Journal of Computer-Aided Molecular Design最新文献

Gallic acid (GA), a naturally occurring compound with antioxidant, anti-inflammatory, anti-apoptotic, and regenerative properties, has gained attention for its potential protective role against kidney dysfunction and diseases, though its therapeutic efficacy in this context remains underexplored. The primary objective of this study was to explore the therapeutic effects of GA in treating adenine-induced chronic kidney disease (CKD) in male Wistar rats. The study evaluated GA’s therapeutic potential against CKD, along with its pharmacokinetic and drug-likeness properties through a comprehensive analysis. It also assessed GA’s inhibitory effects on key kidney proteins, KIM-1 and NGAL, using gene expression analysis, molecular docking, and molecular dynamics simulations. The results demonstrated a range of positive effects, including significant improvement in adenine-induced kidney damage, as shown by changes in urine and serum markers, as well as oxidative stress biomarkers, following GA treatment. The study revealed that GA effectively suppresses the adenine-induced gene expression of KIM-1 and NGAL. Furthermore, GA adhered to Lipinski’s Rule of Five, and molecular docking analysis indicated strong interactions and low binding energies between GA and the target proteins KIM-1 and NGAL, further supporting its efficacy in targeting these markers. Additionally, 100 ns molecular dynamics simulations showed that gallic acid has a stronger binding affinity for NGAL than for KIM-1, with higher binding energy, stability, and stronger hydrogen bonds, suggesting that it primarily influences NGAL interactions. This study underscores gallic acid’s potential in reducing adenine-induced kidney damage and improving kidney function, with computational evidence supporting its promise as a treatment for CKD.

One of the most widespread diseases recognized all over the world is diabetes, accounting for 1.5 million deaths each year. Recent studies have demonstrated benzimidazole derivatives as potential antidiabetic agents. Hence, the present study is focused on designing new derivatives of 2-mercaptobenzimidazole by C-S cross-coupling reaction and are subjected to computational screening to identify the most promising candidate. Molecular docking and MM-GBSA calculations were performed to ascertain the binding potential with different antidiabetic targets, including α-glucosidase, PPaR-γ, DPP-4, and AMPK. We observed somewhat moderate binding interactions of the synthesized compound against the α-glucosidase. Since binding affinities can be improved using synthetic chemistry approaches, synthesis of analogues (A-18a-c) by designing hybrids at sites such as the acidic functionality of A-18 was done. The analogue A-18a, with p-fluorobenzyl substitution, exhibited enhanced binding affinity (-4.339 Kcal/mol) with the α-glucosidase compared to the parent compound (-3.827 Kcal/mol). The synthesized analogues were also subjected to an in-vitro α-glucosidase inhibitory assay. Among them, A-18a exhibited the most significant inhibitory potential, with an IC₅₀ value of 0.521 ± 0.01 µM as compared to the standard drug Acarbose (IC₅₀ 21.0 ± 0.5 µM). This aligns with the computational study findings, where A-18a exhibited stronger binding interactions within the active site of the enzyme. Hence, a promising analogue of the designed compound was synthesized through a computationally guided approach as an anti-hyperglycaemic agent. Additionally, most of the designed compounds showed significantly greater binding affinity with PPaR-γ as compared to the standard pioglitazone. A-18 was successfully synthesized by S-arylation reaction using CuI in 89% yield and was subjected to MD-simulation against PPaR-γ, which revealed stable binding throughout the 200 ns run. Future studies will focus on exploring the activity of the designed drugs against PPaR-γ through in-vitro and in-vivo assays.

Graphical Abstract

Graphical depiction of flow of study starting from drug designing and followed by the prediction of molecular targets, ligand binding and molecular dynamics.

Parkinson’s disease (PD) is a progressive neurological disorder with an increasing prevalence in aging populations. Identifying effective therapeutic targets and treatments remains a critical challenge. This study aimed to discover potential therapeutic targets and design novel compounds for PD treatment. Gene expression analysis was conducted using diverse datasets, including microarray, mRNA sequencing, and miRNA sequencing. While no common genes were identified across all datasets, the RNA-seq dataset GSE-135036 was prioritized. The investigation focused on downregulated miRNAs targeting upregulated mRNAs, revealing that hsa-mir-5585 regulates Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) within the Shigellosis pathway. Given RIPK1’s role in cell death and inflammation, it emerged as a promising therapeutic target for PD. To identify RIPK1 inhibitors, 67 compounds were screened via molecular docking, with CHEMBL-3109201 exhibiting the highest binding affinity. A structurally similar compound, CHEMBL-76328382, also demonstrated strong interactions. A fragment-based drug design approach generated two novel compounds, BI-1215 and BI-146, which, along with RIPK1-IN-4 and CHEMBL-70909876, were shortlisted based on docking scores and ADME profiles. Molecular dynamics simulations confirmed the stability of CHEMBL-70909876 and BI-1215, with RMSD fluctuations between 0.005 and 0.2 nm. MM-PBSA analysis further validated their superior thermodynamic stability and binding affinity compared to other candidates. This study offers novel insights into PD pathogenesis and potential therapeutic interventions, marking a significant step toward effective treatment strategies for this debilitating disorder.

