The Zika virus (ZIKV) is an arthropod-borne virus that belongs to the Flaviviridae family. The ZIKV infection is usually asymptomatic or is associated with mild clinical manifestations; however, increased numbers of cases of microcephaly and birth defects have been recently reported. To date, neither a vaccine nor an antiviral treatment has become available to control ZIKV replication. Among the natural compounds recognized for their medical properties, flavonoids, which can be found in fruits and vegetables, have been found to possess biological activity against a variety of viruses. Here, we demonstrate that the citrus flavanone naringenin (NAR) prevented ZIKV infection in human A549 cells in a concentration-dependent and ZIKV-lineage independent manner. NAR antiviral activity was also observed when primary human monocyte-derived dendritic cells were infected by ZIKV. NAR displayed its antiviral activity when the cells were treated after infection, suggesting that NAR acts on the viral replication or assembly of viral particles. Moreover, a molecular docking analysis suggests a potential interaction between NAR and the protease domain of the NS2B-NS3 protein of ZIKV which could explain the anti-ZIKV activity of NAR. Finally, the results support the potential of NAR as a suitable candidate molecule for developing anti-ZIKV treatments.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

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Background Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Biological membranes are continuously remodeled in the cell by specific membrane-shaping machineries to form, for example, tubes and vesicles. We examine fundamental mechanisms involved in the vesiculation processes induced by a cluster of envelope (E) and membrane (M) proteins of the dengue virus (DENV) using molecular dynamics simulations and a coarse-grained model. We show that an arrangement of three E-M heterotetramers (EM) works as a bending unit and an ordered cluster of five such units generates a closed vesicle, reminiscent of the virus budding process. In silico mutagenesis of two charged residues of the anchor helices of the envelope proteins of DENV shows that Arg-471 and Arg-60 are fundamental to produce bending stress on the membrane. The fine-tuning between the size of the EM unit and its specific bending action suggests this protein unit is an important factor in determining the viral particle size.

This work describes the synthesis of the 1,2,3-triazole amino acid-derived-3-O-galactosides 1-6 and the 1,2,3-triazole di-lactose-derived glycoconjugate 7 as potential galectin-3 inhibitors. The target compounds were synthesized by Cu(I)-catalyzed azide-alkyne cycloaddition reaction ('click chemistry') between the azido-derived amino acids N3-ThrOBn, N3-PheOBn, N3-N-Boc-TrpOBn, N3-N-Boc-LysOBn, N3-O-tBu-AspOBn and N3-l-TyrOH, and the corresponding alkyne-based sugar 3-O-propynyl-GalOMe, as well as by click chemistry reaction between the azido-lactose and 2-propynyl lactose. Surface plasmon resonance (SPR) assays showed that all synthetic glycoconjugates 1-7 bound to galectin-3 with high affinity, but the highest binders were the amino acids-derived glycoconjugates 2 (KD 7.96μM) and 4 (KD 4.56μM), and the divalent lactoside 7 (KD1 0.15μM/KD2 19μM). Molecular modeling results were in agreement with SPR assays, since more stable interactions with galectin-3 were identified for glycoconjugates 2, 4 and 7. Regarding compounds 2 and 4, they established specific cation-π (Arg144) and ionic (Asp148) interactions, whereas glycoconjugate 7 was capable to bridge two independent galectin-3 CRDs, creating a non-covalent cross-link between two monomers and, thus, reaching a submicromolar affinity towards galectin-3.

Changes in the concentration of different ions modulate several cellular processes, such as Ca(2+) and Zn(2+) in inflammation. Upon activation of immune system effector cells, the intracellular Ca(2+) concentration rises propagating the activation signal, leading to degranulation and generation of reactive oxygen species, which increases the Zn(2+) intracellular concentration as a consequence of the cellular antioxidant machinery. In this context, S100A12 is of special interest because it is a pro-inflammatory protein expressed in neutrophils whose structure and function are modulated by both Ca(2+) and Zn(2+). The current hypothesis about its mechanism of action was built based on biochemical and crystallographic data. However, there are missing connections between molecular structure and the way in which many events are concatenated at the triggering and along the inflammatory process. In this work we use molecular dynamics simulations to describe how variations in Zn(2+) and Ca(2+) concentrations modulate the structural dynamics of the calcium-free S100A12 dimer and monomer, which was not considered a part of the mechanism of action before. Our results suggest that (i) Zn(2+) have a determinant role in the dimerization step, as well as in the unbinding of the Na(+) complexed to the N-terminal EF-hand; (ii) the N-terminal EF-hand domain is the first to bind Ca(2+), and not the C-terminal, as usually accepted; and that (iii) Ca(2+) modulates the structural dynamics of H-III.

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