CHARMM: the biomolecular simulation program

A hammerhead ribozyme selects mechanically stable conformations for catalysis against viral RNA

Ribozymes, widely found in prokaryotes and eukaryotes, target nucleic acids and can be engineered as biotechnical tools or for gene regulation or immune therapy. Among them, hammerhead is the smallest and best characterized ribozyme. However, the structure and biochemical data of ribozymes have been disagreed on, making the understanding of its catalysis mechanism a longstanding issue. Particularly, the role of conformational dynamics in ribozyme catalysis remains elusive. Here, we use single-molecule magnetic tweezers to reveal a concerted catalysis mechanism of mechanical conformational selection for a mini hammerhead ribozyme against a viral RNA sequence from the SARS-CoV-2. We identify a conformational set containing five mechanical conformers of the mini ribozyme, where magnesium ions select the active one. Our results are supported by molecular dynamics simulations. Our understanding of the RNA catalytic mechanism will be beneficial for ribozyme's biotechnological applications and as potential therapeutics against RNA viruses.

Investigation of Muscarinic Acetylcholine Receptor M3 Activation in Atomistic Detail: A Chemist's Viewpoint

We analyzed the precise ligand:receptor interactions required for activation of the muscarinic acetylcholine receptor M3, a prototypical G protein-coupled receptor and potential diabetes target. Starting from literature-known compounds and docking solutions, ligands were tailored for the modulation of this receptor's activation. Several aspects of the structure-activity relationship of agonists were investigated in atomistic detail, in order to delineate how the receptor can be activated via the orthosteric site. Such exquisitely precise knowledge is instrumental for designing potent and effiacious ligands. We put this strategy into practice and acquired or synthesized and measured a diverse set of 55 ligands ranging from small fragment-like amines coordinating D3.32 to bigger molecules extending towards helices 5 and 6 with diphenyl moieties. In the course of these investigations, we showed that the polarizability of the amine nitrogen and the rigidity and size of the moieties in the space delimited by helices 5 and 6 are the two key elements distinguishing potent and efficacious ligands from those that are not. The resulting data set will be highly useful in drug design and molecular machine learning alike.

Computational analysis of Urolithin A as a potential compound for anti-inflammatory, antioxidant, and neurodegenerative pathways

Urolithin A, an active precursor derived from the metabolism of ellagitanins in rats and humans, is known for its potential health benefits, including stimulating mitophagy and promoting muscular skeletal function. While experimental studies have demonstrated Urolithin A's potential to enhance cellular health, the detailed molecular interactions through which Urolithin A exerts its effects are not fully elucidated. In this study, we investigated the anti-inflammatory, antioxidation and neuroprotective abilities of Urolithin A in selected targets using molecular docking and molecular dynamics simulation methods. Molecular docking studies revealed the strong affinity for receptors involved in inflammation activities, including human p38 MAP kinase (4DLI) with -10.1 kcal/mol interacting with SER252, LYS249, and ASP294 residues. The binding energy in the 5KIR target was -8.6 kcal/mol, interacting with GLN203 through hydrogen bond, and lastly, 1A9U with an affinity of -6.8 with no hydrogen bond formed with Urolithin A and interacts with van der Waals interactions. In oxidant targets, the influence of Urolithin was observed in 1OG5 with -7.9 kcal/mol interacting with GLN185, PHE447. For the 1M17 target, the binding affinity was -7.7 kcal/mol interacting with THR95 residue and 1ZXM target at -7.4 kcal/mol interacting with TYR36, TYR216, and LEU234 residues. The neuroprotective ability of urolithin A was observed in selected targets for acetylcholinesterase; the binding energy was -9.7 kcal/mol interacting with van der Waals and π interactions; for the 1GQR target, the binding energy was -9.9 kcal/mol interacting with van der Waals and π interactions and for β-amylase (1iyt) the binding energy was -5.5 forming hydrogen bond with SER8, GLN15 residues. Molecular Dynamics simulations at 100 ns of Urolithin A compared with reference 4DLI. The Urolithin A-4DLI complex exhibited greater stability than the reference receptor, as confirmed by RMSD, RMSF, Radius of Gyration, Hydrogen bond, and SASA analyses.

Insights into Interlayer Dislocation Augmented Zinc-Ion Storage Kinetics in MoS2 Nanosheets for Rocking-Chair Zinc-Ion Batteries with Ultralong Cycle-Life

Increasing attention to sustainability and cost-effectiveness in energy storage sector has catalyzed the rise of rechargeable Zinc-ion batteries (ZIBs). However, finding replacement for limited cycle-life Zn-anode is a major challenge. Molybdenum disulfide (MoS2), an insertion-type 2D layered material, has shown promising characteristics as a ZIB anode. Nevertheless, its high Zn-ion diffusion barrier because of limited interlayer spacing substantiates the need for interlayer modifications. Here, N-doped carbon quantum dots (N-CQDs) are used to modify the interlayers of MoS2, resulting in increased interlayer spacing (0.8 nm) and rich interlayer dislocations. MoS2@N-CQDs attain a high specific capacity (258 mAh g-1 at 0.1 A g-1), good cycle life (94.5% after 2000 cycles), and an ultrahigh diffusion coefficient (10-6 to 10-8 cm2 s-1), much better than pristine MoS2. Ex situ Raman studies at charge/discharge states reveal that the N-CQDs-induced interlayer expansion and dislocations can reversibly accommodate the volume strain created by Zn-ion diffusion within MoS2 layers. Atomistic insight into the interlayer dislocation-induced Zn-ion storage of MoS2 is unveiled by molecular dynamic simulations. Finally, rocking-chair ZIB with MoS2@N-CQDs anode and a ZnxMnO2 cathode is realized, which achieved a maximum energy density of 120.3 Wh kg-1 and excellent cyclic stability with 97% retention after 15 000 cycles.

Phytochemical profiling, in vitro analysis for anti-inflammatory, immunomodulatory activities, structural elucidation and in silico evaluation of potential selective COX-2 and TNF-α inhibitor from Hydrilla verticillata (L.f.) Royle

Hydrilla verticillata (L.f.) Royle is a perennial aquatic plant, which exhibits nutritional as well as therapeutic properties. The present study has been carried out to evaluate anti-inflammatory and immunomodulatory activities along with in silico evaluation of potential selective COX-2 and TNF-α inhibitors from methanolic extract of H. verticillata (L.f.) Royle. The potential therapeutic compounds have been identified by high-resolution GC-MS analysis. Its capacity to inhibit inflammatory responses using lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophage cells has been explored. The anti-inflammatory properties of the plant extract were investigated by inhibiting inducible nitric oxide (NO) synthase and reduced NO generation driven by LPS on stimulated RAW 264.7 macrophage cells. Further investigation for the underlying molecular mechanism of the anti-inflammatory activity of plant extract has been carried out by molecular docking and molecular dynamics simulation approaches with COX-2 and TNF-α inhibitors ability against the most potent phytocompound phytol from the plant extract. To evaluate whether the extract causes any toxicity, the cytotoxicity test has been carried out with the Human embryonic kidney cell line (Hek-293), Mouse fibroblast (L929), human mesenchyme stem cells (hMSCs) and human breast epithelial cell line (MCF-10a). Ultimately, our findings suggest that the plant extract have great potential to reduce inflammation without causing any toxicity to normal cell.Communicated by Ramaswamy H. Sarma.

Characterization of the binding of the globular domains of the complement component C1q to phosphatidylserine

C1q, the key component of the classical pathway of the Complement system, is known for its vast functional activity including clearance of apoptotic cells. The binding of C1q to apoptotic blebs occurs via an interaction with the phosphatidylserine externalized on the cell surface. In this study, we characterized the interaction between C1q and phosphatidylserine, with emphasis on the structure of the phosphatidylserine-binding site within the globular domains of C1q and the nature of binding of C1q with phosphatidylserine, using both in vitro and in silico methods. We established that all three globular fragments, forming one C1q globular domain, bound phosphatidylserine with the leading role of the phosphatidylserine-binding site pertaining to the A chain of the globular fragment of C1q. We also determined the closest-contact amino acids of C1q participating in the interaction with phosphatidylserine. An important role is suggested for the glycosylated Asn124 residue in the A chain of the globular fragment.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.

