On the molecular dynamics of the chemo-mechanical conversion in muscle contraction.
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Actins↗
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Molecular dynamics in the solid state. A dynamic model of the low-spin iron(III) to high-spin iron(III) transformation in P450 enzymes.
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Binding Sites↗
A 1H nuclear-magnetic-resonance study of the conformation and the molecular dynamics of the glycoprotein cow-colostrum trypsin inhibitor.
The glycoprotein cow colostrum trypsin inhibitor was investigated by high resolution 1H nuclear magnetic resonance (NMR) at 360 MHz and, on the basis of the NMR data, compared with the basic pancreatic trypsin inhibitor (Kunitz) from bovine tissue. Detailed studies of the chemical shifts and the exchange kinetics of the labile protons indicated extensive homologies between the spatial structures of the polypeptide chains in the two compounds. This was further corroborated by comparison of the NMR spectral features and the dynamic properties of the aromatic amino acid residues in the two inhibitors. It thus appears that the covalently attached carbohydrate moiety in the colostrum inhibitor has only very limited effects on the spatial structure of the protein part of the molecule. On the other hand, as evidenced by the NMR line widths, the carbohydrate attachment causes a pronounced restriction of the overall mobility of the molecule, indicating a sizeable increase of the average radius of gyration as compared to the basic pancreatic trypsin inhibitor. Possible spatial arrangements of the globular polypeptide and the carbohydrate moieties in the colostrum inhibitor, which would be compatible with the experimental observations, are discussed.
Amino Acid Sequence↗
[Molecular dynamics of energy-conversion process in biological systems--laying stress on muscular contraction (author's transl)].
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Actomyosin↗
Molecular dynamics of lipids in human plasma high density lipoproteins. A high field 13C NMR study.
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Cholesterol↗
A Computational Workflow for Prioritizing Microbial Metabolite-Associated Host Genes in Constipation-Predominant Irritable Bowel Syndrome.
No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn.
Irritable Bowel Syndrome↗
Dynamic cooperativity of molecular processes in active streaming, muscle contraction, and subcellular dynamics: the molecular mechanism of self-organization at the subcellular level.
Life phenomena are a kind of ordered dynamics appearing in macroscopic systems, living systems. Schrödinger has proposed a molecular mechanism for the organization of life phenomena, i.e., 'order-from-order' mechanism where ordered dynamics are composed of molecular dynamics having order as the ordered dynamics of a watch is caused by orderly movements of its mechanical elements. However, neither evidence supporting the 'order-from-order' mechanism has been found in living systems nor the reason why molecular dynamics acquire order instead of disorder has been elucidated for more than 30 years. The latter is quite anomalous from the point of views of thermodynamics, which is based on disordered behaviors of molecules. In this paper, we verify from studies of a streaming system reconstituted from rabbit skeletal F-actin and HMM that one life phenomenon, active streaming, is caused by the 'order-from-order' mechanism. This is also the case for muscle contraction. Moreover, it is probable that this mechanism generally works at the subcellular level, not only in biological motilities but also in life phenomena at biomembranes. We also clarify that dynamic cooperativity among molecule gives rise to order in molecular dynamics. Hence, dynamic cooperativity is the key mechanism for life phenomena caused by the 'order-from-order' principle at the subcellular level. To produce dynamic cooperativity it is necessary for component molecules or elements to have three states, i.e., inactive (stable) state 0, energized or energy storing (quasi-stable) state 1, and active (unstable) state 2. Each molecule performs elementary cycle 0 yields 1 yields 2 yields 0 repeatedly by using free energy at the molecular level. In a state far from thermodynamic equilibrium dynamic cooperativity is yielded in 2 yields 0 due to a kind of triggering action of neighboring elements and breaks thermodynamic detailed balance. In addition, dynamic cooperativity gives component molecules long-range interactions which depend on the structure of organelles or molecular assemblies. Dynamic cooperativity is able to decrease entropy production and will give a high efficiency in chemo-mechanical conversions. Great progress would be achieved in the understanding of the molecular mechanisms and thermodynamic principles of energy transformations in biological systems, if molecular dynamics during transformation could be directly observed. This is not only because physical changes accompanied by specific movements of macromolecules are essentially involved but also because such molecular movements play a substantial role in energy transformation. Entirely new ideas will be needed for this purpose although high voltage electron microscopy or X-ray diffraction, for instance, is now expected as to be one of the possible tools in the future. Fortunately even at present it is possible to obtain important information on molecular dynamics from biochemical and physiological data, if analyses are properly performed...
