Journal of Computer-Aided Molecular Design最新文献

The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, traditional protein design methods often depend on extensive datasets and detailed structural information, limiting their efficiency and applicability. To overcome these limitations, we propose a deep learning-based approach that directly targets desired binding functions by introducing mutations at selected SpA sites to optimize its properties. Specifically, we present a de novo protein design strategy that integrates a diffusion-based generative model with Kidera factor representations to create SpA variants. The framework comprises three modules: sequence generation, where protein sequences are encoded via Kidera factors and novel variants are generated using a diffusion model; computational screening, employing tools like AlphaFold3 to assess structural properties, solubility, and physicochemical characteristics, thereby selecting high-potential candidates; and experimental validation, involving wet-lab experiments to evaluate the biological activities and binding affinities of the designed proteins. The generated SpA variants demonstrated high success rates and strong binding affinities toward IgG. These findings confirm the effectiveness of our method in producing functional proteins comparable to natural counterparts, offering a scalable and data-efficient alternative to protein engineering.

Herein, we report theoretical investigations of the imidazo[1,2-a]pyridine derivative IPD (systematic name 2-(1-(4-bromophenyl)-5-methyl-1H-1,2,3-triazol-4-yl)imidazo[1,2-a]pyridine), and compare the computational outcome with experimental data available from X-ray crystallography studies and spectroscopic analysis. Density functional theory (DFT) was employed as a computational chemistry approach to optimize the geometry and investigate the electronic properties, molecular descriptors, and frontier molecular orbital features of the investigated compound. The DFT-optimized molecular geometry showed good agreement with the experimental structure determined by single-crystal X-ray diffraction (RMSD = 0.2074 Å). The electrostatic potential map of the IPD molecule revealed potential sites for electrophilic attack at the nitrogen in the imidazole ring and at the nitrogen atoms within the 1,2,3-triazole moiety. Additional calculations, however, indicated a higher proton affinity (246.44 kcal/mol) at the aforementioned nitrogen atom in the imidazo[1,2-a]pyridine ring system, suggesting it is the most likely site of protonation. Molecular docking simulations were conducted to investigate the inclusion of the title compound into β-cyclodextrin and to explore the interactions of the IPD molecule with the epidermal growth factor receptor tyrosine kinase (EGFR-TK) as part of an in silico anticancer study. The electronic structures of the docked complexes were further explored using the DFT method, revealing that the intermolecular interactions between the IPD ligand and the receptors also involved a coupling of frontier molecular orbitals.

Alzheimer’s disease (AD) is a neurodegenerative disease with no cure, with aggregates of amyloid-beta (Aβ) plaques, neurofibrillary tangles, and permanent neurodegeneration. Current therapies have been found to provide complementary effects; therefore, there is a need to establish new therapeutic strategies. The neuroprotective activity of Kalanchoe pinnata (KP) was explored in this study using network pharmacology, molecular docking, and in vitro studies. Bioactive compounds with good pharmacokinetic properties have been identified as the 10 bioactive compounds of KP, such as bryotoxin B, kaempferol, and quercetin. A total of 449 common targets of KP and AD that participate in the PI3K-Akt, MAP, and cAMP signaling pathways were identified (AKT1, TNF, and STAT3). Molecular docking results indicated good binding affinities of these KP compounds with AD-related targets. KP aqueous extract (KPAE) inhibited protrophic cytokines and PI3K/Akt signaling in BV-2 microglial cells in a dose-dependent manner by inhibiting Aβ aggregation, antioxidant activity, and neuroinflammation. The above observations indicate that KP has a multi-target effect against AD, which should be proven by preclinical and clinical trials.

