Journal of molecular graphics & modelling最新文献

The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML) models for the classification of bioactivity mechanisms against the (5HT)-2A receptor. Employing neural networks and XGBoost models, we achieved an overall accuracy of around 87 %, which was further enhanced through molecular modelling (MM) (e.g. molecular dynamics simulations) and binding free energy analysis. This ML-MM integration provided insights into the mechanisms of direct modulators and prodrugs. A significant outcome of the current study is the development of a ‘binding free energy fingerprint’ specific to (5HT)-2A modulators, offering a novel metric for evaluating drug efficacy against this target. Our study demonstrates the prospective of employing a successful workflow combining AI with structural biology, offering a powerful tool for advancing psychoactive drug discovery.

Deep learning is playing an increasingly important role in accurate prediction of molecular properties. Prior to being processed by a deep learning model, a molecule is typically represented in the form of a text or a graph. While some methods attempt to integrate these two forms of molecular representations, the misalignment of graph and text embeddings presents a significant challenge to fuse two modalities. To solve this problem, we propose a method that aligns and fuses graph and text features in the embedding space by using contrastive loss and cross attentions. Additionally, we enhance the molecular representation by incorporating multi-granularity information of molecules on the levels of atoms, functional groups, and molecules. Extensive experiments show that our model outperforms state-of-the-art models in downstream tasks of molecular property prediction, achieving superior performance with less pretraining data. The source codes and data are available at https://github.com/zzr624663649/multimodal_molecular_property.

Many protein-ionic liquid investigations have examined laccase interactions. Laccases are a class of poly-copper oxidoreductases that retain significant biotechnological relevance owing to their notable oxidative capabilities and their application in the elimination of synthetic dyes, phenolic compounds, insecticides, and various other substances. This study investigates the impact of surface active ionic liquids (SAILs), namely, decyltrimethylammonium bromide [N₁₀₁₁₁][Br] and 1-decyl-3-methylimidazolium chloride [C_10mim][Cl] as cationic surfactant ionic liquids and cholinium decanoate [Chl][Dec], an anionic surfactant ionic liquid, on the structure and function of laccase from the fungus Trametes versicolor (TvL) by the molecular dynamics (MD) simulation method. In summary, this study showed that laccase solvent-accessible surface area increased in the ionic liquid [Chl][Dec] while it decreased in the other two ionic liquids. Interestingly, [Chl][Dec] ionic liquid components formed hydrogen bonds with laccase, while [N₁₀₁₁₁][Br] and [C_10mim][Cl] components were unable to form hydrogen bonds with laccase. The quantity of hydrogen bonds formed between water molecules and the enzyme was also diminished in the presence of [Chl][Dec] in comparison to the other two ionic liquids. especially at a concentration of 250 mM. In 250 mM concentrations of [N₁₀₁₁₁][Br] and [C_10mim][Cl], clusters of long-chain cations are likely to form near the copper T1 site. However, even at low [Chl][Dec] concentrations, long [Dec]^- chains were observed to penetrate the enzyme near the copper T1 site, and at 250 mM [Chl][Dec], a large cluster of anions occupied the opening of the active site. The results of the analysis also show that the interaction between the [Dec]^- anion and the enzyme is stronger than the interaction between [N₁₀₁₁₁]⁺ and [C_10mim]⁺ with laccase; in addition, the [Dec]^- anion, compared to [Br]^- and [Cl]^- has a much greater tendency to bind with the enzyme residues.

Aluminum nanosheets are a form of Al nanoparticle that have been recently manufactured on an industrial scale and have a variety of uses. Al nanoparticles are extensively used in a variety of sectors, including aerospace, construction, medical, chemistry, and marine industries. Crack propagation in various constructions must be investigated thoroughly for structural design purposes. Cracks in nanoparticles may occur during the production of nanosheets (NSs) or when different mechanical or thermal pressures were applied. In this work, the effect of a continuous electric field on the fracture formation process of aluminum nanosheets was investigated. For this study, molecular dynamics simulation and LAMMPS software were used. The effects of various electric fields on several parameters, including as stress, velocity (Velo), and fracture length, were explored, and numerical data were retrieved using software. The results show that the amplitude of the electric field parameter affected the atomic development of modeled Al nanosheets throughout the fracture operation. This effect resulted in atomic resonance (amplitude) fluctuations, which affected the mean interatomic forces and led the temporal evolution of atoms to converge to certain specified initial conditions. The crack length in our modeled samples ranged from 22.88 to 32.63 Å, depending on the electric field parameter (0.1–1 V/Å). Finally, it was determined that the crack growth of modeled Al nanosheets may be controlled using CEF parameters in real-world situations.