Actomyosin↗
Genomic and structural analysis of dacB variants associated with cephalosporin resistance in Pseudomonas aeruginosa.
The rise of resistance to fourth-generation cephalosporin in Pseudomonas aeruginosa (P. aeruginosa) is a global concern. The resistance is largely driven by variants of chromosomally encoded AmpC β-lactamase, known as Pseudomonas-derived cephalosporinase (PDC), which arise from the mutations in the ampC gene. In addition, alteration in dacB, which encode the penicillin-binding protein 4 (PBP4), can lead to the overexpression of ampC, thereby contributing to β-lactam resistance. Present work analyzed 208 clinical isolates of P. aeruginosa using whole-genome sequencing (WGS) and detected multiple nonsynonymous single nucleotide polymorphisms (nsSNPs), such as Y264C, G444D, and a double mutation (A394P-T428P). All nsSNPs were predicted to be deleterious by several prediction program. Molecular dynamics (MD) simulations suggested that these substitutions destabilize PBP4, increase structural flexibility, and contribute to the resistance mechanism, which favored their selection. To determine the effective therapeutics against these mutations, molecular docking was conducted with various antibiotics. Cefoperazone exhibited the highest binding affinity (-7.3 kcal/mol) among multiple PBP4 variants. The Molecular dynamics (MD) simulations and Molecular Mechanics Poisson Boltzmann Surface Area calculations (MMPBSA) further confirmed the favorable interactions between cefoperazone and PBP4 variants. In vitro MIC analyses supported these findings, indicating that cefoperazone displayed significant activity against clinical dacB mutants of P. aeruginosa. The study offers structural insight of dacB variants leading to antibiotic resistance and emphasizes the need to prioritize specific antibiotics to address the challenges arising from protein mutations.
Pseudomonas aeruginosa↗
In silico screening of anti-atherosclerotic compounds from Morus alba leaves by machine learning and network pharmacology.
OBJECTIVE: This study integrates machine learning with network pharmacology, molecular docking, and molecular dynamics simulations to screen bioactive compounds from Mulberry leaves and elucidate their potential mechanisms against atherosclerosis (AS). METHODS: A training dataset of anti-AS active compounds was compiled and encoded as Morgan fingerprints. Three machine learning classifiers, specifically Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XG-Boost), were constructed and evaluated using multiple performance metrics. Potential active components from Mulberry leaves and AS-related targets were retrieved, followed by protein-protein interaction network construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was then performed to evaluate binding affinities between core targets and candidate compounds, and the most stable complex was subjected to molecular dynamics simulations using GROMACS (2025). RESULTS: The RF model achieved superior performance (accuracy= 0.8354, F1 = 0.8408, AUC = 0.9119) with 100% external validation accuracy. Thirteen anti-AS candidates were prioritized from mulberry leaves, four of which have been previously documented. Network pharmacology revealed AKT1 and IL6 as core targets, enriched in pathways such as endocrine resistance. Molecular docking and dynamics simulations confirmed strong binding between oxysanguinarine and AKT1, with the complex exhibiting high stability. CONCLUSION: The RF model provides a reliable computational tool for prioritizing anti-AS compounds from Mulberry leaves. The integrated analysis reveals that Mulberry leaves exert anti-atherosclerotic effects through multi-target (e.g., AKT1, IL6) and multi-pathway (e.g., PI3K-Akt) mechanisms, offering a framework for further experimental validation.
Morus↗
The Mechanism of Celosiae Semen in the Treatment of Diabetic Cataract: Based on Network Pharmacology.
INTRODUCTION: Diabetes mellitus can be complicated by a variety of ocular diseases, among which the postoperative complications of diabetic cataract (DC) are significantly higher than those of non-DC patients. Therefore, finding drugs with natural active ingredients is an urgent challenge in the prevention and treatment of DC. Discovering the potential molecular mechanism of celosiae semen (CS) for the treatment of DC and providing new ideas and programs for the treatment and prevention of DC. METHODS: In this study, network pharmacology, molecular docking, and molecular dynamics simulations were utilized to predict the binding and functional enrichment of the main active ingredients of CS with DC-related targets, and to explore the potential pathways and mechanisms of CS for the treatment of DC. RESULTS: Through database searching and screening, a total of 45 potential targets of CS for the treatment of DC were identified, functionally enriched, and a protein-protein interaction network was constructed, and the key target, SRC, was finally found. The results of molecular docking and molecular dynamics simulation showed that the main active ingredient of CS, stigmasterol, could bind stably to the key target SRC protein. DISCUSSION: This study not only elucidates the phyto-pharmacological basis of CS in DC management but also provides a framework for developing natural product-derived targeted therapies against diabetic ocular complications. The integration of modern genomics and computational chemistry to deconstruct the therapeutic effects of traditional Chinese herbal medicines has great clinical significance in expanding the scope of traditional Chinese medicines for the treatment of DC and promoting precision targeting. However, this requires verification through basic experiments. CONCLUSION: These computational findings suggest that CS may exert its anti-cataract effects through the multi-target modulation of diabetic metabolic pathways and SRC-mediated signaling cascades.