Utility of Brownian dynamics simulations in chemistry and biology: A comprehensive review

Brownian dynamics (BD) simulations, a powerful computer simulation tool that has gained significant attraction in investigating the intricate dynamics of chemical and biological systems. By meticulously modeling the diffusive motion of molecules and their intricate interactions, BD simulations offer invaluable insights into a diverse array of phenomena, including reaction kinetics, molecular transport, and biomolecular association. This comprehensive review delves into the utility of BD simulations within the realms of chemistry and biology. We meticulously explore the theoretical underpinnings of the technique, critically analyze its strengths and limitations, and showcase its diverse applications across various scientific domains. By providing a comprehensive analysis of the existing literature, we aim to illuminate the potential of BD simulations to significantly advance our understanding of complex chemical and biological systems, ultimately contributing to the development of innovative therapeutic solutions serving a broad range of biomedical applications.

Profiling the antidiabetic potential of GC-MS compounds identified from the methanolic extract of Spilanthes filicaulis: experimental and computational insight

This study examines the nutritional composition, phytochemical profiling, and antioxidant, antidiabetic, and anti-inflammatory potential of a methanolic extract of Spilanthes filicaulis leaves (MESFL) via in vitro, ex vivo, and in silico studies. In vitro antioxidant, antidiabetic, and anti-inflammatory activities were examined. In the ex vivo study, liver tissues were subjected to FeSO4-induced oxidative damage and treated with varying concentrations of MESFL. MESFL contains a reasonable amount of nitrogen-free extract, moisture, ash content, crude protein, and fat, with a lesser amount of crude fiber. According to GC-MS analysis, MESFL contains ten compounds, the most abundant of which are 13-octadecenal and Ar-tumerone. In this study, MESFL demonstrated anti-inflammatory activities via membrane stabilizing properties, proteinase inhibition, and inhibition of protein denaturation (IC50 = 72.75 ± 11.06 µg/mL). MESFL also strongly inhibited both α-amylase (IC50 = 307.02 ± 4.25 µg/mL) and α-glucosidase (IC50 = 215.51 ± 0.47 µg/mL) activities. Our findings also showed that FeSO4-induced tissue damage decreased the levels of GSH, SOD, and CAT activities while increasing the levels of MDA. In contrast, treatment with MESFL helped to restore these parameters to near-normal levels, which signifies that MESFL has great potential to address complications from oxidative stress. Furthermore, the in silico interaction of the GCMS-identified phytochemicals with the active sites of α-amylase and α-glucosidase via molecular and ensembled-based docking displayed strong binding affinities of Ar-tumerone and 4-hydroxy-3-methylacetophenone to α-amylase and α-glucosidase, respectively. Taken together, the biological activities of MESFL might be a result of the effects of these secondary metabolites.Communicated by Ramaswamy H. Sarma.

2-{N-[ω-(1-Benzylpiperidin-4-yl)alkyl]amino}-6-[(prop-2-yn-1-yl)amino]pyridine-3,5-dicarbonitriles Showing High Affinity for σ1/2 Receptors

Sigma receptors (σRs) represent very attractive biological targets for the development of potential agents for the treatment of several neurological disorders. In the search for new small molecule drugs against neuropathic pain, we identified 2-{[2-(1-benzylpiperidin-4-yl)ethyl]amino}-6-[methyl(prop-2-yn-1-yl)amino]pyridine-3,5-dicarbonitrile (5) as a polyfunctionalized small pyridine with potent dual-target activities against acetylcholinesterase (AChE) (IC50 = 13 nM) and butyrylcholinesterase (BuChE) (IC50 = 3.1 µM), exhibiting high σ1R affinity (Ki(hσ1R) = 1.45 nM) and 290-fold selectivity over the σ2R subtype. These results are in good agreement with those found in the molecular modeling of compound 5. This is possibly due to the preferred combination in this molecule of a linker n = 2 connecting the N-Bn-piperidine motif to the C2 pyridine, without a phenyl group at C4, and a N-Me-substituted propargyl amine in the chain located at C6.

Molecular mechanisms of phytoconstituents from selected Egyptian plants against non-small cell lung cancer using integrated in vitro network pharmacology and molecular docking approach

Non-small cell lung cancer (NSCLC) is a widespread highly malignant type of lung cancer. Conventional chemotherapeutic drugs may be accompanied by both drug resistance and serious side effects in patients. Therefore, safer and more effective medications are urgently needed for the treatment of NSCLC. This study investigates the mode of action of 21 phytoconstituents previously isolated from the Amaryllidaceous plants Crinum bulbispermum (Burm.f.), Pancratium maritimum L., and Hippeastrum vittatum Herbert alongside the Asteraceous plant Centaurea scoparia Sieb. for therapy of NSCLC via in vitro cytotoxic, network pharmacology, and molecular docking analyses. Despite the in vitro and in vivo cytotoxic studies carried out on phytoconstituents from these plants in treating numerous cancer types, scarce information documenting their cytotoxic activity towards NSCLC cells is available. First, the compounds were tested for their in vitro cytotoxic activities and selectivity on human non-small cell lung cancer cells using disk diffusion assay. Compounds having significant potencies were promoted for network pharmacology analysis. Pharm mapper, Genecards, STRING, and KEGG databases were utilized for surfing target genes and pathways for these compounds, while for construction of compound-target-pathway (C-T-P) network, Cytoscape 3.7.1. freeware was used. Molecular docking and dynamics simulation were run for the top hit constituents against the most enriched molecular targets followed by in silico ADMET studies using Schrodinger® suite and Gromacs. In vitro cytotoxicity testing demonstrated that crinamine was the most potent compound followed by lycorine, hemanthidine, and haemanthamine. The network pharmacology approach revealed the enrichment of acetyllycoramine, pluviine, 5-hydroxy-7-methoxy-2-methylchromone, and ismine. Whereas, androgen receptor (AR), epidermal growth factor receptor (EGFR), and estrogen-sensitive receptor alpha (ESR1) were the most enriched target genes. Pathway analysis revealed that central carbon metabolism, EGFR tyrosine kinase inhibitor endocrine resistance, and non-small cell lung cancer were the most enriched cancer-related pathways. Ismine possessed the most stable ligand-protein interactions when docked to the three proteins, with MD simulations further confirming its strong and consistent binding to AR, moderate stability with ESR-1, and lower stability with EGFR over the 100 ns trajectory. ADMET study conducted on the above compounds confirmed their excellent drug-likeness properties, oral bioavailability, and safety profiles highlighting the need for some structural modifications to pluviine to enhance its oral bioavailability. These integrated approaches showed that some constituents from the investigated plants interact synergistically against non-small cell lung cancer-related genes and pathways.

Navigating the landscape: A comprehensive overview of computational approaches in therapeutic antibody design and analysis

Immunotherapy, harnessing components like antibodies, cells, and cytokines, has become a cornerstone in treating diseases such as cancer and autoimmune disorders. Therapeutic antibodies, in particular, have transformed modern medicine, providing a targeted approach that destroys disease-causing cells while sparing healthy tissues, thereby reducing the side effects commonly associated with chemotherapy. Beyond oncology, these antibodies also hold promise in addressing chronic infections where conventional therapeutics may fall short. However, antibodies identified through in vivo or in vitro methods often require extensive engineering to enhance their therapeutic potential. This optimization process, aimed at improving affinity, specificity, and reducing immunogenicity, is both challenging and costly, often involving trade-offs between critical properties. Traditional methods of antibody development, such as hybridoma technology and display techniques, are resource-intensive and time-consuming. In contrast, computational approaches offer a faster, more efficient alternative, enabling the precise design and analysis of therapeutic antibodies. These methods include sequence and structural bioinformatics approaches, next-generation sequencing-based data mining, machine learning algorithms, systems biology, immuno-informatics, and integrative approaches. These approaches are advancing the field by providing new insights and enhancing the accuracy of antibody design and analysis. In conclusion, computational approaches are essential in the development of therapeutic antibodies, significantly improving the precision and speed of discovery, optimization, and validation. Integrating these methods with experimental approaches accelerates therapeutic antibody development, paving the way for innovative strategies and treatments for various diseases ranging from cancers to autoimmune and infectious diseases.