Multiple myeloma is recognized as the second most common hematological cancer. MafA transcriptional repressor is an established mediator of myelomagenesis. While there are multitude of drugs available for targeting various effectors in multiple myeloma, current literature lacks a candidate RNA based MafA modulator. Thus, using the structure of MafA homodimer-consensus target DNA, a computational effort was implemented to design a novel RNA based chemical modulator against MafA. First, available MafA-consensus DNA structure was employed to generate an RNA library. This library was further subjected to global docking to select the most plausible RNA candidates, preferring to bind DNA binding region of MafA. Following global docking, MD-ready complexes that were prepared via local docking program, were subjected to 500 ns of MD simulations. First, each of these MD simulations were analyzed for relative binding free energy through MM-PBSA method, which pointed towards a strong RNA based MafA binder, RNA1. Second, through a detailed MD analysis, RNA1 was shown to prefer binding to a single monomer of the dimeric DNA binding domain of MafA using higher number of hydrophobic interactions compared with positive control MafA-DNA complex. At the final phase, a principal component analyses was conducted, which led us to identify the actual interaction region of RNA1 and MafA monomer. Overall, to our knowledge, this is the first computational study that presents an RNA molecule capable of potentially targeting MafA protein. Furthermore, limitations of our study together with possible future implications of RNA1 in multiple myeloma were also discussed.

Baicalein, a flavone derived from Scutellaria baicalensis Georgi, exhibits potent anti-inflammatory, antiviral, and anticancer properties. Its derivative, known as 8-bromobaicalein (BB), has been found to have strong cytotoxic effect on MCF-7 human breast cancer cells. However, its limited solubility in water has hindered its potential for wider applications. To address this issue, we investigated the use of cyclodextrins specifically βCD, 2,6-di-O-methyl-β-cyclodextrin (DMβCD), and hydroxypropyl-β-cyclodextrin (HPβCD) to improve the solubility of BB through inclusion complexation. During 250 ns molecular dynamics simulations, it was found that BB can form inclusion complexes with all βCDs. These complexes exhibit two distinct orientations: chromone group insertion (C-form) and phenyl group insertion (P-form). The formation of these complexes is primarily driven by van der Waals interactions. DMβCD has the highest number of atom contacts with BB and the lowest solvent accessibility in the hydrophobic cavity. These results coincide with the highest binding affinity from the MM/GBSA-based free energy calculation method. Experimental phase solubility diagrams revealed a 1:1 stoichiometric ratio (A_L type) between BB and βCDs, in which BB/DMβCD showed the highest stability. The formation of inclusion complexes was confirmed by differential scanning calorimetry and scanning electron microscope methods. Additionally, the BB/DMβCD inclusion complex demonstrated significantly higher anticancer activity against MCF-7 human breast cancer cells compared to BB alone. These findings underscore the potential of DMβCD for formulating BB in pharmaceutical and medical applications.

Understanding the mechanical properties of porous carbon-based materials can lead to advancements in various applications, including energy storage, filtration, and lightweight structural components. Also, investigating how silicon doping affects these materials can help optimize their mechanical properties, potentially improving strength, durability, and other performance metrics. This research investigated the effects of atomic doping (Si particle up to 10 %) on the mechanical properties of the porous carbon matrix using molecular dynamics methods. Young's modulus, ultimate strength, radial distribution function, interaction energy, mean square displacement and potential energy of designed samples were reported. MD outputs predict the Si doping process improved the mechanical performance of porous structures. Numerically, Young's modulus of the C-based porous matrix increased from 234.33 GPa to 363.82 GPa by 5 % Si inserted into a pristine porous sample. Also, the ultimate strength increases from 48.54 to 115.93 GPa with increasing Si doping from 1 % to 5 %. Silicon doping enhances the bonding strength and reduces defects in the carbon matrix, leading to improved stiffness and load-bearing capacity. This results in significant increases in mechanical performance. However, excess Si may disrupt the optimal bonding network, leading to weaker connections within the matrix. Also, considering the negative value of potential energy in different doping percentages, it can be concluded that the amount of doping added up to 10 % does not disturb the initial structure and stability of the system, and the structure still has structural stability. So, we expected our introduced atomic samples to be used in actual applications.

