Biomolecular Sites Research Articles

A Targeting Singlet Oxygen Battery for Multidrug-Resistant Bacterial Deep-Tissue Infections.

Traditional photodynamic therapy (PDT) is dependent on externally applied light and oxygen, and the depth of penetration of these factors can be insufficient for the treatment of deep infections. The short half-life and short diffusion distance of reactive oxygen species (ROS) also limit the antibacterial efficiency of PDT. Herein, we designed a targeting singlet oxygen delivery system, CARG-Py, for irradiation-free and oxygen-free PDT. This system was converted to the "singlet oxygen battery" CARG-1 O2 and released singlet oxygen without external irradiation or oxygen. CARG-1 O2 is composed of pyridones coupled to a targeting peptide that improves the utilization of singlet oxygen in deep multidrug-resistant bacterial infections. CARG-1 O2 was shown to damage DNA, protein, and membranes by increasing the level of reactive oxygen inside bacteria; the attacking of multiple biomolecular sites caused the death of methicillin-resistant Staphylococcus aureus (MRSA). An in vivo study in a MRSA-infected mouse model of pneumonia demonstrated the potential of CARG-1 O2 for the efficient treatment of deep infections. This work provides a new strategy to improve traditional PDT for irradiation- and oxygen-free treatment of deep infections while improving convenience of PDT.

A Targeting Singlet Oxygen Battery for Multidrug‐Resistant Bacterial Deep‐Tissue Infections

AbstractTraditional photodynamic therapy (PDT) is dependent on externally applied light and oxygen, and the depth of penetration of these factors can be insufficient for the treatment of deep infections. The short half‐life and short diffusion distance of reactive oxygen species (ROS) also limit the antibacterial efficiency of PDT. Herein, we designed a targeting singlet oxygen delivery system, CARG‐Py, for irradiation‐free and oxygen‐free PDT. This system was converted to the “singlet oxygen battery” CARG‐1O2 and released singlet oxygen without external irradiation or oxygen. CARG‐1O2 is composed of pyridones coupled to a targeting peptide that improves the utilization of singlet oxygen in deep multidrug‐resistant bacterial infections. CARG‐1O2 was shown to damage DNA, protein, and membranes by increasing the level of reactive oxygen inside bacteria; the attacking of multiple biomolecular sites caused the death of methicillin‐resistant Staphylococcus aureus (MRSA). An in vivo study in a MRSA‐infected mouse model of pneumonia demonstrated the potential of CARG‐1O2 for the efficient treatment of deep infections. This work provides a new strategy to improve traditional PDT for irradiation‐ and oxygen‐free treatment of deep infections while improving convenience of PDT.

Flexible Modulation of Cellular Activities with Cationic Photosensitizers: Insights of Alkyl Chain Length on Reactive Oxygen Species Antimicrobial Mechanisms.

Cationic photosensitizers have good binding ability with negatively charged bacteria and fungi, exhibiting broad applications potential in antimicrobial photodynamic therapy (aPDT). However, cationic photosensitizers often display unsatisfactory transkingdom selectivity between mammalian cells and pathogens, especially for eukaryotic fungi. It is unclear which biomolecular sites are more efficient for photodynamic damage, owing to the lack of systematic research with the same photosensitizer system. Herein, a series of cationic aggregation-induced emission (AIE) derivatives (CABs) (using berberine (BBR) as the photosensitizers core) with different length alkyl chains are successfully designed and synthesized for flexible modulation of cellular activities. The BBR core can efficiently produce reactive oxygen species (ROS) and achieve high-performance aPDT . Through the precise regulation of alkyl chain length, different bindings, localizations, and photodynamic killing effects of CABs are achieved and investigated systematically among bacteria, fungi, and mammalian cells. It is found that intracellular active substances, not membranes, are more efficient damage sites of aPDT. Moderate length alkyl chains enable CABs to effectively kill Gram-negative bacteria and fungi with light, while still maintaining excellent mammalian cell and blood compatibility. This study is expected to provide systematic theoretical and strategic research guidance for the construction of high-performance cationic photosensitizers with good transkingdom selectivity.