Humans↗
Mechanism of Action of Hedyotis diffusa Extract in a Rat Model of Acute Lung Injury Based on Transcriptomic Analysis.
OBJECTIVE: This study established a rat model of lipopolysaccharide (LPS)-induced acute lung injury (ALI) to evaluate pathological damage, collagen deposition, inflammatory cytokine levels, and key gene/protein expression following Hedyotis diffusa water extract (HDWE) intervention. Combined with ultra-high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), transcriptomic analysis, and molecular simulation, this study identified the bioactive components of HDWE, evaluated their potential interactions with ALI-related targets, and explored the multi-omics-based protective mechanisms of HDWE. METHODS: Thirty-six Sprague-Dawley (SD) rats were randomly divided into six groups: Control group, ALI group, DXMS group, HDWE-L group (100 mg/kg), HDWE-M group (200 mg/kg), and HDWE-H group (300 mg/kg). Hematoxylin and eosin (H&E) and Masson's trichrome staining were used to evaluate lung pathological changes and collagen deposition. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum tumor necrosis factor-α TNF-α interleukin-1β IL-1β, erleukin-6 (IL-6), and interleukin-10 (IL-10) levels. Transcriptomic analysis identified differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), receiver operating characteristic (ROC), and immune infiltration analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) detected the mRNA expression levels of SPHK1, RELA, and NFKBIA. Immunohistochemistry evaluated the expression of eight hub targets, including endothelin-1 (EDN1), sphingosine kinase 1 (SPHK1), intercellular adhesion molecule 1 (ICAM1), interleukin-17 (IL-17), prostaglandin-endoperoxide synthase 2 (PTGS2/COX-2), NF-κB p65 (encoded by RELA), WT1-associated protein (WTAP), and myeloperoxidase (MPO). UHPLC-Q-Orbitrap HRMS characterized HDWE constituents. Molecular docking analysis was performed between 22 compounds and eight hub targets, followed by 100 ns molecular dynamics simulations and molecular mechanics-Poisson-Boltzmann surface area (MM/PBSA) binding free energy calculations for five core targets. Compared with the control group, the ALI group showed increased levels of TNF-α (86%), IL-1β (107%), and IL-6 (66%), accompanied by a 43% reduction in IL-10 and a 300% increase in lung collagen deposition. All HDWE doses alleviated inflammatory responses, with medium-dose HDWE showing the most pronounced effects. Specifically, medium-dose HDWE increased IL-10 levels by 52% and reduced IL-6, TNF-α, and IL-1β levels by 18%, 22%, and 11%, respectively. Transcriptomic analysis identified 2512 DEGs between the control group and ALI groups, 832 exclusive DEGs between the ALI group and HDWE-M groups, and 876 overlapping DEGs enriched in TNF, IL-17, and NF-κB signaling pathways. The eight-hub-gene diagnostic model achieved an area under the curve (AUC) of 0.969. RELA, SPHK1, and four other hub genes showed positive correlations with Th1, Th17, and neutrophil infiltration. In the ALI group, SPHK1, RELA, and NFKBIA mRNA expression levels were 1.30-, 0.96-, and 0.71-fold of those in the control group, respectively. Compared with the ALI group, high-dose HDWE treatment and low-dose HDWE treatment reduced SPHK1 expression to 0.62- and 0.57-fold, respectively, and increased NFKBIA expression to 1.68- and 1.58-fold, respectively. High-dose HDWE treatment reduced RELA expression to 0.43-fold. The expression levels of inflammation-related proteins were increased in the ALI group and were reduced after HDWE treatment. Twenty-two HDWE components were identified, 16 of which met the docking criteria. Asperulosidic acid exhibited favorable predicted binding affinities with all eight targets, with calculated binding free energies of -14.74, -14.92, -17.58, -23.04, and -16.10 kcal/mol for MPO, IL-17, NF-κB p65, PTGS2/COX-2, and SPHK1, respectively. CONCLUSIONS: This study provides systematic in vivo pharmacodynamic and in silico component-target evidence regarding the protective effects of HDWE against LPS-induced ALI. HDWE treatment increased NFKBIA expression and reduced SPHK1, RELA, and multiple inflammatory protein levels, suggesting that HDWE may regulate the IL-17/NF-κB-associated inflammatory network, although direct causal relationships require further validation. Asperulosidic acid may represent a key bioactive component with broad target-binding potential. This study was limited by the use of an LPS-induced rat ALI model without gene knockout or target inhibitor validation; therefore, further functional experiments are required to confirm the proposed regulatory mechanisms.
