Key residues in TLR4-MD2 tetramer formation identified by free energy simulations

Identification and Molecular Mechanism of Novel Immunomodulatory Peptides from Gelatin Hydrolysates: Molecular Docking, Dynamic Simulation, and Cell Experiments

The purpose of this study was to identify donkey-hide gelatin-derived immunomodulatory peptides targeting Toll-like receptor 4-myeloid differentiation 2 (TLR4-MD2) and elucidate their binding modes using physicochemical property prediction, molecular docking, molecular dynamics simulations, and in vitro cell experiments. After hydrolyzing gelatin, 519 peptides were identified by liquid chromatography-tandem mass spectrometry. Peptides VQLSGEEK and GFSGLDGAKG bound to TLR4-MD2 with high binding affinity. In TLR4-MD2, Arg90, Ser118, Phe126, Tyr131, and Arg264 were key residues involved in the binding of these peptides. The RMSD and R_g values demonstrated that VQLSGEEK-TLR4-MD2 and GFSGLDGAKG-TLR4-MD2 complexes had stable and compact conformations. VQLSGEEK and GFSGLDGAKG were found to increase the cell viability and phagocytic activity of RAW264.7 macrophages; significantly promote the production of cytokines TNF-α, IL-1β, and IL-6 in cells; and inhibit the overproduction of nitric oxide (NO) and cytokines in lipopolysaccharide (LPS)-induced RAW264.7 cells. Our results provided preliminary evidence that VQLSGEEK and GFSGLDGAKG could function as two-way immunomodulatory peptides with immunostimulatory and anti-inflammatory activities.

A drug repurposing endeavor to discover a multi-targeting ligand against RhlR and LasR proteins from opportunistic human pathogen Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic human pathogen. It synthesizes the poison called Hydrogen Cyanide (HCN). The synthesis of HCN is mediated by the enzyme HCN synthase which is obtained from the hcnABC operon and the transcription of the hcnABC operon is mediated by three proteins LasR, RhlR, and ANR. In our previous works, we analyzed the activation process of RhlR and LasR proteins by their cognate auto-inducer ligands (N-butanoyl-L-homoserine lactone and N-(3-oxododecanoyl)-homoserine lactone respectively). In this work, we attempted to identify some multi-targeting ligands which would be able to destroy the structural integrity of both the RhlR and LasR proteins using steered MD simulations. We used the virtual screening of ligand libraries, and for that purpose, we used the NCI drug database. We selected the top 4 ligands from our virtual screening experiments. We then tried to check their relative binding affinities with the LasR and RhlR proteins in comparison to their native auto-inducer ligands. Through this work, we were able to identify 4 such ligands which were capable of binding to both the RhlR and LasR proteins in a better way than their native auto-inducer ligands. The efficacies of these ligands to actually perturb the structural integrity of RhlR and LasR proteins could be tested in wet lab. The work is the first work in the field of structure-based drug design to come up with possible multi-targeting drug-like structures against the RhlR and LasR proteins from Pseudomonas aeruginosa.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

In silico investigation and surmounting of Lipopolysaccharide barrier in Gram-Negative Bacteria: How far has molecular dynamics Come?

Lipopolysaccharide (LPS), a main component of the outer membrane of Gram-negative bacteria, has crucial implications on both antibiotic resistance and the overstimulation of the host innate immune system. Fighting against these global concerns calls for the molecular understanding of the barrier function and immunostimulatory ability of LPS. Molecular dynamics (MD) simulations have become an invaluable tool for uncovering important findings in LPS research. While the reach of MD simulations for investigating the immunostimulatory ability of LPS has been already outlined, little attention has been paid to the role of MD simulations for exploring its barrier function and synthesis. Herein, we give an overview about the impact of MD simulations on gaining insight into the shield role and synthesis pathway of LPS, which have attracted considerable attention to discover molecules able to surmount antibiotic resistance, either circumventing LPS defenses or disrupting its synthesis. We specifically focus on the enhanced sampling and free energy calculation methods that have been combined with MD simulations to address such research. We also highlight the use of special-purpose MD supercomputers, the importance of appropriate LPS and ions parameterization to obtain reliable results, and the complementary views that MD and wet-lab experiments provide. Thereby, this work, which covers the last five years of research, apart from outlining the phenomena and strategies that are being explored, evidences the valuable insights that are gained by MD, which may be useful to advance antibiotic design, and what the prospects of this in silico method could be in LPS research.

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

Lipopolysaccharide (LPS) is the primary component of the outer leaflet of Gram-negative bacterial outer membranes. LPS elicits an overwhelming immune response during infection, which can lead to life-threatening sepsis or septic shock for which no suitable treatment is available so far. As a result of the worldwide expanding multidrug-resistant bacteria, the occurrence and frequency of sepsis are expected to increase; thus, there is an urge to develop novel strategies for treating bacterial infections. In this regard, gaining an in-depth understanding about the ability of LPS to both stimulate the host immune system and interact with several molecules is crucial for fighting against LPS-caused infections and allowing for the rational design of novel antisepsis drugs, vaccines and LPS sequestration and detection methods. Molecular dynamics (MD) simulations, which are understood as being a computational microscope, have proven to be of significant value to understand LPS-related phenomena, driving and optimizing experimental research studies. In this work, a comprehensive review on the methods that can be combined with MD simulations, recently applied in LPS research, is provided. We focus especially on both enhanced sampling methods, which enable the exploration of more complex systems and access to larger time scales, and free energy calculation approaches. Thereby, apart from outlining several strategies for surmounting LPS-caused infections, this work reports the current state-of-the-art of the methods applied with MD simulations for moving a step forward in the development of such strategies.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Aureusidin derivative CNQX inhibits chronic colitis inflammation and mucosal barrier damage by targeting myeloid differentiation 2 protein

