Now showing 1 - 10 of 13
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Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles

2015-12, Lopez, Hender, Lobaskin, Vladimir

We present a coarse-grained model for evaluation of interactions of globular proteins with nanoparticles (NPs). The protein molecules are represented by one bead per aminoacid and the nanoparticle by a homogeneous sphere that interacts with the aminoacids via a central force that depends on the nanoparticle size. The proposed methodology is used to predict the adsorption energies for six common human blood plasma proteins on hydrophobic charged or neutral nanoparticles of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the nanoparticle, which can be used for predicting the composition of the NP-protein corona. The predicted ranking is in good agreement with known experimental data for proteinadsorption on surfaces.

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EU US Roadmap Nanoinformatics 2030

2018-11-15, Lobaskin, Vladimir, Puzyn, Tomasz, Verheyen, Geert, et al., Haase, Andrea, Klaessig, Frederick

The Nanoinformatics Roadmap 2030 is a compilation of state-of-the-art commentaries from multiple interconnecting scientific fields, combined with issues involving nanomaterial (NM) risk assessment and governance. In bringing these issues together into a coherent set of milestones, the authors address three recognised challenges facing nanoinformatics: (1) limited data sets; (2) limited data access; and (3) regulatory requirements for validating and accepting computational models. It is also recognised that data generation will progress unequally and unstructured if not captured within a nanoinformatics framework based on harmonised, interconnected databases and standards. The implicit coordination efforts within such a framework ensure early use of the data for regulatory purposes, e.g., for the read-across method of filling data gaps.

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Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations

2020-01-17, Silva, Fernando Luís Barroso da, Carloni, Paolo, Cheung, David, Gulzar, Muhammad, Jacquier, Jean Christophe, Lobaskin, Vladimir, MacKernan, Donal, et al.

Molecular mechanisms play key roles at a fundamental and processing level, in innovative taste systems, functional and nutritional ingredients, and integrated solutions for the food, beverage and pharmaceutical markets. Incorporating a multiscale understanding of such mechanisms can provide greater insight into, and control of the relevant processes at play. Combining data from experiment, human panels and simulation through machine learning allows the construction of statistical models relating nano-scale properties to physiological outcomes and consumer tastes. This review will touch on several examples where advanced computer simulations at a molecular, meso- and multi-scale level can shed light into the mechanisms at play thereby facilitating their control. It includes a practical simulation toolbox for those new to in-silico modelling.

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Tricritical points in a Vicsek model of self-propelled particles with bounded confidence

2014-12-24, Romensky, Maksym, Lobaskin, Vladimir, Ihle, Thomas

We study the orientational ordering in systems of self-propelled particles with selective interactions. To introduce the selectivity we augment the standard Vicsek model with a bounded-confidence collision rule: a given particle only aligns to neighbors who have directions quite similar to its own. Neighbors whose directions deviate more than a fixed restriction angle α are ignored. The collective dynamics of this system is studied by agent-based simulations and kinetic mean-field theory. We demonstrate that the reduction of the restriction angle leads to a critical noise amplitude decreasing monotonically with that angle, turning into a power law with exponent 3/2 for small angles. Moreover, for small system sizes we show that upon decreasing the restriction angle, the kind of the transition to polar collective motion changes from continuous to discontinuous. Thus, an apparent tricritical point with different scaling laws is identified and calculated analytically. We investigate the shifting and vanishing of this point due to the formation of density bands as the system size is increased. Agent-based simulations in small systems with large particle velocities show excellent agreement with the kinetic theory predictions. We also find that at very small interaction angles, the polar ordered phase becomes unstable with respect to the apolar phase. We derive analytical expressions for the dependence of the threshold noise on the restriction angle. We show that the mean-field kinetic theory also permits stationary nematic states below a restriction angle of 0.681 π. We calculate the critical noise, at which the disordered state bifurcates to a nematic state, and find that it is always smaller than the threshold noise for the transition from disorder to polar order. The disordered-nematic transition features two tricritical points: At low and high restriction angle, the transition is discontinuous but continuous at intermediate α. We generalize our results to systems that show fragmentation into more than two groups and obtain scaling laws for the transition lines and the corresponding tricritical points. A numerical method to evaluate the nonlinear Fredholm integral equation for the stationary distribution function is also presented. This method is shown to give excellent agreement with agent-based simulations, even in strongly ordered systems at noise values close to zero.

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An Integrative Computational Approach for a Prioritization of Key Transcription Regulators Associated With Nanomaterial-Induced Toxicity

2019-10, Zhernovkov, Vadim, Santra, Tapesh, Cassidy, Hilary, Rukhlenko, Oleksii S., Matallanas, David, Krstic, Aleksandar, Kolch, Walter, Lobaskin, Vladimir, Kholodenko, Boris N.

