Nanomatrix: Scalable Construction of Crowded Biological Environments.

We present a novel method for the interactive construction and rendering of extremely large molecular scenes, capable of representing multiple biological cells in atomistic detail. Our method is tailored for scenes, which are procedurally constructed, based on a given set of building rules. Rendering of large scenes normally requires the entire scene available in-core, or alternatively, it requires out-of-core management to load data into the memory hierarchy as a part of the rendering loop. Instead of out-of-core memory management, we propose to procedurally generate the scene on-demand on the fly. The key idea is a positional- and view-dependent procedural scene-construction strategy, where only a fraction of the atomistic scene around the camera is available in the GPU memory at any given time. The atomistic detail is populated into a uniform-space partitioning using a grid that covers the entire scene. Most of the grid cells are not filled with geometry, only those are populated that are potentially seen by the camera. The atomistic detail is populated in a compute shader and its representation is connected with acceleration data structures for hardware ray-tracing of modern GPUs. Objects which are far away, where atomistic detail is not perceivable from a given viewpoint, are represented by a triangle mesh mapped with a seamless texture, generated from the rendering of geometry from atomistic detail. The algorithm consists of two pipelines, the construction-compute pipeline, and the rendering pipeline, which work together to render molecular scenes at an atomistic resolution far beyond the limit of the GPU memory containing trillions of atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the red blood cell.

Lycorine and homolycorine derivatives for chemo-sensitizing resistant human ovarian adenocarcinoma cells.

BACKGROUND: Multidrug resistance is the major obstacle to cancer chemotherapy. Modulation of P-glycoprotein and drug combination approaches have been considered important strategies to overcome drug resistance. PURPOSE: Aiming at generating a small library of Amaryllidaceae-type alkaloids to overcome drug resistance, two major alkaloids, isolated from Pancratium maritimum, lycorine (1), and 2α-10bα-dihydroxy-9-O-demethylhomolycorine (2), were derivatized, giving rise to nineteen derivatives (3 - 21). METHODS: The main chemical transformation of lycorine resulted from the cleavage of ring E of the diacetylated lycorine derivative (3) to obtain compounds that have carbamate and amine functions (5 - 16), while acylation of compound 2 provided derivatives 17 - 21. Compounds 1 - 21 were evaluated for their effects on cytotoxicity, and drug resistance reversal, using resistant human ovarian carcinoma cells (HOC/ADR), overexpressing P-glycoprotein (P-gp/ABCB1), as model. RESULTS: Excluding lycorine (1) (IC50 values of 1.2- 2.5 µM), the compounds were not cytotoxic or showed moderate/weak cytotoxicity. Chemo-sensitization assays were performed by studying the in vitro interaction between the compounds and the anticancer drug doxorubicin. Most of the compounds have shown synergistic interactions with doxorubicin. Compounds 5, 6, 9 - 14, bearing both carbamate and aromatic amine moieties, were found to have the highest sensitization rate, reducing the dose of doxorubicin 5-35 times, highlighting their potential to reverse drug resistance in combination chemotherapy. Selected compounds (4 - 6, 9 - 14, and 21), able of re-sensitizing resistant cancer cells, were further evaluated as P-gp inhibitors. Compound 11, which has a para­methoxy-N-methylbenzylamine moiety, was the strongest inhibitor. In the ATPase assay, compounds 9-11 and 13 behaved as verapamil, suggesting competitive inhibition of P-gp. At the same time, none of these compounds affected P-gp expression at the mRNA or protein level. CONCLUSIONS: This study provided evidence of the potential of Amaryllidaceae alkaloids as lead candidates for the development of MDR reversal agents.

SpyDirect: A Novel Biofunctionalization Method for High Stability and Longevity of Electronic Biosensors.

