Project description:In the severity of myocardial infarction, Toll-like receptor (TLR) 9 plays a pivotal role in the inflammatory responses induced through damage-associated molecular patterns involving mitochondrial DNA and HMGB1. However, the therapeutic agents currently available for myocardial infarction substantially lack any association with the effects on immune responses. In this study, we applied a comprehensive drug discovery approach, which integrates in silico, in vitro, and in vivo processes, as well as determined therapeutic effects and mechanisms free from side effects. In an animal model of myocardial infarction, conditioned based on pharmacological properties, improved effects compared to the FDA-approved Rosuvastatin were detected. In summary, a small molecular inhibitor, ETA53 (endosomal Toll-like receptor antagonist 53), which selectively acts on TLR9 in nano-molar units, was developed. ETA53 was found to be effective for treating myocardial infarction caused by permanent ligation of the left anterior descending coronary artery, based on indicators such as cardiac function, inflammation, infarct size, and fibrosis. The results offered insights that varied from those of conventional drug development perspectives; consequently, the novel inhibitor may provide an alternative treatment with a mechanism different from that of the commercially available drugs for myocardial infarction.
Project description:Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
Project description:The synthesis of polymeric nanoparticles (NPs) with efficient drug loading content and targeting moieties is an attractive field and remains a challenge in drug delivery systems. Atomistic investigations can provide an in-depth understanding of delivery devices and reduce the number of expensive experiments. In this paper, we studied the self-assembly of poly (lactic-co-glycolic acid)-b-poly (ethylene glycol) with different molecular weights and surface compositions. The innovation of this molecular study is the loading of an antitumor drug (docetaxel) on a targeting ligand (riboflavin). According to this work, a novel, biocompatible and targeted system for cancer treatment has been developed. The obtained results revealed a correlation between polymer molecular weight and the stability of particles. In this line, samples including 20 and 10 w/w% moiety NPs formed from polymers with 3 and 4.5 kDa backbone sizes, respectively, are the stable models with the highest drug loading and entrapment efficiencies. Next, we evaluated NP morphology and found that NPs have a core/shell structure consisting of a hydrophobic core with a shell of poly (ethylene glycol) and riboflavin. Interestingly, morphology assessments confirmed that the targeting moiety located on the surface can improve drug delivery to receptors and cancerous cells. The developed models provided significant insight into the structure and morphology of NPs before the synthesis and further analysis of NPs in biological environments. However, in the best cases of this system, Dynamic Light Scattering (DLS) tests were also taken and the results were consistent with the results obtained from All Atom and Coarse Grained simulations.
Project description:Biomolecular engineering can be used to purposefully manipulate biomolecules, such as peptides, proteins, nucleic acids and lipids, within the framework of the relations among their structures, functions and properties, as well as their applicability to such areas as developing novel biomaterials, biosensing, bioimaging, and clinical diagnostics and therapeutics. Nanotechnology can also be used to design and tune the sizes, shapes, properties and functionality of nanomaterials. As such, there are considerable overlaps between nanotechnology and biomolecular engineering, in that both are concerned with the structure and behavior of materials on the nanometer scale or smaller. Therefore, in combination with nanotechnology, biomolecular engineering is expected to open up new fields of nanobio/bionanotechnology and to contribute to the development of novel nanobiomaterials, nanobiodevices and nanobiosystems. This review highlights recent studies using engineered biological molecules (e.g., oligonucleotides, peptides, proteins, enzymes, polysaccharides, lipids, biological cofactors and ligands) combined with functional nanomaterials in nanobio/bionanotechnology applications, including therapeutics, diagnostics, biosensing, bioanalysis and biocatalysts. Furthermore, this review focuses on five areas of recent advances in biomolecular engineering: (a) nucleic acid engineering, (b) gene engineering, (c) protein engineering, (d) chemical and enzymatic conjugation technologies, and (e) linker engineering. Precisely engineered nanobiomaterials, nanobiodevices and nanobiosystems are anticipated to emerge as next-generation platforms for bioelectronics, biosensors, biocatalysts, molecular imaging modalities, biological actuators, and biomedical applications.
Project description:The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.
Project description:CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.
Project description:Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.
Project description:The molecular details of biomolecular kinetics present a challenging estimation problem because the identities of relevant intermediates and the rates of exchange between them must be determined. These can be derived from prior knowledge, but in recent years, great advances have been made in the development and application of methods to systematically determine states and rates using biomolecular simulation. Doing this for biological systems of reasonable complexity requires substantial computational power, and contemporary methods leverage distributed computing or leadership-class computing resources to accomplish this. The result has been substantial insight into pressing contemporary problems, including structural activation of pandemic viruses. Here, we highlight recent developments in both methodology and exciting applications.
Project description:In this paper, we present efficient algorithms for generating hierarchical molecular skin meshes with decreasing size and guaranteed quality. Our algorithms generate a sequence of coarse meshes for both the surfaces and the bounded volumes. Each coarser surface mesh is adaptive to the surface curvature and maintains the topology of the skin surface with guaranteed mesh quality. The corresponding tetrahedral mesh is conforming to the interface surface mesh and contains high quality tetrahedral that decompose both the interior of the molecule and the surrounding region (enclosed in a sphere). Our hierarchical tetrahedral meshes have a number of advantages that will facilitate fast and accurate multigrid PDE solvers. Firstly, the quality of both the surface triangulations and tetrahedral meshes is guaranteed. Secondly, the interface in the tetrahedral mesh is an accurate approximation of the molecular boundary. In particular, all the boundary points lie on the skin surface. Thirdly, our meshes are Delaunay meshes. Finally, the meshes are adaptive to the geometry.
Project description:In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL-MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL-MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL-MS for protein structural studies and the extension to quantitative XL-MS measurements, 3D interactome visualization tools are of critical need.