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV), a member of the Orthopoxvirus genus closely related to smallpox. The structure of the A42R profilin-like protein is the first and only available structure among MPXV proteins. Biochemical studies of A42R were conducted in the 1990s and later work also analyzed the protein's function in viral replication in cells. This study aims to screen tripeptides for their potential inhibition of the A42R profilin-like protein using computational methods, with implications for MPXV therapy. A total of 8000 tripeptides underwent molecular docking simulations, resulting in the identification of 20 compounds exhibiting strong binding affinity to A42R. To validate the docking results, molecular dynamics simulations and free energy perturbation calculations were performed. These analyses revealed two tripeptides with sequences TRP-THR-TRP and TRP-TRP-TRP, which displayed robust binding affinity to A42R. Markedly, electrostatic interactions predominated over van der Waals interactions in the binding process between tripeptides and A42R. Three A42R residues, namely Glu9, Ser12, and Arg38, appear to be pivotal in mediating the interaction between A42R and the tripeptide ligands. Notably, tripeptides containing two or three tryptophan residues demonstrate a pronounced binding affinity, with the tripeptide comprising three tryptophan amino acids showing the highest level of affinity. These findings offer valuable insights for the selection of compounds sharing a similar structure and possessing a high affinity for A42R, potentially capable of inhibiting its enzyme activity. The study highlights a structural advantage and paves the way for the development of targeted therapies against MPXV infections.

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression. Despite their relatively short length (about 21 nucleotides), they can regulate thousands of transcripts within a cell. Due to their low complementarity to targets, studying their activity and binding region preferences (3′UTR, 5′UTR, or CDS) is challenging. In this paper, we analyzed a set of human miRNAs to uncover their general patterns. We began with a sequence logo to verify conservation at specific positions. To discover long-range correlations, we employed chaos game representation (CGR) and genomatrix, methods that enable both graphical and analytical analysis of sequence sets and are well-established in bioinformatics. Our results showed that miRNAs exhibit strongly non-random and characteristic patterns. To incorporate physicochemical properties into the analysis, we applied the electron-ion interaction potential (EIIP) parameter. An important part of our study was to validate the division of miRNAs into two parts—seed and puzzle. The seed region is responsible for target binding, while the puzzle region likely interacts with the RISC complex. We estimated duplex binding energy within the 3′UTR, 5′UTR, and CDS regions using the miRanda tool. Based on the median energy distribution, we divided the miRNAs into two subsets, reflecting different patterns in chaos game representation. Interestingly, one subset displayed significant similarity to conserved and highly confidential miRNAs. Our results confirm the low complementarity of miRNA/mRNA interactions and support the functional division of miRNA structure. Additionally, we present findings related to the localization of transcript target sites, which form the basis for further analyses.

In this paper, the geometric analysis of carbon nanotubes (CNTs) without external loading is carried out by energy method. Based on the theory of molecular mechanics, an improved mechanical model is proposed to predict the energy of armchair carbon nanotubes under stress-free conditions, and the diameter of CNTs is estimated according to the principle of minimum energy. The results show that the diameter obtained by the improved model is larger, but basically consistent with that obtained by conformal mapping. The inversion energy term is added to the modified model, and the inversion energy term related to atomic curvature is characterized by the conization angle. It can be seen from the error that the inversion energy of carbon nanotubes can not be neglected in the stress-free state, especially in the case of small diameter. The agglomeration of nanotubes is one of the important factors, which affects the effective elastic modulus of nanocomposites. Here, a new micro-mechanics model consisting of both agglomeration of CNTs and pure matrix is also presented to analyze its effect on the effective elastic modulus. It is noted from the results that the stiffness of nanocomposites is very sensitive to the CNTs agglomeration.