Electronic immunosensors are indispensable tools for diagnostics, particularly in scenarios demanding immediate results. Conventionally, these sensors rely on the chemical immobilization of antibodies onto electrodes. However, globular proteins tend to adsorb and unfold on these surfaces. Therefore, self-assembled monolayers (SAMs) of thiolated alkyl molecules are commonly used for indirect gold-antibody coupling. Here, a limitation associated with SAMs is revealed, wherein they curtail the longevity of protein sensors, particularly when integrated into the state-of-the-art transducer of organic bioelectronics-the organic electrochemical transistor. The SpyDirect method is introduced, generating an ultrahigh-density array of oriented nanobody receptors stably linked to the gold electrode without any SAMs. It is accomplished by directly coupling cysteine-terminated and orientation-optimized spyTag peptides, onto which nanobody-spyCatcher fusion proteins are autocatalytically attached, yielding a dense and uniform biorecognition layer. The structure-guided design optimizes the conformation and packing of flexibly tethered nanobodies. This biolayer enhances shelf-life and reduces background noise in various complex media. SpyDirect functionalization is faster and easier than SAM-based methods and does not necessitate organic solvents, rendering the sensors eco-friendly, accessible, and amenable to scalability. SpyDirect represents a broadly applicable biofunctionalization method for enhancing the cost-effectiveness, sustainability, and longevity of electronic biosensors, all without compromising sensitivity.

Toxicological investigation of lilial.

Lilial (also called lysmeral) is a fragrance ingredient presented in many everyday cosmetics and household products. The concentrations of lilial in the final products is rather low. Its maximum concentration in cosmetics was limited and recently, its use in cosmetics products was prohibited in the EU due to the classification as reproductive toxicant. Additionally, according to the European Chemicals Agency, it was under assessment as one of the potential endocrine disruptors, i.e. a substance that may alter the function of the endocrine system and, as a result, cause health problems. Its ability to act as an androgen receptor agonist and the estrogenic and androgenic activity of its metabolites, to the best of our knowledge, have not yet been tested. The aim of this work was to determine the intestinal absorption, cytotoxicity, nephrotoxicity, mutagenicity, activation of cellular stress-related signal pathways and, most importantly, to test the ability to disrupt the endocrine system of lilial and its Phase I metabolites. This was tested using set of in vitro assays including resazurin assay, the CHO/HPRT mutation assay, γH2AX biomarker-based genotoxicity assay, qPCR and in vitro reporter assays based on luminescence of luciferase for estrogen, androgen, NF-κB and NRF2 signalling pathway. It was determined that neither lilial nor its metabolites have a negative effect on cell viability in the concentration range from 1 nM to 100 µM. Using human cell lines HeLa9903 and MDA-kb2, it was verified that this substance did not have agonistic activity towards estrogen or androgen receptor, respectively. Lilial metabolites, generated by incubation with the rat liver S9 fraction, did not show the ability to bind to estrogen or androgen receptors. Neither lilial nor its metabolites showed a nephrotoxic effect on human renal tubular cells (RPTEC/TERT1 line) and at the same time they were unable to activate the NF-κB and NRF2 signalling pathway at a concentration of 50 µM (HEK 293/pGL4.32 or pGL4.37). Neither lilial nor its metabolites showed mutagenic activity in the HPRT gene mutation test in CHO-K1 cells, nor were they able to cause double-strand breaks in DNA (γH2AX biomarker) in CHO-K1 and HeLa cells. In our study, no negative effects of lilial or its in vitro metabolites were observed up to 100 µM using different in vitro tests.

Cannabidiol nanoemulsion for eye treatment - Anti-inflammatory, wound healing activity and its bioavailability using in vitro human corneal substitute.

Cannabidiol (CBD) is the non-psychoactive component of the plant Cannabis sativa (L.) that has great anti-inflammatory benefits and wound healing effects. However, its high lipophilicity, chemical instability, and extensive metabolism impair its bioavailability and clinical use. Here, we report on the preparation of a human cornea substitute in vitro and validate this substitute for the evaluation of drug penetration. CBD nanoemulsion was developed and evaluated for stability and biological activity. The physicochemical properties of CBD nanoemulsion were maintained during storage for 90 days under room conditions. In the scratch assay, nanoformulation showed significantly ameliorated wound closure rates compared to the control and pure CBD. Due to the lower cytotoxicity of nanoformulated CBD, a higher anti-inflammatory activity was demonstrated. Neither nanoemulsion nor pure CBD can penetrate the cornea after the four-hour apical treatment. For nanoemulsion, 94 % of the initial amount of CBD remained in the apical compartment while only 54 % of the original amount of pure CBD was detected in the apical medium, and 7 % in the cornea, the rest was most likely metabolized. In summary, the nanoemulsion developed in this study enhanced the stability and biological activity of CBD.