Hedyotis diffusa↗
Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.
PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, this study aimed to clarify their association, identify the toxic targets of DEHP, and elucidate the underlying molecular mechanisms. METHODS: Multiple integrated approaches were employed, including Gene Expression Omnibus (GEO) data analysis, network toxicology, molecular docking, and machine learning. STRING and Cytoscape tools were utilized to identify key targets, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functional enrichment of intersecting targets. Machine learning and SHAP analysis were applied to screen core targets in GC. Molecular docking was performed to evaluate the binding affinity of DEHP toward core targets, and 200 ns molecular dynamics simulations were further conducted for representative complexes to validate their dynamic stability. RESULTS: A total of 18 key targets were identified using STRING and Cytoscape. GO and KEGG enrichment analyses demonstrated that these intersecting targets were primarily enriched in the extracellular region, as well as the Calcium signaling pathway and cAMP signaling pathway. Through machine learning analyses, 7 key genes (ADRB2, ESRRG, GRIA4, IL13RA2, NR3C2, PLA2G1B, and SULT2A1) were identified as core targets in GC through machine learning analyses. Molecular docking simulations revealed strong binding specificity between DEHP and the target proteins. Among them, NR3C2 and ADRB2 exhibited relatively high predictive importance in the machine learning models. DEHP showed favorable binding affinity toward these core targets, and molecular dynamics simulations further confirmed that ADRB2-DEHP and NR3C2-DEHP complexes maintained stable conformations throughout the simulation. CONCLUSIONS: Our findings identified GC associated genes that were computationally predicted as potential targets of DEHP. These results indicated structural compatibility between DEHP and its target proteins but did not prove that DEHP exposure accounts for the gene expression changes in GC.
Molecular Docking Simulation↗
Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.
INTRODUCTION: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. OBJECTIVES: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. METHODS: We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. RESULTS: The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. CONCLUSION: This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.
Animals↗
Activity of natural single nucleotide variants of human alkyladenine DNA glycosylase AAG R145H, G163S and R197C involved in DNA binding.
Alkyladenine DNA glycosylase (AAG) is a critical enzyme in the base excision repair (BER) pathway that safeguards genome integrity by removing structurally diverse alkylated and deaminated purine lesions from DNA. It serves as a primary defense against alkylation-induced mutations, which are linked to cancer development, chronic inflammation, and neurodegenerative diseases. Single nucleotide variants (SNVs) in the gene coding region have the potential to alter the enzyme's functionality, potentially modulating the repair capacity and affecting response and prognosis following chemoradiotherapy. In our study, we investigated three SNVs that lead to amino acid class changes in regions involved in DNA substrate coordination: R145H, G163S, and R197C using an in vitro approach. Using biochemical assays and molecular dynamics simulations, we evaluated the thermal stability, DNA binding affinity, and glycosylase activity of AAG variants toward hypoxanthine (Hx) and 1,N6-ethenoadenosine (εA) containing substrates. The G163S variant showed reduced thermal stability due to the conformational strain in the β-hairpin loop that intercalates in DNA, but retained εA excision activity comparable to that of wild-type AAG, while losing activity against Hx-containing DNA. The R197C variant had a four-fold reduction in DNA binding affinity for both substrates, and was catalytically inactive, unable to excise either damaged bases. This loss of function correlated with the rearrangement of the 201-210 loop and the reorientation of Arg-201 and Arg-207, which disrupts critical DNA contacts. However, the R145H variant retained near-wild-type thermal stability and activity on both substrates, despite bioinformatic predictions of deleterious effect. Molecular dynamics simulations revealed variant-specific structural disruptions. The data obtained underscore the importance of experimental validation in assessing the functional impact SNVs.
DNA Glycosylases↗
Zhiling Jiangya decoction treats hypertension in rats: An integrative study of network pharmacology, immune infiltration, molecular simulation, and 16S rDNA sequencing.