Conformational dynamics of a multienzyme complex in anaerobic carbon fixation

In the ancient microbial Wood-Ljungdahl pathway, carbon dioxide (CO2) is fixed in a multistep process that ends with acetyl-coenzyme A (acetyl-CoA) synthesis at the bifunctional carbon monoxide dehydrogenase/acetyl-CoA synthase complex (CODH/ACS). In this work, we present structural snapshots of the CODH/ACS from the gas-converting acetogen Clostridium autoethanogenum, characterizing the molecular choreography of the overall reaction, including electron transfer to the CODH for CO2 reduction, methyl transfer from the corrinoid iron-sulfur protein (CoFeSP) partner to the ACS active site, and acetyl-CoA production. Unlike CODH, the multidomain ACS undergoes large conformational changes to form an internal connection to the CODH active site, accommodate the CoFeSP for methyl transfer, and protect the reaction intermediates. Altogether, the structures allow us to draw a detailed reaction mechanism of this enzyme, which is crucial for CO2 fixation in anaerobic organisms.

Fluoxetine Alters the Biophysics of DPPC and DPPG Bilayers through Phase-Dependent and Electrostatic Interactions

Lipid membranes can control the permeability of a pharmaceutical drug, whereas the drug can induce changes in the structural and biophysical properties of the membranes. Understanding this interplay of drug-lipid membrane interactions can be of great importance in drug design. Here, we present a molecular dynamics study to provide insights into the interactions between the antidepressant fluoxetine and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) or 1,2-dipalmitoyl-sn-glycero-3-phosphoglycerol (DPPG) bilayers. It was found that, due to the electrostatic interaction, the headgroup of the zwitterionic DPPC lipid is more stable than that of the negatively charged DPPG lipid, allowing the gel phase to persist even at the elevated temperature. At 25 °C, fluoxetine cannot penetrate into the gel-phase DPPC bilayer, while the electrostatic interaction between positively charged fluoxetine and negatively charged DPPG bilayer retains the drug within the lipid headgroup domain. When the temperature is increased to 45 °C, both neutral and charged forms of fluoxetine can partition into the DPPC and DPPG bilayers spontaneously. Analysis of the biophysical and structural changes in both DPPC and DPPG bilayers in the presence of fluoxetine revealed a phase-dependent effect. The binding of fluoxetine to the lipid bilayers limits the movement and orientation of the drug. These findings shed light on the interactions between a commonly prescribed antidepressant and lipid membranes, and such information can be beneficial to the development of potential therapeutic agents.

Developing T Cell Epitope-Based Vaccines Against Infection: Challenging but Worthwhile

T cell epitope-based vaccines are designed to elicit long-lived pathogen-specific memory T cells that can quickly activate protective effector functions in response to subsequent infections. These vaccines have the potential to provide sustained protection against mutated variants, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which are increasingly capable of evading neutralizing antibodies. Recent advancements in epitope discovery, T cell receptor analysis, and bioinformatics have enabled the precise selection of epitopes and the sophisticated design of epitope-based vaccines. This review outlines the development process for T cell epitope-based vaccines. We summarize the current progress in T cell epitope discovery technologies, highlighting the advantages and disadvantages of each method. We also examine advancements in the design and optimization of epitope-based vaccines, particularly through bioinformatics tools. Additionally, we discuss the challenges of validating the accurate processing and presentation of individual epitopes and establishing suitable rodent models to evaluate vaccine immunogenicity and protective efficacy.

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions.

Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions

Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different enzyme environments and reacting species, thereby eliminating the need for retraining with each new enzyme variant. In this study, we explore this potential by evaluating the transferability of a pretrained ΔMLP model across different enzyme mutations within the MM environment using the QM/MM-based ML architecture developed by Pan, X. J. Chem. Theory Comput. 2021, 17(9), 5745-5758. The study includes scenarios such as single point substitutions, a homologous enzyme from different species, and even a transition to an aqueous environment, where the last two systems have MM environment that is substantially different from that used in MLP training. The results show that the ΔMLP model effectively captures and predicts the effects of enzyme mutations on electrostatic interactions, producing reliable free energy profiles of enzyme-catalyzed reactions without the need for retraining. The study also identified notable limitations in transferability, particularly when transitioning from enzyme to water-rich MM environments. Overall, this study demonstrates the robustness of the Pan et al.'s QM/MM-based ML architecture for application to diverse enzyme systems, as well as the need for further research and the development of more sophisticated MLP models and training methods.

PACKMOL-GUI: An All-In-One VMD Interface for Efficient Molecular Packing

PACKMOL is a widely utilized molecular modeling tool within the computational chemistry community. However, its tremendous advantages have been impeded by the longstanding lack of a robust open-source graphical user interface (GUI) that integrates parameter settings with the visualization of molecular and geometric constraints. To address this limitation, we have developed PACKMOL-GUI, a VMD plugin that leverages the dynamic extensibility of the Tcl/Tk toolkit. This GUI enables the configuration of all PACKMOL parameters through an intuitive user panel, while also facilitating the visualization of molecular structures and geometric constraints, including cubes, boxes, and spheres, among others via the VMD software. The seamless interaction between the VMD and PACKMOL provides an intuitive and efficient all-in-one platform for the packing of complex molecular systems.

Split Membrane: A New Model to Accelerate All-Atom MD Simulation of Phospholipid Bilayers

All-atom molecular dynamics simulations are powerful tools for studying cell membranes and their interactions with proteins and other molecules. However, these processes occur on time scales determined by the diffusion rate of phospholipids, which are challenging to achieve in all-atom models. Here, we present a new all-atom model that accelerates lipid diffusion by splitting phospholipid molecules into head and tail groups. The bilayer structure is maintained by using external lateral potentials, which compensate for the lipid split. This split model enhances lateral lipid diffusion more than ten times, allowing faster and cheaper equilibration of large systems with different phospholipid types. The current model has been tested on membranes containing PSM, POPC, POPS, POPE, POPA, and cholesterol. We have also evaluated the interaction of the split model membranes with the Disheveled DEP domain and amphiphilic helix motif of the transcriptional repressor Opi1 as representative of peripheral proteins as well as the dimeric fragment of the epidermal growth factor receptor transmembrane domain and the Human A2A Adenosine of G protein-coupled receptors as representative of transmembrane proteins. The split model can predict the interaction sites of proteins and their preferred phospholipid type. Thus, the model could be used to identify lipid binding sites and equilibrate large membranes at an affordable computational cost.

mTOR Variants Activation Discovers PI3K-like Cryptic Pocket, Expanding Allosteric, Mutant-Selective Inhibitor Designs

mTOR plays a crucial role in PI3K/AKT/mTOR signaling. We hypothesized that mTOR activation mechanisms driving oncogenesis can advise effective therapeutic designs. To test this, we combined cancer genomic analysis with extensive molecular dynamics simulations of mTOR oncogenic variants. We observed that conformational changes within mTOR kinase domain are associated with multiple mutational activation events. The mutations disturb the α-packing formed by the kαAL, kα3, kα9, kα9b, and kα10 helices in the kinase domain, creating cryptic pocket. Its opening correlates with opening of the catalytic cleft, including active site residues realignment, favoring catalysis. The cryptic pocket created by disrupted α-packing coincides with the allosteric pocket in PI3Kα can be harmoniously fitted by the PI3Kα allosteric inhibitor RLY-2608, suggesting that analogous drugs designed based on RLY-2608 can restore the packed α-structure, resulting in mTOR inactive conformation. Our results exemplify that knowledge of detailed kinase activation mechanisms can inform innovative allosteric inhibitor development.

