Assessment of protein-protein interfaces in cryo-EM derived assemblies.

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Assessment of Fibrinogen Macromolecules Interaction with Red Blood Cells Membrane by Means of Laser Aggregometry, Flow Cytometry, and Optical Tweezers Combined with Microfluidics.

An elevated concentration of fibrinogen in blood is a significant risk factor during many pathological diseases, as it leads to an increase in red blood cells (RBC) aggregation, resulting in hemorheological disorders. Despite the biomedical importance, the mechanisms of fibrinogen-induced RBC aggregation are still debatable. One of the discussed models is the non-specific adsorption of fibrinogen macromolecules onto the RBC membrane, leading to the cells bridging in aggregates. However, recent works point to the specific character of the interaction between fibrinogen and the RBC membrane. Fibrinogen is the major physiological ligand of glycoproteins receptors IIbIIIa (GPIIbIIIa or αIIßß3 or CD41/CD61). Inhibitors of GPIIbIIIa are widely used in clinics for the treatment of various cardiovascular diseases as antiplatelets agents preventing the platelets' aggregation. However, the effects of GPIIbIIIa inhibition on RBC aggregation are not sufficiently well studied. The objective of the present work was the complex multimodal in vitro study of the interaction between fibrinogen and the RBC membrane, revealing the role of GPIIbIIIa in the specificity of binding of fibrinogen by the RBC membrane and its involvement in the cells' aggregation process. We demonstrate that GPIIbIIIa inhibition leads to a significant decrease in the adsorption of fibrinogen macromolecules onto the membrane, resulting in the reduction of RBC aggregation. We show that the mechanisms underlying these effects are governed by a decrease in the bridging components of RBC aggregation forces.

Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.

BACKGROUND: Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms. RESULTS: We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold. CONCLUSIONS: Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method.

Microalga Scenedesmus sp.: A potential low-cost green machine for silver nanoparticle synthesis.

Bionanotechnology has revolutionized nanomaterial synthesis by providing a green synthetic platform using biological systems. Among such biological systems, microalgae have tremendous potential to take up metal ions and produce nanoparticles by a detoxification process. The present study explores the intracellular and extracellular biogenic syntheses of silver nanoparticles (SNPs) using the unicellular green microalga Scenedesmus sp. Biosynthesized SNPs were characterized by AAS, UV-Vis spectroscopy, TEM, XRD, FTIR, DLS, and TGA studies and finally checked for antibacterial activity. Intracellular nanoparticle biosynthesis was initiated by a high rate of Ag(+) ion accumulation in the microalgal biomass and subsequent formation of spherical crystalline SNPs (average size, 15-20 nm) due to the biochemical reduction of Ag(+) ions. The synthesized nanoparticles were intracellular, as confirmed by the UV-Vis spectra of the outside medium. Furthermore, extracellular synthesis using boiled extract showed the formation of well scattered, highly stable, spherical SNPs with an average size of 5-10 nm. The size and morphology of the nanoparticles were confirmed by TEM. The crystalline nature of the SNPs was evident from the diffraction peaks of XRD and bright circular ring pattern of SAED. FTIR and UV-Vis spectra showed that biomolecules, proteins and peptides, are mainly responsible for the formation and stabilization of SNPs. Furthermore, the synthesized nanoparticles exhibited high antimicrobial activity against pathogenic gram-negative and gram-positive bacteria. Use of such a microalgal system provides a simple, cost-effective alternative template for the biosynthesis of nanomaterials in a large-scale system that could be of great use in biomedical applications.