Our previous study has found that aureusidin can inhibit inflammation by targeting myeloid differentiation 2 (MD2) protein. Structural optimization of aureusidin gave rise to a derivative named CNQX. LPS was used to induce inflammation in intestinal macrophages; flow cytometry, PI staining and Hoechst 33342 staining were used to detect the apoptotic level of macrophages; enzyme-linked immunosorbent assay (ELISA) was utilized to detect the expression level of inflammatory factors (including IL-1β, IL-18 and TNF-α); immunofluorescence staining was used to investigate the expression of MD2; Western blot was employed to measure the protein level of TLR4, MD2, MyD88 and p-P65. As a result, CNQX with IC50 of 2.5 μM can significantly inhibit the inflammatory damage of macrophages, decrease apoptotic level, reduce the expression level of inflammatory factors and simultaneously decrease the expression level of TLR4, MD2, MyD88 as well as p-P65. Caco-2 cell line was used to simulate the intestinal mucosal barrier in vitro, LPS was employed to induce cell injury in Caco-2 (to up-regulate barrier permeability), and CNQX with IC50 of 2.5 μl was used for intervention. Flow cytometry was used to detect the apoptotic level of Caco-2 cells, trans-epithelial electric resistance (TEER) was measured, FITC-D was used to detect the permeability of the intestinal mucosa, and Western blot was used to detect the expression levels of tight junction proteins (including occludin, claudin-1, MyD88, TLR4 and MD2). As a result, CNQX decreased the apoptotic level of Caco-2 cells, increased TEER value, decreased the expression levels of MyD88, TLR4 and MD2, and increased the protein levels of tight junction proteins (including occludin and claudin-1). C57BL/6 wild-type mice were treated with drinking water containing Dextran sulphate sodium (DSS) to establish murine chronic colitis model. After CQNX intervention, we detected the bodyweight, DAI score and H&E tissue staining to evaluate the life status and pathological changes. Immunohistochemistry (IHC) staining was used to detect the expression of MD2 protein, tight junction protein (including occludin and claudin-1). Transmission electron microscopy and FITC-D were used to detect intestinal mucosal permeability. Western blot was used to detect the expression levels of tight junction proteins (including occludin, claudin-1, MyD88, TLR4 and MD2) in the intestinal mucosa tissue. Consequently, CNQX can inhibit the intestinal inflammatory response in mice with colitis, inhibit the mucosal barrier injury, increase the expression of tight junction proteins (including occludin and claudin-1) and decrease the expression levels of MyD88, TLR4 and MD2. Mechanistically, pull-down and immunoprecipitation assays showed that CNQX can inhibit the activation of TLR4/MD2-NF-κB by binding to MD2 protein. Collectively, in this study, we found that CNQX can suppress the activation of TLR4 signals by targeting MD2 protein, thereby inhibiting inflammation and mucosal barrier damage of chronic colitis.

Structural Changes beyond the EF-Hand Contribute to Apparent Calcium Binding Affinities: Insights from Parvalbumins

Members of the parvalbumin (PV) family of calcium (Ca²⁺) binding proteins (CBPs) share a relatively high level of sequence similarity. However, their Ca²⁺ affinities and selectivities against competing ions like Mg²⁺ can widely vary. We conducted molecular dynamics simulations of several α-parvalbumin (αPV) constructs with micromolar to nanomolar Ca²⁺ affinities to identify structural and dynamic features that contribute to their binding of ions. Specifically, we examined a D94S/G98E construct with a lower Ca²⁺ affinity (≈-18 kcal/mol) relative to the wild type (WT) (≈-22 kcal/mol) and an S55D/E59D variant with enhanced affinity (≈-24 kcal/mol). Additionally, we also examined the binding of Mg²⁺ to these isoforms, which is much weaker than Ca²⁺. We used mean spherical approximation (MSA) theory to evaluate ion binding thermodynamics within the proteins' EF-hand domains to account for the impact of ions' finite sizes and the surrounding electrolyte composition. While the MSA scores differentiated Mg²⁺ from Ca²⁺, they did not indicate that Ca²⁺ binding affinities at the binding loop differed between the PV isoforms. Instead, molecular mechanics generalized Born surface area (MM/GBSA) approximation energies, which we used to quantify the thermodynamic cost of structural rearrangement of the proteins upon binding ions, indicated that S55D/E59D αPV favored Ca²⁺ binding by -20 kcal/mol relative to WT versus 30 kcal/mol for D94S/G98E αPV. Meanwhile, Mg²⁺ binding was favored for the S55D/E59D αPV and D94S/G98E αPV variants by -18.32 and -1.65 kcal/mol, respectively. These energies implicate significant contributions to ion binding beyond oxygen coordination at the binding loop, which stemmed from changes in α-helicity, β-sheet character, and hydrogen bonding. Hence, Ca²⁺ affinity and selectivity against Mg²⁺ are emergent properties stemming from both local effects within the proteins' ion binding sites as well as non-local contributions elsewhere. Our findings broaden our understanding of the molecular bases governing αPV ion binding that are likely shared by members of the broad family of CBPs.

TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer's disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.