The COVID-19 pandemic, caused by SARS-CoV-2, has underscored the urgent need for effective antiviral therapies, particularly against vaccine-resistant variants. This study investigates natural xanthone derivatives as potential inhibitors of the ACE2 receptor, a critical entry point for the virus. We computationally evaluated 91 xanthone compounds derived from Swertia chirayita, identifying two promising candidates: 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1,7-dihydroxy-3-methoxy xanthone (XAN71) and 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1-hydroxy-3,7-dimethoxy-xanthone (XAN72). Molecular docking and dynamics simulations (MDDS) were performed to assess their binding energy and stability within the ACE2 active site, comparing them to the reference inhibitor MLN-4067. The top six compounds were selected based on their docking performance, followed by Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to quantify binding affinities. Additionally, molecular electrostatic potential (MEP) analysis was conducted to visualize electron density regions relevant to binding interactions. Our results demonstrate that XAN71 and XAN72 exhibit superior binding affinities of -70.97 and − 69.85 kcal/mol, respectively, outperforming MLN-4067 (-61.33 kcal/mol). MD simulations revealed stable interactions with key ACE2 residues, primarily through hydrogen bonds and hydrophobic contacts. The Molecular Electrostatic Potential(MEP) analysis further elucidated critical electron density regions that enhance binding stability. This study establishes XAN71 and XAN72 as viable candidates for ACE2 inhibition, providing a structural basis for their development as natural xanthone-based therapeutics against SARS-CoV-2. These findings highlight the potential of targeting ACE2 with natural compounds to combat COVID-19, particularly in light of emerging viral variants.

Ceftaroline (CFT) effectively combats methicillin-resistant Staphylococcus aureus (MRSA) by binding to the allosteric site on penicillin-binding protein 2a (PBP2a) and activating allosteric signals that remotely open the active pocket. However, the widespread clinical use of CFT has led to specific mutations, such as N146K/E150K, at the PBP2a allosteric site, which confers resistance to CFT in MRSA by disrupting the transmission of allosteric signals. Herein, computational simulations were employed to elucidate how the mutations disrupt the transmission of allosteric signals, thereby enhancing the resistance of MRSA to CFT. Specifically, the mutations alter the salt bridge network and electrostatic environment, resulting in a dynamic setting and decreased binding affinity of CFT within the allosteric pocket. Additionally, dynamical network analysis and the identification of allosteric pathways revealed that the reduced binding affinity diminishes the propagation of allosteric signals to the active site. Further evaluations demonstrated that this diminished signaling reduces the openness of the active pocket in the mutant systems, with “gatekeeper” residues and functional loops remaining partially closed. Redocking experiments confirmed that mutations lead to decreased docking scores and unfavorable docking poses for CFT within the active pocket. These findings highlight the complex interactions between structural changes induced by mutations and antibiotic resistance, providing crucial insights for developing new therapeutic strategies against MRSA resistance.

Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (GK), a key enzyme in glucose homeostasis, primarily regulates blood glucose levels in hepatocytes and pancreatic cells. Unlike other hexokinases, GK exhibits unique kinetic properties, such as a high Km and lack of feedback inhibition, allowing it to function as a glucose sensor Glucokinase activators (GKAs) have emerged as promising candidates for managing type-2 diabetes by allosterically enhancing GK activity. Despite initial promise, existing GKAs face significant safety concerns, driving the need for compounds with improved safety profiles. This study introduces a novel chemical scaffold within the GKA landscape: peptide-based GKAs incorporating non-standard amino acid residues such as α,β-dehydrophenylalanine (ΔPhe/ΔF). A virtual library containing 3,368,000 peptides was constructed and screened using a hybrid pharmacophore, namely DHRR (D: donor; H: hydrogen; R: aromatic ring). Molecular docking and molecular dynamics simulations assisted in identifying three peptides, Pep-11, Pep-15, and Pep-16, which depicted stable binding at the allosteric site of Glucokinase. These peptides were synthesized using a combination of solid and solution phase synthesis methods. In vitro enzymatic activity of glucokinase was increased by at least 1.5 times in the presence of these peptides. Several machine learning algorithms were explored as alternatives to conventional in-silico methods for predicting GK activity. Regression and tree-based algorithms outperformed other methods, with the logistic regression and random forest classifiers both achieving an ROC-AUC of 0.98.

Conotoxins, being small disulfide-rich and bioactive peptides, manifest notable pharmacological potential and find extensive applications. However, the exploration of conotoxins’ vast molecular space using traditional methods is severely limited, necessitating the urgent need of developing novel approaches. Recently, deep learning (DL)-based methods have advanced to the molecular generation of proteins and peptides. Nevertheless, the limited data and the intricate structure of conotoxins constrain the application of deep learning models in the generation of conotoxins. We propose ConoDL, a framework for the generation and prediction of conotoxins, comprising the end-to-end conotoxin generation model (ConoGen) and the conotoxin prediction model (ConoPred). ConoGen employs transfer learning and a large language model (LLM) to tackle the challenges in conotoxin generation. Meanwhile, ConoPred filters artificial conotoxins generated by ConoGen, narrowing down the scope for subsequent research. A comprehensive evaluation of the peptide properties at both sequence and structure levels indicates that the artificial conotoxins generated by ConoDL exhibit a certain degree of similarity to natural conotoxins. Furthermore, ConoDL has generated artificial conotoxins with novel cysteine scaffolds. Therefore, ConoDL may uncover new cysteine scaffolds and conotoxin molecules, facilitating further exploration of the molecular space of conotoxins and the discovery of pharmacologically active variants.

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-based approaches. Although various tools and packages exist to apply GNNs in molecular ML, a new GNN package, named MolGraph, was developed in this work with the motivation to create GNN model pipelines highly compatible with the TensorFlow and Keras application programming interface (API). MolGraph also implements a module to accommodate the generation of small molecular graphs, which can be passed to a GNN algorithm to solve a molecular ML problem. To validate the GNNs, benchmarking was conducted using the datasets from MoleculeNet, as well as three chromatographic retention time datasets. The benchmarking results demonstrate that the GNNs performed in line with expectations. Additionally, the GNNs proved useful for molecular identification and improved interpretability of chromatographic retention time data. MolGraph is available at https://github.com/akensert/molgraph. Installation, tutorials and implementation details can be found at https://molgraph.readthedocs.io/en/latest/.