Activation and Reactivity of the Deubiquitinylase OTU Cezanne-2 from MD Simulations and QM/MM Calculations

Cezanne-2 (Cez2) is a deubiquitinylating (DUB) enzyme involved in the regulation of ubiquitin-driven cellular signaling and selectively targets Lys11-linked polyubiquitin chains. As a representative member of the ovarian tumor (OTU) subfamily DUBs, it performs cysteine proteolytic isopeptide bond cleavage; however, its exact catalytic mechanism is not yet resolved. In this work, we used different computational approaches to get molecular insights into the Cezanne-2 catalytic mechanism. Extensive molecular dynamics (MD) simulations were performed for 12 μs to model free Cez2 and the diubiquitin (diUb) substrate-bound protein-protein complex in two different charge states of Cez2, each corresponding to a distinct reactive state in its catalytic cycle. The simulations were analyzed in terms of the relevant structural parameters for productive enzymatic catalysis. Reactive diUb-Cez2 complex configurations were identified, which lead to isopeptide bond cleavage and stabilization of the tetrahedral oxyanion intermediate. The reliability of these complexes was further assessed by quantum mechanics/molecular mechanics (QM/MM) optimizations. The results show that Cez2 follows a modified cysteine protease mechanism involving a catalytic Cys210/His367 dyad, with the oxyanion hole to be a part of the "C-loop," and polarization of His367 by the formation of a strictly conserved water bridge with Glu173. The third residue has a dual role in catalysis as it mediates substrate binding and polarization of the catalytic dyad. A similar mechanism was identified for Cezanne-1, the paralogue of Cez2. In general, our simulations provide valuable molecular information that may help in the rational design of selective inhibitors of Cez2 and closely related enzymes.

Hypoxia increases methylated histones to prevent histone clipping and heterochromatin redistribution during Raf-induced senescence

Hypoxia enhances histone methylation by inhibiting oxygen- and α-ketoglutarate-dependent demethylases, resulting in increased methylated histones. This study reveals how hypoxia-induced methylation affects histone clipping and the reorganization of heterochromatin into senescence-associated heterochromatin foci (SAHF) during oncogene-induced senescence (OIS) in IMR90 human fibroblasts. Notably, using top-down proteomics, we discovered specific cleavage sites targeted by Cathepsin L (CTSL) in H3, H2B and H4 during Raf activation, identifying novel sites in H2B and H4. Hypoxia counteracts CTSL-mediated histone clipping by promoting methylation without affecting CTSL's activity. This increase in methylation under hypoxia protects against clipping, reshaping the epigenetic landscape and influencing chromatin accessibility, as shown by ATAC-seq analysis. These insights underscore the pivotal role of hypoxia-induced histone methylation in protecting chromatin from significant epigenetic shifts during cellular aging.

Exploring the impact of deleterious missense nonsynonymous single nucleotide polymorphisms in the DRD4 gene using computational approaches

Dopamine receptor D4 (DRD4) plays a vital role in regulating various physiological functions, including attention, impulse control, and sleep, as well as being associated with various neurological diseases, including attention deficit hyperactivity disorder, novelty seeking, and so on. However, a comprehensive analysis of harmful nonsynonymous single nucleotide polymorphisms (nsSNPs) of the DRD4 gene and their effects remains unexplored. The aim of this study is to uncover novel damaging missense nsSNPs and their structural and functional effects on the DRD4 receptor. From the dbSNP database, we found 677 nsSNPs, and then we analyzed their functional consequences, disease associations, and effects on protein stability with fifteen in silico tools. Five variants, including L65ICL1P (rs1459150721), V1163.33D (rs761875546), I1293.46S (rs751467198), I1564.46T (rs757732258), and F2015.47S (rs199609858), were identified as the most deleterious mutations that were also present in the conserved region and showed lower interactions with neighboring residues. To comprehensively understand their impact, we docked agonist dopamine and antagonist nemonapride at the binding site of the receptor, followed by 200 ns molecular dynamics simulations. We identified the V116D and I129S mutations as the most damaging, followed by F201S in the dopamine-bound states. Both the V116D and I129S variants demonstrated significantly high RMSD, Rg, and SASA, and low thermodynamic stability. The F201S-dopamine complex exhibited lower compactness and higher motions, along with a significant loss of hydrogen bonds and active site interactions. By contrast, while interacting with nemonapride, the impact of the I156T and L65P mutations was highly deleterious; both showed lower stability, higher flexibility, and higher motions. Additionally, nemonapride significantly lost interactions with the active site, notably in the I156T variant. We also found the V116D-nemonapride complex as structurally damaging; however, the interaction patterns of nemonapride were less altered in the MMPBSA analysis. Overall, this study revealed five novel deleterious variants along with a comprehensive understanding of their effect in the presence of an agonist and antagonist, which could be helpful for understanding disease susceptibility, precision medicine, and developing potential drugs.

In Vitro Functional Validation of an Anti-FREM2 Nanobody for Glioblastoma Cell Targeting

Background/Objectives: Glioblastomas are the most common brain malignancies. Despite the implementation of multimodal therapy, patient life expectancy after diagnosis is barely 12 to 18 months. Glioblastomas are highly heterogeneous at the genetic and epigenetic level and comprise multiple different cell subpopulations. Therefore, small molecules such as nanobodies, able to target membrane proteins specific to glioblastoma cells or specific cell types within the tumor are being investigated as novel tools to treat glioblastomas. Methods: Here, we describe the identification of such a nanobody and its in silico and in vitro validation. NB3F18, as we named it, is directed against the membrane-associated protein FREM2, overexpressed in glioblastoma stem cells. Results: Three dimensional in silico modeling indicated that NB3F18 and FREM2 form a stable complex. Surface plasmon resonance confirmed their interaction with moderate affinity. As we demonstrated by flow cytometry, NB3F18 binds to glioblastoma stem cells to a greater extent than to differentiated glioblastoma cells and astrocytes. Immunocytochemistry revealed surface localization of NB3F18 on glioblastoma stem cells, whereas cytoplasmic localization of NB3F18 was observed in other cell lines. NB3F18 was detected by transmission electron microscopy on the plasma membrane and in various compartments of the endocytic pathway, from endocytic vesicles to multivesicular bodies (endosomes) and lysosomes. Interestingly, NB3F18 was cytotoxic to glioblastoma stem cells. Conclusions: Collectively, NB3F18 has been qualified as an interesting tool to target glioblastoma cells and as a potential vehicle to deliver biological or pharmaceutical agents to these cells.

Structure Characterization of a Disordered Peptide Using In-Droplet Hydrogen/Deuterium Exchange Mass Spectrometry and Molecular Dynamics

In-droplet hydrogen/deuterium exchange (HDX)-mass spectrometry (MS) experiments have been conducted for peptides of highly varied conformational type. A new model is presented that combines the use of protection factors (PF) from molecular dynamics (MD) simulations with intrinsic HDX rates (k int) to obtain a structure-to-reactivity calibration curve. Using the model, the relationship of peptide structural flexibility and HDX reactivity for different peptides is elucidated. Additionally, the model is used to describe the degree of conformational flexibility and structural bias for the disease-relevant Nt17 peptide; although highly flexible, intrinsically primed for facile conversion to α-helical conformation upon binding with molecular partners imparts significant in-droplet HDX protection for this peptide. In the future, a scale may be developed whereby HDX reactivity is predictive of the degree of structural flexibility and bias (propensity to form 2° structural elements such as α-helix, β-sheet, and β-turn) for intrinsically disordered regions (IDRs). Such structural resolution may ultimately be used for high-throughput screening of IDR structural transformation(s) upon binding of ligands such as drug candidates.