OBJECTIVE: This study integrated network pharmacology, immune infiltration analysis, molecular docking, molecular dynamics simulation, ADMET prediction, 16S rDNA sequencing, and rat experiments to elucidate the potential mechanisms underlying the antihypertensive effects of Zhiling Jiangya Decoction (ZLJYD). METHODS: Active compounds and their potential targets were screened from the PubChem, TCMSP, NovoPro, and SwissTargetPrediction databases. Hypertension-related targets were retrieved from the OMIM and GeneCards databases, and overlapping targets were identified. The STRING database and Cytoscape 3.10.1 software were used to construct a protein-protein interaction network and a herb-component-target-disease network. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the key biological processes and signaling pathways involved. Using the CIBERSORT algorithm combined with correlation analysis, we investigated the association between key targets and immune cell infiltration. Molecular docking, molecular dynamics simulations, and ADMET predictions were performed to assess the binding stability and pharmacokinetic properties of the main compounds with their corresponding targets. Finally, the antihypertensive efficacy of ZLJYD was validated using a spontaneously hypertensive rat model, and alterations in gut microbiota were analyzed using 16S rDNA sequencing. RESULTS: A total of 123 active compounds and 267 hypertension-related targets of ZLJYD were identified. Enrichment analysis revealed that these targets were primarily associated with the PI3K-Akt signaling pathway and lipid and atherosclerosis pathways. Immune infiltration analysis suggested that the therapeutic effects of ZLJYD may involve the regulation of follicular helper T cells, naïve B cells, and naïve CD4⁺ T cells. Molecular docking and dynamics simulations supported the stable binding of key compounds to their target proteins, while ADMET predictions indicated favorable pharmacokinetic properties and safety profiles. Rat experiments demonstrated that ZLJYD significantly reduced blood pressure in spontaneously hypertensive rats, partially alleviated gut microbiota dysbiosis, and altered microbial community structure and phylogenetic diversity. CONCLUSION: This study systematically elucidates the potential mechanisms underlying the antihypertensive effects of ZLJYD through multiple components, targets, and pathways, particularly immune regulation and gut microbiota remodeling. These findings provide mechanistic insights into its potential therapeutic application.
16S rDNA sequencing↗
A reinterpretation of the infrared linear dichroism of oriented nucleic acid films and a calculation of some effective partial changes on the ribose phosphate backbone.
In this paper we show, based on symmetry considerations, that structural information cannot be obtained from the linear infrared dichroism of the dioxy vibrations of the phosphate group of nucleic acids. Consequently, the discrepancies between the results of x-ray structure measurements and linear dichroism measurements are not meaningful. The linear dichroism measurements are instead important for a calculation of transition dipole moments that involve both the vibrations of all the atoms of the nucleotide and their charges. Independent information on either the atomic displacements contributing to a given vibration or the atomic charges permits a refinement of the unknown quantities. Based on the molecular dynamics calculations of Prohofsky et al., atomic charges of DNA are calculated to reproduce the observed linear dichroism results. Some of the resulting charges are unexpected and may reflect the inadequacy of the molecular dynamic calculation.
DNA↗
Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.
OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.
Blood Platelets↗
An ATP-Driven N Protein-DDX21 Molecular Switch Dynamically Controls SARS-CoV-2 RNA G-Quadruplex Heterogeneity.
The SARS-CoV-2 RNA genome functions as a highly structured regulatory scaffold. Although bioinformatic analyses predict widespread RNA G-quadruplexes (G4s) across the viral genome, their structural diversity and regulatory mechanisms remain poorly understood. Here, we report a diverse landscape of viral G4s encompassing parallel and non-canonical topologies with remarkable thermostability. Unlike typical eukaryotic G4s, these two-tetrad viral G4s exhibit a hierarchical ion-dependent mechanism, in which K+ establishes the core fold, and Mg2 + acts as a secondary regulator promoting conformational compaction. Single-molecule FRET analysis further distinguishes rigid, long-lived G4 folds from highly dynamic, metastable species, defining a continuum of conformational states along the viral genome. Functionally, we identify a synergistic yet competitive interplay between the viral nucleocapsid (N) protein and host helicase DDX21. While the N protein acts as a molecular chaperone to promote G4 folding, DDX21 selectively resolves these structures in an ATP-dependent manner. Strikingly, N and DDX21 jointly constitute a finely tuned, ATP-driven molecular switch, where ATP availability dictates the equilibrium between G4-stabilized and resolved states. Our findings establish a mechanistic framework for the active regulation of SARS-CoV-2 RNA architecture and reveal a multilayered host-virus regulatory axis that modulates viral genome heterogeneity.
DEAD‐box helicases↗