Biomolecular Characteristics of Amyloid Precursor Protein Mutations As Causes of Alzheimer's Disease Analyzed Through Bioinformatics Media

Methods of biomolecular analysis on protein through bioinformatics media has been growing rapidly. This report is intended to describe the results of biomolecular amyloid precursor protein (APP) analysis and the characteristic change of APP mutations causing cognitive degeneration disorder through bioinformatics media and search to reliable biomolecular sites. APP is a transmembrane protein that is widely present in neuronal tissue. APP plays an important role for the formation of neuronal cells. The APP gene is located on the 21st chromosome that has 290,586 base pairs (bp) of 20 exons and 19 introns. In the form of a protein, APP consists of 770 amino acids with major expressions located on the cell surface. Mutation of APP genes is believed to play a role in the incidence of Alzheimer's disease. One of the mutations studied in this paper is the mutation of Alanin to Glysin at the position of the 692 amino acid (APP mutant A692G). This mutation affects the impairment of cerebral amyloid angiopathy belonging to the Alzheimer Disease Familial-1 group.These mutations do not cause changes in predictions of secondary structures and only cause slight changes in molecular weight, amino acid composition, atomic composition, aliphatic index, hydroplasticity and stability index. APP A692G mutation does not alter topology, hydrophobicity, cutting prediction and protein location prediction. Mutation also removes only the side chains at the position of the mutated amino acids.

SPATKIN: a simulator for rule-based modeling of biomolecular site dynamics on surfaces.

Rule-based modeling is a powerful approach for studying biomolecular site dynamics. Here, we present SPATKIN, a general-purpose simulator for rule-based modeling in two spatial dimensions. The simulation algorithm is a lattice-based method that tracks Brownian motion of individual molecules and the stochastic firing of rule-defined reaction events. Because rules are used as event generators, the algorithm is network-free, meaning that it does not require to generate the complete reaction network implied by rules prior to simulation. In a simulation, each molecule (or complex of molecules) is taken to occupy a single lattice site that cannot be shared with another molecule (or complex). SPATKIN is capable of simulating a wide array of membrane-associated processes, including adsorption, desorption and crowding. Models are specified using an extension of the BioNetGen language, which allows to account for spatial features of the simulated process. The C ++ source code for SPATKIN is distributed freely under the terms of the GNU GPLv3 license. The source code can be compiled for execution on popular platforms (Windows, Mac and Linux). An installer for 64-bit Windows and a macOS app are available. The source code and precompiled binaries are available at the SPATKIN Web site (http://pmbm.ippt.pan.pl/software/spatkin). spatkin.simulator@gmail.com. Supplementary data are available at Bioinformatics online.

Modeling biomolecular site dynamics in immunoreceptor signaling systems.

The immune system plays a central role in human health. The activities of immune cells, whether defending an organism from disease or triggering a pathological condition such as autoimmunity, are driven by the molecular machinery of cellular signaling systems. Decades of experimentation have elucidated many of the biomolecules and interactions involved in immune signaling and regulation, and recently developed technologies have led to new types of quantitative, systems-level data. To integrate such information and develop nontrivial insights into the immune system, computational modeling is needed, and it is essential for modeling methods to keep pace with experimental advances. In this chapter, we focus on the dynamic, site-specific, and context-dependent nature of interactions in immunoreceptor signaling (i.e., the biomolecular site dynamics of immunoreceptor signaling), the challenges associated with capturing these details in computational models, and how these challenges have been met through use of rule-based modeling approaches.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Quantifying intrinsic chemical reactivity of molecular structural features for protein binding and reactive toxicity, using the MOSES chemoinformatics system

Covalent binding of xenobiotic compounds to endogenous biomolecular sites, e.g. protein residues, leads to potentially irreversible toxic effects such as enhanced acute toxicity or skin sensitization [1]. This mechanistic knowledge provides the basis for the in silico prediction of these toxicities, as required by the EU REACH legislation and the EU Cosmetics Directive. A general toxicity prediction can be based on three consecutive steps [2]: (1.) Identification of a potential reactive protein binding mechanism via a set of molecular structural features. Those structural features can be encoded by the Chemical Subgraph Representation Markup Language (CSRML), that supports a flexible annotation of meta information, including physicochemical properties as annotations. (2.) Confirmation and quantification of (bio)chemical reactivity. The potential for a chemical to be reactive can be captured by mechanistically based QSAR models. This intrinsic reactivity is calculated rapidly by descriptors of the chemoinformatics platform Molecular Structure Encoding System (MOSES) [3]. It represents electronic, steric and resonance effects in chemical structures. The performances obtained by these reactivity models are close to time-consuming quantum chemical reactivity calculations, e.g., se = 0.44 versus 0.40 for glutathione adduct formation via Michael addition, comparing predicted values to an experimental reactivity dataset [1]. (3.) Establishing a relationship between calculated reactivity and toxicity. The predicted intrinsic reactivity values were linked to the computational prediction for different modes of toxic action, with good correlations between predicted and in vitro toxicity (up to r2=0.86). The combined use of structural information and computed reactivity could assist in the non-animal based risk assessment of chemicals for regulatory purposes and in the application of integrated testing strategies (ITS). The research has received funding from the EU FP7 COSMOS Project (grant agreement n° 266835) and financing from COLIPA.