Molecumentary: Adaptable Narrated Documentaries Using Molecular Visualization.

We present a method for producing documentary-style content using real-time scientific visualization. We introduce molecumentaries, i.e., molecular documentaries featuring structural models from molecular biology, created through adaptable methods instead of the rigid traditional production pipeline. Our work is motivated by the rapid evolution of scientific visualization and it potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: (1) preparing a story structure and (2) turning the story into a concrete narrative. In the first step, we compile information about the model from heterogeneous sources into a story graph. We combine local knowledge with external sources to complete the story graph and enrich the final result. In the second step, we synthesize a narrative, i.e., story elements presented in sequence, using the story graph. We then traverse the story graph and generate a virtual tour, using automated camera and visualization transitions. We turn texts written by domain experts into verbal representations using text-to-speech functionality and provide them as a commentary. Using the described framework, we synthesize fly-throughs with descriptions: automatic ones that mimic a manually authored documentary or semi-automatic ones which guide the documentary narrative solely through curated textual input.

Nanotilus: Generator of Immersive Guided-Tours in Crowded 3D Environments.

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This article proposes Nanotilus-a new visibility and guidance approach for very dense environments that generates an endoscopic inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multi-scale, multi-shell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models - SARS-CoV-2 and HIV. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.

Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation.

Cryo-electron tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural details. Existing volume visualization methods, however, are not able to reveal details of interest due to low signal-to-noise ratio. In order to design more powerful transfer functions, we propose leveraging soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning, where we combine the advantages of two segmentation algorithms. First, the weak segmentation algorithm provides good results for propagating sparse user-provided labels to other voxels in the same volume and is used to generate dense pseudo-labels. Second, the powerful deep-learning-based segmentation algorithm learns from these pseudo-labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses deep-learning-based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through frequency distribution analysis. Furthermore, our visualization uses gradient-free ambient occlusion shading to further suppress the visual presence of noise, and to give structural detail the desired prominence. The cryo-ET data studied in our technical experiments are based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.

GPU Accelerated 3D Tomographic Reconstruction and Visualization from Noisy Electron Microscopy Tilt-Series.

We present a novel framework for 3D tomographic reconstruction and visualization of tomograms from noisy electron microscopy tilt-series. Our technique takes as an input aligned tilt-series from cryogenic electron microscopy and creates denoised 3D tomograms using a proximal jointly-optimized approach that iteratively performs reconstruction and denoising, relieving the users of the need to select appropriate denoising algorithms in the pre-reconstruction or post-reconstruction steps. The whole process is accelerated by exploiting parallelism on modern GPUs, and the results can be visualized immediately after the reconstruction using volume rendering tools incorporated in the framework. We show that our technique can be used with multiple combinations of reconstruction algorithms and regularizers, thanks to the flexibility provided by proximal algorithms. Additionally, the reconstruction framework is open-source and can be easily extended with additional reconstruction and denoising methods. Furthermore, our approach enables visualization of reconstruction error throughout the iterative process within the reconstructed tomogram and on projection planes of the input tilt-series. We evaluate our approach in comparison with state-of-the-art approaches and additionally show how our error visualization can be used for reconstruction evaluation.

Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models.

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.

CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories.

Motivation: Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results: CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation: CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz. Supplementary information: Supplementary data are available at Bioinformatics online.

CAVER Analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures.

UNLABELLED: The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics. AVAILABILITY AND IMPLEMENTATION: CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz.

CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures.

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.