Accurate prediction of a drug molecule’s toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often rely on handcrafted molecular descriptors, which fall short in capturing the intricate structural and chemical nuances of molecules. In this study, we propose ToxiGraphNet, a graph neural network (GNN)—based regression model for predicting the LD50 value—a quantitative measure of acute toxicity-directly from molecular SMILES strings. Using RDKit, molecules are transformed into graph representations where atoms serve as nodes and bonds as edges, each enriched with chemically meaningful features. Atom features encompass atomic type, degree, aromaticity, chirality, and more, while bond features capture bond type, conjugation, and ring status. These molecular graphs are processed via edge-conditioned convolution layers (NNConv) within the PyTorch Geometric framework, enabling dynamic, chemistry-aware feature aggregation. The model architecture includes three NNConv layers with batch normalization, dropout, and a residual connection to ensure stable training. After global mean pooling, the learned graph-level representations are passed through fully connected layers to predict LD50 values. Training on a curated LD50 dataset yielded impressive performance (MSE: 0.3610, MAE: 0.4424, RMSE: 6009, (R^2): 0.5959), demonstrating strong generalization and predictive accuracy. This work highlights the efficacy of GNNs in modeling molecular toxicity without relying on hand-engineered features and presents a scalable solution for property prediction in drug discovery pipelines.

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The µ-opioid receptor (µOR) is one of the most important therapeutic targets for drugs worldwide as it plays a fundamental role in pain modulation. Tramadol interacts effectively with µOR for the treatment of pain, with its metabolite (+)-O-desmethyltramadol (M1) being the main cause of its opioid action. However, the structural and pharmacological differences of M1 with respect to opioids do not allow us to fully understand its functioning in the body. In this work, we contribute to the molecular understanding of the mechanism of action of M1. We conduct an exhaustive computational study that integrates molecular docking and molecular dynamics simulations of the µOR-M1 complex. To achieve a comprehensive analysis, we consider eight different conformations for M1, two chair-type and six twisted boat-type. Our study suggests interconversion from twisted boat-type to chair-type conformations and the factors that drive this interconversion, which are, ligand fluctuations, lack of intramolecular bonds, the effect of solvation and conformational energy barriers. We also conclude that to perform protein-ligand molecular modeling it is necessary to use several techniques to achieve reliable results as in our case. These findings contribute to the design of more effective chemical analogues of tramadol.

New porphyrin dyes employing alkoxysilyl anchoring groups were designed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT) for possible use in dye-sensitized solar cells. The new dyes, named SiA series, demonstrated more favorable charge transfer and enhanced light-harvesting efficiency (LHE) compared with SM315 dye, which utilized the conventional cyanoacrylic acid anchoring unit. Among the designed dyes, the SiA-2 displayed the superior light-harvesting properties as shown by the broadened and bathochromically shifted Q-band, most favorable LHE curve, as well as the highest calculated theoretical maximum photocurrent density (({J}_{text{SC}}^{text{max}})). The SiA-2 dye also showcased the most enhanced charge transfer properties based on the calculated transferred charges (({q}^{CT})), charge-transfer distance (({D}^{CT})), and change in dipole moment accompanying intermolecular charge-transfer (({mu }^{CT})). Moreover, the spatial separation distance (r) between the photogenerated hole center and the surface of the TiO₂ semiconductor of SiA-2 suggests a favorable ({V}_{text{OC}}). In this series, SiA-2 emerges as the most promising sensitizer due to its favorable overall characteristics.