In situ molecular dynamics analysis of the water hydrogen bond at biomolecular sites: Hydrophobicity enhances dynamics heterogeneity

Molecular dynamics simulations have been used to investigate the dynamics of hydrogen bonds (HBs) formed by hydration water in selected hydrophilic and hydrophobic biomolecules. Labelling all of atoms in the molecule allows analysing the HB relaxation time at specific donor and acceptor binding sites. The results show that the water–water HBs present a longer relaxation time around the completely hydrophilic peptide, and that the water–biomolecule HBs exhibit a distinct behaviour depending on the binding sites at the hydrophilic or hydrophobic bio-interface. The presence of a large hydrophobic surface enhances, in particular at low temperature, the dynamical heterogeneity in the neighbouring hydrogen bond network.

Vibrational Density of States of Hydration Water at Biomolecular Sites: Hydrophobicity Promotes Low Density Amorphous Ice Behavior

Inelastic neutron scattering experiments and molecular dynamics simulations have been used to investigate the low frequency modes, in the region between 0 and 100 meV, of hydration water in selected hydrophilic and hydrophobic biomolecules. The results show changes in the plasticity of the hydrogen-bond network of hydration water molecules depending on the biomolecular site. At 200 K, the measured low frequency density of states of hydration water molecules of hydrophilic peptides is remarkably similar to that of high density amorphous ice, whereas, for hydrophobic biomolecules, it is comparable to that of low density amorphous ice behavior. In both hydrophilic and hydrophobic biomolecules, the high frequency modes show a blue shift of the libration mode as compared to the room temperature data. These results can be related to the density of water molecules around the biological interface, suggesting that the apparent local density of water is larger in a hydrophilic environment.

On the behaviour of water hydrogen bonds at biomolecular sites: Dependences on temperature and on network dimensionality

Neutron scattering experiments have been used to investigate the effects of temperature and network dimensionality (from hydrated powders to highly concentrated solutions) on the hydrogen bond dynamics of hydration water molecules at specific sites in selected biomolecules. With this aim in view, the evolution of hydration water dynamics of a prototypical hydrophobic amino acid with polar backbone, N-acetyl-leucine-methylamide (NALMA), and a hydrophilic amino acid, N-acetyl-glycine-methylamide (NAGMA), has been investigated as a function of temperature. We show that the temperature dependence of the diffusive dynamics of water molecules is the same for both hydrophilic and hydrophobic peptides. A comparison between hydrated powders and high concentrated solutions reveals a similar behaviour, particularly for the hydrophobic peptide. On the other hand we find a distinct difference in the behaviour with temperature of the hydrogen bond lifetime in solutions and hydrated powders. Whereas at room temperature the hydrogen bond lifetime is longer in solution than in the hydrated powder, the reverse situation obtains at low temperatures. This result suggests a change in the plasticity of the hydrogen bond network depending on its extension. Differences in the densities of states lend support to this concept.

Characterizing the microenvironment surrounding protein sites

Sites are microenvironments within a biomolecular structure, distinguished by their structural or functional role. A site can be defined by a three-dimensional location and a local neighborhood around this location in which the structure or function exists. We have developed a computer system to facilitate structural analysis (both qualitative and quantitative) of biomolecular sites. Our system automatically examines the spatial distributions of biophysical and biochemical properties, and reports those regions within a site where the distribution of these properties differs significantly from control nonsites. The properties range from simple atom-based characteristics such as charge to polypeptide-based characteristics such as type of secondary structure. Our analysis of sites uses non-sites as controls, providing a baseline for the quantitative assessment of the significance of the features that are uncovered. In this paper, we use radial distributions of properties to study three well-known sites (the binding sites for calcium, the milieu of disulfide bridges, and the serine protease active site). We demonstrate that the system automatically finds many of the previously described features of these sites and augments these features with some new details. In some cases, we cannot confirm the statistical significance of previously reported features. Our results demonstrate that analysis of protein structure is sensitive to assumptions about background distributions, and that these distributions should be considered explicitly during structural analyses.