Balancing enthalpy and entropy in inhibitor binding to the prostate-specific membrane antigen (PSMA)

Understanding the molecular mechanism of inhibitor binding to prostate-specific membrane antigen (PSMA) is of fundamental importance for designing targeted drugs for prostate cancer. Here we designed a series of PSMA-targeting inhibitors with distinct molecular structures, which were synthesized and characterized using both experimental and computational approaches. Microsecond molecular dynamics simulations revealed the structural and thermodynamic details of PSMA-inhibitor interactions. Our findings emphasize the pivotal role of the inhibitor's P1 region in modulating binding affinity and selectivity and shed light on the binding-induced conformational shifts of two key loops (the entrance lid and the interface loop). Binding energy calculations demonstrate the enthalpy-entropy balance in the thermodynamic driving force of different inhibitors. The binding of inhibitors in monomeric form is entropy-driven, in which the solvation entropy from the binding-induced water restraints plays a key role, while the binding of inhibitors in dimeric form is enthalpy-driven, due to the promiscuous PSMA-inhibitor interactions. These insights into the molecular driving force of protein-ligand binding offer valuable guidance for rational drug design.

Evaluating cutinase from Fusarium oxysporum as a biocatalyst for the degradation of nine synthetic polymer

Plastic poses a significant environmental impact due to its chemical resilience, leading to prolonged and degradation times and resulting in widespread adverse effects on global flora and fauna. Cutinases are essential enzymes in the biodegradation process of synthetic polymers like polyethylene terephthalate (PET), which recognized organisms can break down. Here, we used molecular dynamics and binding free energy calculations to explore the interaction of nine synthetic polymers, including PET, with Cutinase from Fusarium oxysporum (FoCut). According to our findings, the polymers poly(ethylene terephthalate) (PET), poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH), poly(butylene succinate) (PBS), poly(butylene adipate-co-terephthalate) (PBAT) and poly(ε-caprolactone) (PCL) can bind to the Cutinase enzyme from F. oxysporum, indicating potential biodegradation activity for these polymers. PET exhibited the highest binding affinity (- 34.26 kcal/mol). Besides PET, the polymers PHBH, PBS, PBAT, and PCL also demonstrated significant affinities for the FoCut enzyme, with binding values of - 18.44, - 29.71, - 22.78, and - 22.26 kcal/mol, respectively. Additionally, analysis of the phylogenetic tree of cutinases produced by different organisms demonstrated that even though the organisms belong to different kingdoms, the cutinase from F. oxysporum (FoCut) showed biological similarity in its activity in degrading polymers with the cutinase enzyme from the bacterium Kineococcus radiotolerans and the fungus Moniliophthora roreri. Furthermore, the phylogenetic analysis demonstrated that the PETase enzyme has a very high similarity with the bacterial cutinase enzyme than with the fungal cutinase, therefore demonstrating that the PETase enzyme from Ideonella sakaiensis can easily be a modified bacterial cutinase enzyme that created a unique feature in biodegrading only the pet polymer through an evolutionary process due to its environment and its biochemical need for carbon. Our data demonstrate that bacterial cutinase enzymes have the same common ancestor as the PETase enzyme. Therefore, cutinases and PETase are interconnected through their biological similarity in biodegrading polymers. We demonstrated that important conserved regions, such as the Ser-Asp-His catalytic triad, exist in the enzyme's catalytic site and that all Cut enzymes from different organisms have the same region to couple with the polymer structures.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Toxic Effects of Butanol in the Plane of the Cell Membrane

Solvent toxicity limits n-butanol fermentation titer, increasing the cost and energy consumption for subsequent separation processes and making biobased production more expensive and energy-intensive than petrochemical approaches. Amphiphilic solvents such as n-butanol partition into the cell membrane of fermenting microorganisms, thinning the transverse structure, and eventually causing a loss of membrane potential and cell death. In this work, we demonstrate the deleterious effects of n-butanol partitioning upon the lateral dimension of the membrane structure, called membrane domains or lipid rafts. Lipid rafts are regions of the cell membrane enriched with certain lipids, providing a reservoir of high melting temperature lipids and a platform for membrane protein partitioning and oligomerization. Neutron scattering experiments and molecular dynamics simulations revealed that n-butanol increased the size of the lipid domains in a model membrane system. The data showed that n-butanol partitions more into the disordered lipid regions than into the raft-like phase, leading to a differential thinning of these coexisting phases in the plane of the membrane and increasing the hydrophobic mismatch. The resulting increase in line tension at the interface favors domain coalescence to minimize the ratio of the interfacial length to domain area. A detailed computational investigation of the lipid domain interface identifies the boundary as a site of membrane disorder and thinning due to an accumulation of n-butanol. Solvent-induced changes to domain morphology and membrane instability at the domain interface are unrecognized modes of solvent-induced stress to fermenting microbes, representing targets for new solvent tolerance strategies to increase the n-butanol titer.

Biophysical Properties of Somatic Cancer Mutations in the S4 Transmembrane Segment of the Human Voltage-Gated Proton Channel hHV1

Somatic mutations are common in cancer, with only a few driving the progression of the disease, while most are silent passengers. Some mutations may hinder or even reverse cancer progression. The voltage-gated proton channel (HV1) plays a key role in cellular pH homeostasis and shows increased expression in several malignancies. Inhibiting HV1 in cancer cells reduces invasion, migration, proton extrusion, and pH recovery, impacting tumor progression. Focusing on HVCN1, the gene coding for the human voltage-gated proton channel (hHV1), 197 mutations were identified from three databases: 134 missense mutations, 51 sense mutations, and 12 introducing stop codons. These mutations cluster in two hotspots: the central region of the N-terminus and the region coding for the S4 transmembrane domain, which contains the channel's voltage sensor. Five somatic mutations within the S4 segment (R205W, R208W, R208Q, G215E, and G215R) were selected for electrophysiological analysis and MD simulations. The findings reveal that while all mutants remain proton-selective, they all exhibit reduced effective charge displacement and proton conduction. The mutations differentially affect hHV1 kinetics, with the most pronounced effects observed in the two Arg-to-Trp substitutions. Mutation of the first voltage-sensing arginine (R1) to tryptophan (R205W) causes proton leakage in the closed state, accelerates channel activation, and diminishes the voltage dependence of gating. Except for R205W, the mutations promote the deactivated channel configuration. Altogether, these data are consistent with impairment of hHV1 function by mutations in the S4 transmembrane segment, potentially affecting pH homeostasis of tumor cells.

Screening of Insertion Sites and Tags on EV-A71 VP1 Protein for Recombinant Virus Construction

This study aimed to create a new recombinant virus by modifying the EV-A71 capsid protein, serving as a useful tool and model for studying human Enteroviruses. We developed a new screening method using EV-A71 pseudovirus particles to systematically identify suitable insertion sites and tag types in the VP1 capsid protein. The pseudovirus's infectivity and replication can be assessed by measuring postinfection luciferase signals. We reported that the site after the 100th amino acid within the VP1 BC loop of EV-A71 is particularly permissive for the insertion of various tags. Notably, the introduction of S and V5 tags at this position had minimal effect on the fitness of the tagged pseudovirus. Furthermore, recombinant infectious EV-A71 strains tagged with S and V5 epitopes were successfully rescued, and the stability of these tags was verified. Computational analysis suggested that viable insertions should be compatible with capsid assembly and receptor binding, whereas non-viable insertions could potentially disrupt the capsid's binding with heparan sulfate. We expect the tagged recombinant EV-A71 to be a useful tool for studying the various stages of the enterovirus life cycle and for virus purification, immunoprecipitation, and research in immunology and vaccine development. Furthermore, this study serves as a proof of principle and may help develop similar tags in enteroviruses, for which there are fewer available tools.

