Structuprint: a scalable and extensible tool for two-dimensional representation of protein surfaces

© 2016 Kontopoulos et al.Background: The term molecular cartography encompasses a family of computational methods for two-dimensional transformation of protein structures and analysis of their physicochemical properties. The underlying algorithms comprise multiple manual steps, whereas the few existing implementations typically restrict the user to a very limited set of molecular descriptors. Results: We present Structuprint, a free standalone software that fully automates the rendering of protein surface maps, given - at the very least - a directory with a PDB file and an amino acid property. The tool comes with a default database of 328 descriptors, which can be extended or substituted by user-provided ones. The core algorithm comprises the generation of a mould of the protein surface, which is subsequently converted to a sphere and mapped to two dimensions, using the Miller cylindrical projection. Structuprint is partly optimized for multicore computers, making the rendering of animations of entire molecular dynamics simulations feasible. Conclusions: Structuprint is an efficient application, implementing a molecular cartography algorithm for protein surfaces. According to the results of a benchmark, its memory requirements and execution time are reasonable, allowing it to run even on low-end personal computers. We believe that it will be of use - primarily but not exclusively - to structural biologists and computational biochemists

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Environmental Biolog

Grouping of orally ingested silica nanomaterials via use of an integrated approach to testing and assessment to streamline risk assessment

Background: Nanomaterials can exist in different nanoforms (NFs). Their grouping may be supported by the formulation of hypotheses which can be interrogated via integrated approaches to testing and assessment (IATA). IATAs are decision trees that guide the user through tiered testing strategies (TTS) to collect the required evidence needed to accept or reject a grouping hypothesis. In the present paper, we investigated the applicability of IATAs for ingested NFs using a case study that includes different silicon dioxide, SiO2 NFs. Two oral grouping hypotheses addressing local and systemic toxicity were identified relevant for the grouping of these NFs and verified through the application of oral IATAs. Following different Tier 1 and/or Tier 2 in vitro methods of the TTS (i.e., in vitro dissolution, barrier integrity and inflammation assays), we generated the NF datasets. Furthermore, similarity algorithms (e.g., Bayesian method and Cluster analysis) were utilized to identify similarities among the NFs and establish a provisional group(s). The grouping based on Tier 1 and/or Tier 2 testing was analyzed in relation to available Tier 3 in vivo data in order to verify if the read-across was possible and therefore support a grouping decision. Results: The measurement of the dissolution rate of the silica NFs in the oro-gastrointestinal tract and in the lysosome identified them as gradually dissolving and biopersistent NFs. For the local toxicity to intestinal epithelium (e.g. cytotoxicity, membrane integrity and inflammation), the biological results of the gastrointestinal tract models indicate that all of the silica NFs were similar with respect to the lack of local toxicity and, therefore, belong to the same group; in vivo data (although limited) confirmed the lack of local toxicity of NFs. For systemic toxicity, Tier 1 data did not identify similarity across the NFs, with results across different decision nodes being inconsistent in providing homogeneous group(s). Moreover, the available Tier 3 in vivo data were also insufficient to support decisions based upon the obtained in vitro results and relating to the toxicity of the tested NFs. Conclusions: The information generated by the tested oral IATAs can be effectively used for similarity assessment to support a grouping decision upon the application of a hypothesis related to toxicity in the gastrointestinal tract. The IATAs facilitated a structured data analysis and, by means of the expert’s interpretation, supported read-across with the available in vivo data. The IATAs also supported the users in decision making, for example, reducing the testing when the grouping was well supported by the evidence and/or moving forward to advanced testing (e.g., the use of more suitable cellular models or chronic exposure) to improve the confidence level of the data and obtain more focused information

