interacting protein kinase 1 (RIPK1) plays a critical role in neuroinflammation and programmed neuronal death, making it an attractive therapeutic target. In this computational study, 16 isoxazolidine derivatives (1-16) were evaluated alongside seven reference inhibitors to identify potential RIPK1 blockers. Molecular docking analyses revealed that compound 7 exhibited the highest binding affinity toward RIPK1 (PDB ID: 7XMK), with a binding energy of -9.0 kcal mol-1, outperforming established inhibitors and demonstrating broad activity against AD-related targets. Density functional theory calculations showed a HOMO-LUMO energy gap of 5.209 eV, indicating favorable electronic stability. Compound 7 complied with Lipinski's rule of five and Veber's criteria and displayed excellent predictive ADMET properties, including high human intestinal absorption (HIA = 1.0), strong blood-brain barrier permeability (BBB = 0.991), and low predicted toxicity. Molecular dynamics (MD) simulations conducted over 100 ns at temperatures ranging from 300 to 320 K confirmed the stability of the RIPK1-compound 7 complex. The root-mean-square deviation (RMSD) values ranged from 5.2 to 14.0 Å (0.52-1.40 nm), indicating acceptable structural fluctuations throughout the simulation. Additionally, the radius of gyration (Rg) ranged from 2.8 to 3.8 nm, indicating that the complex maintained a relatively stable, compact conformation throughout the simulation. Principal component analysis further supported these findings, yielding cosine similarity values of 0.86-0.95. Collectively, these results highlight compound 7 as a promising RIPK1 inhibitor with favorable pharmacokinetic, electronic, and dynamic properties, underscoring its potential as a therapeutic candidate for AD.
Anion binding to nanographenes is governed by noncovalent interactions, particularly anion-π interactions in electron-deficient aromatic regions and CH---anion hydrogen bonding in electron-rich domains. These interactions are primarily driven by electrostatic effects, with the quadrupole moment of the aromatic system playing a central role in determining the strength and directionality of anion-π binding. The perpendicular component of the quadrupole moment (Qzz) correlates with binding energies for both anion-π and CH---anion interactions, though polycyclic systems present challenges due to competing interaction modes. In this study, we investigate the role of the local quadrupole moment in anion binding across 171 Cl--aromatic complexes, comparing various descriptors including aromaticity indices, Fukui functions, and electron density at ring critical points. We find that electrostatic descriptors, particularly the local quadrupole moment, provide a more consistent and robust explanation for binding energies than conventional descriptors. Specifically, two geometric descriptors derived from the local quadrupole moment-the scale factor ( S R max $$ {S}_R^{max} $$ ) and the ellipticity ( e c ' $$ {e}_c^{\prime } $$ )-show good correlation with binding strength, with S R max $$ {S}_R^{max} $$ reflecting π-acidity and ellipticity quantifying charge distribution anisotropy. These descriptors are validated across fluorinated naphthalenes and nanographenes, demonstrating their general applicability. Regression models based on S R max $$ {S}_R^{max} $$ and e c ' $$ {e}_c^{\prime } $$ effectively predict binding energies, with enhanced accuracy when combined with polarization-dependent penalty functions, especially for larger nanographene systems. While the predictive model is still somewhat constrained by polarization effects, its simplicity, robustness, and transferability across a wide range of systems offer distinct advantages over more complex, multilayered machine learning models. These results underscore the critical role of quadrupole moment anisotropy in anion-π interactions and offer a practical framework for predicting anion binding affinities and designing π-acidic receptors.