Twenty-three 4-anilinoquinazoline derivatives were successfully synthesized, including six new compounds (8, 9, 12, 17, 19, 20) and seventeen known compounds. Seventeen derivatives (10–26) were evaluated for cytotoxic activity against three cancer cell lines (A549, HepG2, and SH-SY5Y) using the MTT assay. The results showed that compound 13 exhibited high selectivity toward the SH-SY5Y cell line with an IC₅₀ value of 13.1 µM, while compound 26 displayed good inhibition against A549 and SH-SY5Y with IC₅₀ values of 24.1 and 14.8 µM, respectively. The ADME analysis further indicated that compounds 13 and 26 possess favorable drug-like and pharmacokinetic properties, supporting their potential suitability for further investigation. Furthermore, molecular docking and molecular dynamics simulations were performed on these two compounds (13 and 26) targeting phosphoglycerate dehydrogenase (PHGDH). The docking results revealed that the fluorine atom exhibited halogen interactions with Tyr173, and the –NH group formed hydrogen bonds with Asp174. Additional hydrogen bond interactions were observed for the nitro group of compound 13 with Gly156 and for the amine group of compound 26 with Leu152. Other interactions were dominated by van der Waals, π–π, π–sigma, alkyl, and π–alkyl contacts with the aromatic N-anilinoquinazoline scaffold. The molecular dynamics simulation demonstrated consistent RMSD, Rg, RMSF, hydrogen bond, and binding energy profiles, confirming the stability and reliability of the PHGDH–ligand complexes in aqueous solution. Notably, compound 13 maintained more persistent hydrogen bonding interactions and induced localized flexibility around the active site compared to compound 26.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a complex pathogenesis and limited treatment options. Recently, necroptosis has been found to play a significant role in UC. This study aimed to investigate necroptosis-related mechanisms and hub targets in UC, and to screen natural potential inhibitors. Firstly, transcriptomic and single-cell analyses were used to explore the molecular and cellular mechanisms of necroptosis in UC and identify hub targets. Subsequently, virtual screening and molecular dynamics were performed. The results indicated that twenty-three necroptosis-related differentially expressed genes (DEGs) were predicted as diagnostic biomarkers in the best machine-learning model (GBM). Furthermore, four hub targets (IL1B, MLKL, STAT1, and BIRC3) were computationally prioritized and their overexpression might promote pro-inflammatory activity (neutrophils/M1 macrophages) while suppressing anti-inflammatory responses (Tregs/M2 macrophages), aggravating UC progression. Single-cell analysis revealed reduced epithelial cells and increased fibroblasts, endothelial cells, and immune cells in UC tissues, suggesting disruption of the intestinal epithelial barrier, exacerbation of fibrosis, and activation of the immune system. The high abundance of endothelial cells and monocytes expressing necroptosis-related DEGs suggested the important role of necroptosis in UC. Moreover, eight natural products were screened with strong binding affinity to MLKL, whose motion trajectories and energy trajectories reached equilibrium within 10 ns. Among them, the potential of trifolirhizin and curcumin as natural inhibitors was particularly prominent. Conclusively, this study computationally predicts four hub DEGs and eight potential natural necroptosis inhibitors, may provide a basis for future therapeutic exploration.

Microtubules are crucial components of the mitotic spindle, essential in chromosome segregation during cell division. EG5, a kinesin motor protein, has emerged as a critical player in this process by promoting the separation of sister chromatids. Dysregulation of EG5 function is associated with tumorigenesis, making it a promising target for cancer therapeutics. Hence, understanding EG5’s molecular mechanisms is a key to developing better therapies with fewer side effects. Here, we investigate the mechanisms by which EG5 interacts with microtubules and how this interaction enhances its motor activity. Utilizing computational methods, we probe the role of microtubule binding in the allosteric regulation of EG5 dynamics. Our results demonstrate that microtubule binding significantly enhances EG5’s dynamic flexibility and motor activity, while inhibitors targeting distinct allosteric sites disrupt this interaction. These insights provide a molecular framework for the rational design of EG5-targeted inhibitors, with potential implications for anticancer drug development.

Drug discovery relies on the ability to predict drug-target affinity (DTA), which allows for the efficient identification of drug candidates for certain protein targets. Scalability, accuracy, and interpretability are issues that traditional methods must deal with. In order to improve prediction accuracy, this study proposes a sophisticated approach that combines contact map representations with the Triple Pre-Activated Random Residual Planet Convolution Attention Network (Tri-Pre-A2RP-2CAN). The DTA, KIBA, and Davis datasets are the sources of the input data. Preprocessing employs Focal Vision Transformer with a Gabor Filter for feature enhancement. Feature extraction uses a Dual-Aggregation Transformer (DAT) to capture complex molecular and protein patterns. The modeling framework incorporates Tri-Pre-A2RP-2CAN and RCNN, optimized with PACRTAMN architecture and Planet optimization based hyperparameter tuning. This innovative approach achieves 99.9% accuracy, outperforming existing methods in modeling drug-target interactions. It enhances DTA prediction, improves molecular interaction analysis, and optimizes drug discovery processes, offering scalable and interpretable solutions for pharmaceutical advancements.