Deliverable Raport D4.6 Tools for generating QMRF and QPRF reports

Scientific reports carry significant importance for the straightforward and effective transfer of knowledge, results and ideas. Good practice dictates that reports should be well-structured and concise. This deliverable describes the reporting services for models, predictions and validation tasks that have been integrated within the eNanoMapper (eNM) modelling infrastructure. Validation services have been added to the Jaqpot Quattro (JQ) modelling platform and the nano-lazar read-across framework developed within WP4 to support eNM modelling activities. Moreover, we have proceeded with the development of reporting services for predictions and models, respectively QPRF and QMRF reports. Therefore, in this deliverable, we first describe the three validation schemes created, namely training set split, cross- and external validation in detail and demonstrate their functionality both on API and UI levels. We then proceed with the description of the read across functionalities and finally, we present and describe the QPRF and QMRF reporting services

Similarity of multicomponent nanomaterials in a safer-by-design context : the case of core–shell quantum dots

Concepts of similarity, such as grouping, categorization, and read-across, enable a fast comparative screening of hazard, reducing animal testing. These concepts are established primarily for molecular substances. We demonstrate the development of multi-dimensional similarity assessment methods that can be applied to multicomponent nanomaterials (MCNMs) for the case of core–shell quantum dots (QDs). The term ‘multicomponent’ refers to their structural composition, which consists of up to four different heavy metals (cadmium, zinc, copper, indium) in different mass percentages, with different morphologies and surface chemistries. The development of concepts of similarity is also motivated by the increased need for comparison of innovative against conventional materials in the safe and sustainable by design (SSbD) context. This case study thus considers the industrial need for an informed balance of functionality and safety: we propose two different approaches to compare and rank the case study materials amongst themselves and against well-known benchmark materials, here ZnO NM110, BaSO4 NM220, TiO2 NM105, and CuO. Relative differences in the sample set are calibrated against the biologically relevant range. The choice of properties that are subjected to similarity assessment is guided by the integrated approaches to testing and assessment (IATA) for the inhalation hazard of simple nanomaterials, which recommends characterizing QDs by (i) dynamic dissolution in lung simulant fluids and (ii) the surface reactivity in the abiotic ferric reducing ability of serum (FRAS) assay. In addition, the similarity of fluorescence spectra was assessed as a measure of the QD performance for the intended functionality as a color converter. We applied two approaches to evaluate the data matrix: in the first approach, specific descriptors for each assay (i.e., leachable mass (%) and mass based biological oxidative damage (mBOD)) were selected based on expert knowledge and used as input data for generation of similarity matrices. The second approach introduces the possibility of evaluating multidimensional raw data by a meaningful similarity analysis, without the need for predefined descriptors. We discuss the strengths and weaknesses of each of the two approaches. We anticipate that the similarity assessment approach is transferable to the assessment of further advanced materials (AdMa) that are composed of multiple components

Population pharmacokinetic reanalysis of a Diazepam PBPK model: a comparison of Stan and GNU MCSim

The aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Using the RRegrs r package for automating predictive modelling

Abstract: Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of methodologies such as data splitting, cross-validation methods, best model criteria and Y-randomization. RRegrs is a new R package, available a

eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment

Engineered nanomaterials (ENMs) are being developed to meet specific application needs in diverse domains across the engineering and biomedical sciences (e.g. drug delivery). However, accompanying the exciting proliferation of novel nanomaterials is a challenging race to understand and predict their possibly detrimental effects on human health and the environment. The eNanoMapper project (www.enanomapper.net) is creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. Here, we describe the development of the eNanoMapper ontology based on adopting and extending existing ontologies of relevance for the nanosafety domain. The resulting eNanoMapper ontology is available at http://purl.enanomapper.net/onto/enanomapper.owl. We aim to make the re-use of external ontology content seamless and thus we have developed a library to automate the extraction of subsets of ontology content and the assembly of the subsets into an integrated whole. The library is available (open source) at http://github.com/enanomapper/slimmer/. Finally, we give a comprehensive survey of the domain content and identify gap areas. ENM safety is at the boundary between engineering and the life sciences, and at the boundary between molecular granularity and bulk granularity. This creates challenges for the definition of key entities in the domain, which we also discuss