A rapid increase of new nanomaterial products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of nanomaterials (NMs) are complex, time consuming and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach and data from high-throughput omics experiments can be used. We present a computational approach that combines reverse engineering techniques, network analysis and pathway enrichment analysis for inferring the transcriptional regulation landscape and its functional interpretation. To illustrate this approach, we used published transcriptomic data derived from mice lung tissue exposed to carbon nanotubes (NM-401 and NRCWE-26). Because fibrosis is the most common adverse effect of these NMs, we included in our analysis the data for bleomycin (BLM) treatment, which is a well-known fibrosis inducer. We inferred gene regulatory networks for each NM and BLM to capture functional hierarchical regulatory structures between genes and their regulators. Despite the different nature of the lung injury caused by nanoparticles and BLM, we identified several conserved core regulators for all agents. We reason that these regulators can be considered as early predictors of toxic responses after NMs exposure. This integrative approach, which refines traditional methods of transcriptomic analysis, can be useful for prioritization of potential core regulators and generation of new hypothesis about mechanisms of nanoparticles toxicity.

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Translating Scientific Advances in the AOP Framework to Decision Making for Nanomaterials

2020-06-24, Ede, James D., Lobaskin, Vladimir, Vogel, Ulla, et al.

Much of the current innovation in advanced materials is occurring at the nanoscale, specifically in manufactured nanomaterials (MNs). MNs display unique attributes and behaviors, and may be biologically and physically unique, making them valuable across a wide range of applications. However, as the number, diversity and complexity of MNs coming to market continue to grow, assessing their health and environmental risks with traditional animal testing approaches is too time- and cost-intensive to be practical, and is undesirable for ethical reasons. New approaches are needed that meet current requirements for regulatory risk assessment while reducing reliance on animal testing and enabling safer-by-design product development strategies to be implemented. The adverse outcome pathway (AOP) framework presents a sound model for the advancement of MN decision making. Yet, there are currently gaps in technical and policy aspects of AOPs that hinder the adoption and use for MN risk assessment and regulatory decision making. This review outlines the current status and next steps for the development and use of the AOP framework in decision making regarding the safety of MNs. Opportunities and challenges are identified concerning the advancement and adoption of AOPs as part of an integrated approach to testing and assessing (IATA) MNs, as are specific actions proposed to advance the development, use and acceptance of the AOP framework and associated testing strategies for MN risk assessment and decision making. The intention of this review is to reflect the views of a diversity of stakeholders including experts, researchers, policymakers, regulators, risk assessors and industry representatives on the current status, needs and requirements to facilitate the future use of AOPs in MN risk assessment. It incorporates the views and feedback of experts that participated in two workshops hosted as part of an Organization for Economic Cooperation and Development (OECD) Working Party on Manufactured Nanomaterials (WPMN) project titled, “Advancing AOP Development for Nanomaterial Risk Assessment and Categorization”, as well as input from several EU-funded nanosafety research consortia.

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Structure and elasticity of bush and brush-like models of the endothelial glycocalyx

2018-01-10, Kabedev, Aleksei, Lobaskin, Vladimir

The endothelial glycocalyx (EG), a sugar-rich layer that lines the luminal surface of blood vessels, is an important constituent of the vascular system. Although the chemical composition of the EG is fairly well known, there is no consensus regarding its ultrastructure. While previous experiments probed the properties of the layer at the continuum level, they did not provide sufficient insight into its molecular organisation. In this work, we investigate the EG mechanics using two simple brush and bush-like simulation models, and use these models to describe its molecular structure and elastic response to indentation. We analyse the relationship between the mechanical properties of the EG layer and several molecular parameters, including the filament bending rigidity, grafting density, and the type of ultrastructure. We show that variations in the glycan density determine the elasticity of the EG for small deformations, and that the normal stress may be effectively dampened by the EG layer, preventing the stress from being transferred to the cell membrane. Furthermore, our bush-like model allows us to evaluate the forces and energies required to overcome the mechanical resistance of the EG.

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Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

2018-08-01, Romenskyy, Maksym, Spaiser, Viktoria, Ihle, Thomas, Lobaskin, Vladimir

Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.

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Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

2020-12-11, Lynch, Iseult, Afantitis, Antreas, Exner, Thomas, Lobaskin, Vladimir, et al.

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.

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In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles

2020-10-04, Alsharif, Shada A., Power, David, Rouse, Ian, Lobaskin, Vladimir

The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily available. In this work, we demonstrate how information about adsorption affinity of proteins onto nanoparticles can be obtained from first principles with minimum experimental input. We use a multiscale model of protein–nanoparticle interaction to evaluate adsorption energies for a set of 59 human blood serum proteins on gold and titanium dioxide (anatase) nanoparticles of various sizes. For each protein, we compare the results for 3D structures derived from experiments to those predicted computationally from amino acid sequences using the I-TASSER methodology and software. Based on these calculations and 2D and 3D protein descriptors, we develop statistical models for predicting the binding energy of proteins, enabling the rapid characterization of the affinity of nanoparticles to a wide range of proteins.