Phospholipid Transport Across the Bacterial Periplasm Through the Envelope-spanning Bridge YhdP

The outer membrane of Gram-negative bacteria provides a formidable barrier, essential for both pathogenesis and antimicrobial resistance. Biogenesis of this complex structure necessitates the transport of phospholipids across the cell envelope. Recently, YhdP was implicated as a major protagonist in the trafficking of inner membrane phospholipids to the outer membrane; however the molecular mechanism of YhdP mediated transport remains elusive. Here, utilising AlphaFold, we observe YhdP to form an elongated assembly of 60 β-strands that curve to form a continuous hydrophobic groove. This architecture is consistent with our negative stain electron microscopy data which reveals YhdP to be approximately 250 Å in length and thus sufficient to span the bacterial cell envelope. Furthermore, molecular dynamics simulations and bacterial growth assays indicate essential helical regions at the N- and C-termini of YhdP, that may embed into the inner and outer membranes respectively, reinforcing its envelope spanning nature. Our in vivo crosslinking data reveal phosphate-containing substrates captured along the length of the YhdP groove, providing direct evidence that YhdP interacts with a phosphate-containing substrate, which we propose to be phospholipids. This finding is congruent with our molecular dynamics simulations which demonstrate the propensity for inner membrane lipids to spontaneously enter the groove of YhdP. Collectively, our results support a model in which YhdP bridges the cell envelope, providing a hydrophobic environment for the transport of phospholipids to the outer membrane.

Computational epitope-based vaccine design with bioinformatics approach; a review

The significance of vaccine development has gained heightened importance in light of the COVID-19 pandemic. In such critical circumstances, global citizens anticipate researchers in this field to swiftly identify a vaccine candidate to combat the pandemic's root cause. It is widely recognized that the vaccine design process is traditionally both time-consuming and costly. However, a specialized subfield within bioinformatics, known as "multi-epitope vaccine design" or "reverse vaccinology," has significantly decreased the time and costs of the vaccine design process. The methodology reverses itself in this subfield and finds a potential vaccine candidate by analyzing the pathogen's genome. Leveraging the tools available in this domain, we strive to pinpoint the most suitable antigen for crafting a vaccine against our target. Once the optimal antigen is identified, the next step involves uncovering epitopes within this antigen. The immune system recognizes particular areas of an antigen as epitopes. By characterizing these crucial segments, we gain the opportunity to design a vaccine centered around these epitopes. Subsequently, after identifying and assembling the vital epitopes with the assistance of linkers and adjuvants, our vaccine candidate can be formulated. Finally, employing computational techniques, we can thoroughly evaluate the designed vaccine. This review article comprehensively covers the entire multi-epitope vaccine development process, starting from obtaining the pathogen's genome to identifying the relevant vaccine candidate and concluding with an evaluation. Furthermore, we will delve into the essential tools needed at each stage, comparing and introducing them.

Validation of a Coarse-Grained Martini 3 Model for Molecular Oxygen

Molecular oxygen (O2) is essential for life, and continuous effort has been made to understand its pathways in cellular respiration with all-atom (AA) molecular dynamics (MD) simulations of, e.g., membrane permeation or binding to proteins. To reach larger length scales with models, such as curved membranes in mitochondria or caveolae, coarse-grained (CG) simulations could be used at much lower computational cost than AA simulations. Yet a CG model for O2 is lacking. In this work, a CG model for O2 is therefore carefully selected from the Martini 3 force field based on criteria including size, zero charge, nonpolarity, solubility in nonpolar organic solvents, and partitioning in a phospholipid membrane. This chosen CG model for O2 (TC3 bead) is then further evaluated through the calculation of its diffusion constant in water and hexadecane, its permeability rate across pure phospholipid- and cholesterol-containing membranes, and its binding to the T4 lysozyme L99A protein. Our CG model shows semiquantitative agreement between CG diffusivity and permeation rates with the corresponding AA values and available experimental data. Additionally, it captures the binding to hydrophobic cavities of the protein, aligning well with the AA simulation of the same system. Thus, the results show that our O2 model approximates the behavior observed in the AA simulations. The CG O2 model is compatible with the widely used multifunctional Martini 3 force field for biological simulations, which will allow for the simulation of large biomolecular systems involved in O2's transport in the body.

Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations

Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

Structural and Electronic Properties of Poly(ethylene terephthalate) (PET) from Polarizable Molecular Dynamics Simulations

The environmental and economic challenges posed by the widespread use and disposal of plastics, particularly poly(ethylene terephthalate) (PET), require innovative solutions to mitigate their impact. Such mitigation begins with understanding physical properties of the polymer that could enable new recycling technologies. Although molecular simulations have provided valuable insights into PET interactions with various PET hydrolases, current nonpolarizable force fields neglect the electronic polarization effects inherent to PET interactions. Here, we present parameters for PET polymer and its derivatives that are compatible with the Drude polarizable force field. Our parameter fitting protocol accurately reproduces electrostatic properties from quantum mechanical calculations. We then studied electronic properties of PET amorphous slabs and PET crystal units, revealing a crucial electronic polarization response of PET residues at the interface with water or vacuum, yielding insights into the modulation of electrostatic properties by solvent molecules. Finally, we showcase the interaction between a carbohydrate-binding protein and the PET crystal unit, revealing the role of electronic polarization in enhancing binding affinity. This study represents the first extension of the Drude polarizable force field to a synthetic polymer, offering a robust tool for exploring PET material properties and advancing the design of efficient (bio)technologies for addressing plastic pollution.

Apoptosis-Inducing Activity of a 2-Hydroxyphenyl Benzamide-Based Self-Assembled Anion Channel

Despite the significant interest in designing artificial ion channels, there is limited availability of channel-forming molecules to tackle complex issues, especially in biological systems. Moreover, a major challenge is the scarcity of chloride transporters that can selectively induce toxicity in cancer cells while minimizing harm to normal healthy cells. This work reports a series of 2-hydroxyphenyl benzamide-based small molecules 1 a-1 c, which self-assemble to form barrel rosette-type artificial ion channels that adequately transport chloride ions across membranes. The formation of these ion channels primarily relies on intermolecular hydrogen bonding and π-π stacking interactions, as supported by the analysis of single-crystal X-ray diffraction and molecular dynamics (MD) simulations. Importantly, chloride ion transport by these compounds specifically triggers apoptosis in cancer cells while demonstrating relatively low toxicity toward non-cancerous cell lines.

Towards New Anti-Inflammatory Agents: Design, Synthesis and Evaluation of Molecules Targeting XIAP-BIR2

The X-chromosome-linked inhibitor of apoptosis protein (XIAP) plays a crucial role in controlling cell survival across multiple regulated cell death pathways and coordinating a range of inflammatory signalling events. The discovery of selective inhibitors for XIAP-BIR2, able to disrupt the direct physical interaction between XIAP and RIPK2, offer promising therapeutic options for NOD2-mediated diseases like Crohn's disease, sarcoidosis, and Blau syndrome. The objective of this study was to design, synthesize, and evaluate small synthetic molecules with binding selectivity to XIAP-BIR2 domain. To achieve this, we applied an interdisciplinary drug design approach and firstly we have synthesized an initial fragment library to achieve a first XIAP inhibition activity. Then using a growing strategy, larger compounds were synthesized and one of them presents a good selectivity for XIAP-BIR2 versus XIAP-BIR3 domain, compound 20 c. The ability of compound 20 c to block the NOD1/2 pathway was confirmed in cell models. These data show that we have synthesized molecules capable of blocking NOD1/2 signalling pathways in cellulo, and ultimately leading to new anti-inflammatory compounds.

Ranking Single Fluorescent Protein-Based Calcium Biosensor Performance by Molecular Dynamics Simulations

Genetically encoded fluorescent biosensors (GEFBs) have become indispensable tools for visualizing biological processes in vivo. A typical GEFB is composed of a sensory domain (SD) that undergoes a conformational change upon ligand binding or enzymatic reaction; the SD is genetically fused with a fluorescent protein (FP). The changes in the SD allosterically modulate the chromophore environment whose spectral properties are changed. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable, and simple to use due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD, which affect how well the chromophore responds to conformational change in the SD. In this work, we use all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding-dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.

