Journal of Computer-Aided Molecular Design最新文献

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/.

This study aimed to reveal interactions of the stress response sigma subunits (factors) σ^D and σ^H of RNA polymerase and promoters in Gram-positive bacterium Corynebacterium glutamicum by combining wet-lab obtained data and in silico modeling. Computer modeling-guided point mutagenesis of C. glutamicum σ^H subunit led to the creation of a panel of σ^H variants. Their ability to initiate transcription from naturally occurring hybrid σ^D/σ^H-dependent promoter Pcg0441 and two control canonical promoters (σ^D-dependent PrsdA and σ^H-dependent PuvrD3) was measured and interpreted using molecular dynamics simulations of homology models of all complexes. The results led us to design the artificial hybrid promoter PD₃₅H₁₀ combining the −10 element of the PuvrD3 promoter and the −35 element of the PrsdA promoter. This artificial hybrid promoter PD_35-rsdAH_10-uvrD3 showed almost optimal properties needed for the bio-orthogonal transcription (not interfering with the native biological processes).

The influence of polar, water-miscible organic solvents (POS) on protein structure, stability, and functional activity is a subject of significant interest and complexity. This study examines the effects of acetonitrile (ACN), a semipolar, aprotic solvent, on the solvation properties of blocked Ace-Gly-X-Gly-Nme tripeptides (where Ace and Nme stands for acetyl and N-methyl amide groups respectively and X is any amino acid) through extensive molecular dynamics simulations. Individual simulations were conducted for each peptide, encompassing five different ACN concentrations within the range of χ_ACN = 0.1–0.9. The preferential solvation parameter (Γ) calculated using the Kirkwood-Buff integral method was used for the assessment of peptide interactions with water/ACN. Additionally, weighted Voronoi tessellation was applied to obtain a three-way data set containing four time-averaged contact surface area types between peptide atoms and water/ACN atoms. A mathematical technique known as N-way Partial Least Squares (NPLS) was utilized to anticipate the preferential interactions between peptides and water/ACN from the contact surface areas. Furthermore, the temperature dependency of peptide-solvent interactions was investigated using a subset of 10 amino acids representing a range of hydrophobicities. MD simulations were conducted at five temperatures, spanning from 283 to 343 K, with subsequent analysis of data focusing on both preferential solvation and peptide-solvent contact surface areas. The results demonstrate the efficacy of utilizing contact surface areas between the peptide and solvent constituents for successfully predicting preferential interactions in water/ACN mixtures across various ACN concentrations and temperatures.

PIK3CA gene encoding PI3K p110α is one of the most frequently mutated and overexpressed in majority of human cancers. Development of potent and selective novel inhibitors targeting PI3Kα was considered as the most promising approaches for cancer treatment. In this investigation, a virtual screening platform for PI3Kα inhibitors was established by employing machine learning methods, pharmacophore modeling, and molecular docking approaches. 28 potential PI3Kα inhibitors with different scaffolds were selected from the databases with 295,024 compounds. Among the 28 hits, hit15 exhibited the best inhibitory effect against PI3Kα with IC₅₀ value less than 1.0 µM. The molecular dynamics simulation indicated that hit15 could stably bind to the active site of PI3Kα, interact with some residues by hydrophobic, electrostatic and hydrogen bonding interactions, and finally induced PI3Kα active pocket substantial conformation changes. Stable H-bond interactions were formed between hit15 and residues of Lys776, Asp810 and Asp933. The binding free energy of PI3Kα-hit15 was − 65.3 kJ/mol. The free energy decomposition indicated that key residues of Asp805, Ile848 and Ile932 contributed stronger energies to the binding free energy. The above results indicated that hit15 with novel scaffold was a potent PI3Kα inhibitor and considered as a promising candidate for further drug development to treat various cancers with PI3Kα over activated.

Graphical Abstract

Relative solubilities, i.e. whether a given molecule is more soluble in one solvent compared to others, is a critical parameter for pharmaceutical and agricultural formulation development and chemical synthesis, material science, and environmental chemistry. In silico predictions of this crucial variable can help reducing experiments, waste of solvents and synthesis optimization. In this study, we evaluate the performance of different physics-based methods for predicting relative solubilities. Our assessment involves quantum mechanics-based COSMO-RS and molecular dynamics-based free energy methods using OPLS4, the open-source OpenFF Sage, and GAFF force fields, spanning over 200 solvent–solute combinations. Our investigation highlights the important role of compound multimerization, an effect which must be accounted for to obtain accurate relative solubility predictions. The performance landscape of these methods is varied, with significant differences in precision depending on both the method used and the solute considered, thereby offering an improved understanding of the predictive power of physics-based methods in chemical research.