Minimalist Natural ORPphilin Macarangin B Delineates OSBP Biological Function

OSBP ligands from the ORPphilin family are chemically complex natural products with promising anticancer properties. Here, we describe macarangin B, a natural racemic flavonoid selective for OSBP, which stands out from other ORPphilins due to its structural simplicity and distinct biological activity. Using a bioinspired strategy, we synthesized both (R,R,R) and (S,S,S)-macarangin B enantiomers, enabling us to study their interaction with OSBP based on their unique optical properties. Experimental and computational analyzes revealed that (R,R,R)-macarangin B has the highest affinity for OSBP. Importantly, both enantiomers showed significantly decreased cytotoxicity compared to other ORPphilins, suggesting OSBP is not the primary target in ORPphilin-induced cell death. Yet, OSBP is an attractive antiviral target, as it is hijacked by many positive-strand RNA viruses. Remarkably, (R,R,R)-macarangin B significantly inhibited Zika virus replication in human cells, highlighting its potential as a lead compound for antiviral drug development.

Investigating How Lysophosphatidylcholine and Lysophosphatidylethanolamine Enhance the Membrane Permeabilization Efficacy of Host Defense Peptide Piscidin 1

Lysophospholipids (LPLs) and host defense peptides (HDPs) are naturally occurring membrane-active agents that disrupt key membrane properties, including the hydrocarbon thickness, intrinsic curvature, and molecular packing. Although the membrane activity of these agents has been widely examined separately, their combined effects are largely unexplored. Here, we use experimental and computational tools to investigate how lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE), an LPL of lower positive spontaneous curvature, influence the membrane activity of piscidin 1 (P1), an α-helical HDP from fish. Four membrane systems are probed: 75:25 C16:0-C18:1 PC (POPC)/C16:0-C18:1 phosphoglycerol (POPG), 50:25:25 POPC/POPG/16:0 LPC, 75:25 C16:0-C18:1 PE (POPE)/POPG, and 50:25:25 POPE/POPG/14:0 LPE. Dye leakage, circular dichroism, and NMR experiments demonstrate that while the presence of LPLs alone does not induce leakage-proficient defects, it boosts the permeabilization capability of P1, resulting in an efficacy order of POPC/POPG/16:0 LPC > POPE/POPG/14:0 LPE > POPC/POPG > POPE/POPG. This enhancement occurs without altering the membrane affinity and conformation of P1. Molecular dynamics simulations feature two types of asymmetric membranes to represent the imbalanced ("area stressed") and balanced ("area relaxed") distribution of lipids and peptides in the two leaflets. The simulations capture the membrane thinning effects of P1, LPC, and LPE, and the positive curvature strain imposed by both LPLs is reflected in the lateral pressure profiles. They also reveal a higher number of membrane defects for the P1/LPC than P1/LPE combination, congruent with the permeabilization experiments. Altogether, these results show that P1 and LPLs disrupt membranes in a concerted fashion, with LPC, the more disruptive LPL, boosting the permeabilization of P1 more than LPE. This mechanistic knowledge is relevant to understanding biological processes where multiple membrane-active agents such as HDPs and LPLs are involved.

Structural basis of a microbial trimethylamine transporter

Trimethylamine (TMA), a simple trace biogenic amine resulting from the decomposition of proteins and other macromolecules, is ubiquitous in nature. It is found in the human gut as well as in various terrestrial and marine ecosystems. While the role of TMA in promoting cardiovascular diseases and depolarizing olfactory sensory neurons in humans has only recently been explored, many microbes are well known for their ability to utilize TMA as a carbon, nitrogen, and energy source. Here, we report the first structure of a TMA transporter, TmaT, originally identified from a marine bacterium. TmaT is a member of the betaine-choline-carnitine transporter family, and we show that TmaT is an Na+/TMA symporter, which possessed high specificity and binding affinity toward TMA. Furthermore, the structures of TmaT and two TmaT-TMA complexes were solved by cryo-EM. TmaT forms a homotrimer structure in solution. Each TmaT monomer has 12 transmembrane helices, and the TMA transport channel is formed by a four-helix bundle. TMA can move between different aromatic boxes, which provides the structural basis of TmaT importing TMA. When TMA is bound in location I, residues Trp146, Trp151, Tyr154, and Trp326 form an aromatic box to accommodate TMA. Moreover, Met105 also plays an important role in the binding of TMA. When TMA is transferred to location II, it is bound in the aromatic box formed by Trp325, Trp326, and Trp329. Based on our results, we proposed the TMA transport mechanism by TmaT. This study provides novel insights into TMA transport across biological membranes.

IMPORTANCE

The volatile trimethylamine (TMA) plays an important role in promoting cardiovascular diseases and depolarizing olfactory sensory neurons in humans and serves as a key nutrient source for a variety of ubiquitous marine microbes. While the TMA transporter TmaT has been identified from a marine bacterium, the structure of TmaT and the molecular mechanism involved in TMA transport remain unclear. In this study, we elucidated the high-resolution cryo-EM structures of TmaT and TmaT-TMA complexes and revealed the TMA binding and transport mechanisms by structural and biochemical analyses. The results advance our understanding of the TMA transport processes across biological membranes.

Tracking flaviviral protease conformational dynamics by tuning single-molecule nanopore tweezers

The flaviviral NS2B/NS3 protease is a conserved enzyme required for flavivirus replication. Its highly dynamic conformation poses major challenges but also offers opportunities for antiviral inhibition. Here, we established a nanopore tweezers-based platform to monitor NS2B/NS3 conformational dynamics in real time. Molecular simulations coupled with single-channel current recording measurements revealed that the protease could be captured in the middle of the ClyA nanopore lumen, stabilized mainly by dynamic electrostatic interactions. We designed a new Salmonella typhi ClyA nanopore with enhanced nanopore/protease interaction that can resolve the open and closed states at the single-molecule level for the first time. We demonstrated that the tailored ClyA could track the conformational transitions of the West Nile NS2B/NS3 protease and unravel the conformational energy landscape of various protease constructs through population and kinetic analysis. The new ClyA-protease platform paves a way to search for new allosteric inhibitors that target the NS2B and NS3 interface.

Sickle Trait and Alpha Thalassemia Increase NOS-Dependent Vasodilation of Human Arteries Through Disruption of Endothelial Hemoglobin-eNOS Interactions

BACKGROUND

Severe malaria is associated with impaired nitric oxide (NO) synthase (NOS)-dependent vasodilation, and reversal of this deficit improves survival in murine models. Malaria might have selected for genetic polymorphisms that increase endothelial NO signaling and now contribute to heterogeneity in vascular function among humans. One protein potentially selected for is alpha globin, which, in mouse models, interacts with endothelial NOS (eNOS) to negatively regulate NO signaling. We sought to evaluate the impact of alpha globin gene deletions on NO signaling and unexpectedly found human arteries use not only alpha but also beta globin to regulate eNOS.

METHODS

The eNOS-hemoglobin complex was characterized by multiphoton imaging, gene expression analysis, and coimmunoprecipitation studies of human resistance arteries. Novel contacts between eNOS and hemoglobin were mapped using molecular modeling and simulation. Pharmacological or genetic disruption of the eNOS-hemoglobin complex was evaluated using pressure myography. The association between alpha globin gene deletion and blood pressure was assessed in a population study.

RESULTS

Alpha and beta globin transcripts were detected in the endothelial layer of the artery wall. Imaging colocalized alpha and beta globin proteins with eNOS at myoendothelial junctions. Immunoprecipitation demonstrated that alpha globin and beta globin form a complex with eNOS and cytochrome b5 reductase. Modeling predicted negatively charged glutamic acids at positions 6 and 7 of beta globin to interact with positively charged arginines at positions 97 and 98 of eNOS. Arteries from donors with a glutamic acid-to-valine substitution at beta globin position 6 (sickle trait) exhibited increased NOS-dependent vasodilation. Alpha globin gene deletions were associated with decreased arterial alpha globin expression, increased NOS-dependent vasodilation, and lower blood pressure. Mimetic peptides that targeted the interactions between hemoglobin and eNOS recapitulated the effects of these genetic variants on human arterial vasoreactivity.

CONCLUSIONS

Alpha and beta globin subunits of hemoglobin interact with eNOS to restrict NO signaling in human resistance arteries. Malaria-protective genetic variants that alter the expression of alpha globin or the structure of beta globin are associated with increased NOS-dependent vasodilation. Targeting the hemoglobin-eNOS interface could potentially improve NO signaling in diseases of endothelial dysfunction such as severe malaria or chronic cardiovascular conditions.

Flavuside B exhibits antioxidant and anti-inflammatory properties in Staphylococcus aureus infected skin wound and affect the expression of genes controlling bacterial quorum sensing

AIMS

The aim of this study was to evaluate the antioxidant and anti-inflammatory effects of marine fungal cerebroside flavuside B (FlaB) on Staphylococcus aureus-infected keratinocytes in in vitro skin wounds and to identify FlaB targets in bacterial and human cells.

METHODS AND RESULTS

A combination of enzyme-linked immunosorbent assay (ELISA), plate spectrofluorimetry, and flow cytometry with fluorescence dye staining, scratch assay, and real-time cell imaging techniques was used to investigate the effects of FlaB on S. aureus-infected HaCaT keratinocytes. FlaB decreased reactive oxygen species levels, nitrite oxide levels, and TNF-α and IL-18 release in S. aureus-infected HaCaT cells. FlaB reversed the inhibition of HaCaT cell proliferation caused by S. aureus infection. FlaB significantly increased keratinocyte migration and wound healing in an in vitro S. aureus-infected wound skin model. Using real-time qPCR, we found that FlaB caused a 1.7-fold reduction in agrA expression, which controls quorum sensing system in S. aureus. Bioinformatics analysis and molecular docking, together with experimental data, suggest that FlaB targets the pro/antioxidant defense system in human cells.

CONCLUSIONS

Thus, FlaB can play a dual role as an antibacterial and pro/antioxidant machinery modulator, providing an observable positive effect in S. aureus-infected in vitro skin wounds. Staphylococcal sortase A enzyme and Arg systems are the targets of FlaB in bacterial cells. Nrf2/Bach1 dependent pro/antioxidant defense system is a target of FlaB in human cells. Some suggestions have also been made regarding the biological role of this marine fungal metabolite and its therapeutic possibilities.

High-throughput molecular simulations of SARS-CoV-2 receptor binding domain mutants quantify correlations between dynamic fluctuations and protein expression

Prediction of protein fitness from computational modeling is an area of active research in rational protein design. Here, we investigated whether protein fluctuations computed from molecular dynamics simulations can be used to predict the expression levels of SARS-CoV-2 receptor binding domain (RBD) mutants determined in the deep mutational scanning experiment of Starr et al. [Science (New York, N.Y.) 2022, 377, 420] Specifically, we performed more than 0.7 milliseconds of molecular dynamics (MD) simulations of 557 mutant RBDs in triplicate to achieve statistical significance under various simulation conditions. Our results show modest but significant anticorrelation in the range [-0.4, -0.3] between expression and RBD protein flexibility. A simple linear regression machine learning model achieved correlation coefficients in the range [0.7, 0.8], thus outperforming MD-based models, but required about 25 mutations at each residue position for training.

Additive CHARMM Force Field for Pterins and Folates

Folates comprise a crucial class of biologically active compounds related to folic acid, playing a vital role in numerous enzymatic reactions. One-carbon metabolism, facilitated by the folate cofactor, supports numerous physiological processes, including biosynthesis, amino acid homeostasis, epigenetic maintenance, and redox defense. Folates share a common pterin heterocyclic ring structure capable of undergoing redox reactions and existing in various protonation states. This study aimed to derive molecular mechanics (MM) parameters compatible with the CHARMM36 all-atom additive force field for pterins and biologically important folates, including pterin, biopterin, and folic acid. Three redox forms were considered: oxidized, dihydrofolate, and tetrahydrofolate states. Across all protonation states, a total of 18 folates were parameterized. Partial charges were derived using the CHARMM force field parametrization protocol, based on targeting reference quantum mechanics monohydrate interactions, electrostatic potential, and dipole moment. Bonded terms were parameterized using one-dimensional adiabatic potential energy surface scans, and two-dimensional scans to parametrize in-ring torsions associated with the puckering states of dihydropterin and tetrahydropterin. The quality of the model was demonstrated through simulations of three protein complexes using optimized and initial parameters. These simulations underscored the significantly enhanced performance of the folate model developed in this study compared to the initial model without optimization in reproducing structural properties of folate-protein complexes. Overall, the presented MM model will be valuable for modeling folates in various redox states and serve as a starting point for parameterizing other folate derivatives.

The molecular reach of antibodies crucially underpins their viral neutralisation capacity

Key functions of antibodies, such as viral neutralisation, depend on high-affinity binding. However, viral neutralisation poorly correlates with antigen affinity for reasons that have been unclear. Here, we use a new mechanistic model of bivalent binding to study  >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. The model provides the standard monovalent affinity/kinetics and new bivalent parameters, including the molecular reach: the maximum antigen separation enabling bivalent binding. We find large variations in these parameters across antibodies, including reach variations (22-46 nm) that exceed the physical antibody size (~15 nm). By using antigens of different physical sizes, we show that these large molecular reaches are the result of both the antibody and antigen sizes. Although viral neutralisation correlates poorly with affinity, a striking correlation is observed with molecular reach. Indeed, the molecular reach explains differences in neutralisation for antibodies binding with the same affinity to the same RBD-epitope. Thus, antibodies within an isotype class binding the same antigen can display differences in molecular reach, substantially modulating their binding and functional properties.

Secondary Structure in Free and Assisted Modeling of Proteins with the Coarse-Grained UNRES Force Field

Secondary structure is a solid scaffold on which the three-dimensional structure of a protein is built. Therefore, care must be taken to reproduce the secondary structure as accurately as possible in the simulations of protein systems. In this chapter, we summarize the physics-based energy terms that govern secondary-structure formation, the auxiliary restraints on secondary structure derived from bioinformatics and from the experimental data, and the role of those in the modeling of protein structures, dynamics, and thermodynamics with the physics-based coarse-grained UNRES force field. Examples illustrating the methodology discussed and further directions of development are presented.

Insights into an indolicidin-derived low-toxic anti-microbial peptide's efficacy against bacterial cells while preserving eukaryotic cell viability

Antimicrobial peptides (AMPs) are a current solution to combat antibiotic resistance, but they have limitations, including their expensive production process and the induction of cytotoxic effects. We have developed novel AMP candidate (peptide 3.1) based on indolicidin, among the shortest naturally occurring AMP. The antimicrobial activity of this peptide is demonstrated by the minimum inhibitory concentration, while the hemolysis tests and MTT assay indicate its low cytotoxicity. In optical diffraction tomography, red blood cells treated with peptide 3.1 showed no discernible effects, in contrast to indolicidin. However, peptide 3.1 did induce cell lysis in E. coli, leading to a reduced potential for the development of antibiotic resistance. To investigate the mechanism underlying membrane selectivity, the structure of peptide 3.1 was analyzed using nuclear magnetic resonance spectroscopy and molecular dynamics simulations. Peptide 3.1 is structured with an increased distinction between hydrophobic and charged residues and remained in close proximity to the eukaryotic membrane. On the other hand, peptide 3.1 exhibited a disordered conformation when approaching the prokaryotic membrane, similar to indolicidin, leading to its penetration into the membrane. Consequently, it appears that the amphipathicity and structural rigidity of peptide 3.1 contribute to its membrane selectivity. In conclusion, this study may lead to the development of Peptide 3.1, a promising commercial candidate based on its low cost to produce and low cytotoxicity. We have also shed light on the mechanism of action of AMP, which exhibits selective toxicity to bacteria while not damaging eukaryotic cells.