Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding.

The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we show that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that is consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein.

pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science.

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.

Molecular mechanisms associated with xylan degradation by Xanthomonas plant pathogens.

Xanthomonas pathogens attack a variety of economically relevant plants, and their xylan CUT system (carbohydrate utilization with TonB-dependent outer membrane transporter system) contains two major xylanase-related genes, xynA and xynB, which influence biofilm formation and virulence by molecular mechanisms that are still elusive. Herein, we demonstrated that XynA is a rare reducing end xylose-releasing exo-oligoxylanase and not an endo-ß-1,4-xylanase as predicted. Structural analysis revealed that an insertion in the ß7-α7 loop induces dimerization and promotes a physical barrier at the +2 subsite conferring this unique mode of action within the GH10 family. A single mutation that impaired dimerization became XynA active against xylan, and high endolytic activity was achieved when this loop was tailored to match a canonical sequence of endo-ß-1,4-xylanases, supporting our mechanistic model. On the other hand, the divergent XynB proved to be a classical endo-ß-1,4-xylanase, despite the low sequence similarity to characterized GH10 xylanases. Interestingly, this enzyme contains a calcium ion bound nearby to the glycone-binding region, which is required for catalytic activity and structural stability. These results shed light on the molecular basis for xylan degradation by Xanthomonas and suggest how these enzymes synergistically assist infection and pathogenesis. Our findings indicate that XynB contributes to breach the plant cell wall barrier, providing nutrients and facilitating the translocation of effector molecules, whereas the exo-oligoxylanase XynA possibly participates in the suppression of oligosaccharide-induced immune responses.

Extracellular matrix stiffness regulates degradation of MST2 via SCF ßTrCP.

The Hippo pathway plays central roles in relaying mechanical signals during development and tumorigenesis, but how the proteostasis of the Hippo kinase MST2 is regulated remains unknown. Here, we found that chemical inhibition of proteasomal proteolysis resulted in increased levels of MST2 in human breast epithelial cells. MST2 binds SCFßTrCP E3 ubiquitin ligase and silencing ßTrCP resulted in MST2 accumulation. Site-directed mutagenesis combined with computational molecular dynamics studies revealed that ßTrCP binds MST2 via a non-canonical degradation motif. Additionally, stiffer extracellular matrix, as well as hyperactivation of integrins resulted in enhanced MST2 degradation mediated by integrin-linked kinase (ILK) and actomyosin stress fibers. Our study uncovers the underlying biochemical mechanisms controlling MST2 degradation and underscores how alterations in the microenvironment rigidity regulate the proteostasis of a central Hippo pathway component.

Discovery and structural characterization of chicoric acid as a SARS-CoV-2 nucleocapsid protein ligand and RNA binding disruptor.

The nucleocapsid (N) protein plays critical roles in coronavirus genome transcription and packaging, representing a key target for the development of novel antivirals, and for which structural information on ligand binding is scarce. We used a novel fluorescence polarization assay to identify small molecules that disrupt the binding of the N protein to a target RNA derived from the SARS-CoV-2 genome packaging signal. Several phenolic compounds, including L-chicoric acid (CA), were identified as high-affinity N-protein ligands. The binding of CA to the N protein was confirmed by isothermal titration calorimetry, 1H-STD and 15N-HSQC NMR, and by the crystal structure of CA bound to the N protein C-terminal domain (CTD), further revealing a new modulatory site in the SARS-CoV-2 N protein. Moreover, CA reduced SARS-CoV-2 replication in cell cultures. These data thus open venues for the development of new antivirals targeting the N protein, an essential and yet underexplored coronavirus target.

Cryo-EM structure of the mature and infective Mayaro virus at 4.4 Å resolution reveals features of arthritogenic alphaviruses.

Mayaro virus (MAYV) is an emerging arbovirus of the Americas that may cause a debilitating arthritogenic disease. The biology of MAYV is not fully understood and largely inferred from related arthritogenic alphaviruses. Here, we present the structure of MAYV at 4.4 Å resolution, obtained from a preparation of mature, infective virions. MAYV presents typical alphavirus features and organization. Interactions between viral proteins that lead to particle formation are described together with a hydrophobic pocket formed between E1 and E2 spike proteins and conformational epitopes specific of MAYV. We also describe MAYV glycosylation residues in E1 and E2 that may affect MXRA8 host receptor binding, and a molecular "handshake" between MAYV spikes formed by N262 glycosylation in adjacent E2 proteins. The structure of MAYV is suggestive of structural and functional complexity among alphaviruses, which may be targeted for specificity or antiviral activity.

Protein folding creates structure-based, noncontiguous consensus phosphorylation motifs recognized by kinases.

Linear consensus motifs are short contiguous sequences of residues within a protein that can form recognition modules for protein interaction or catalytic modification. Protein kinase specificity and the matching of kinases to substrates have been mostly defined by phosphorylation sites that occur in linear consensus motifs. However, phosphorylation can also occur within sequences that do not match known linear consensus motifs recognized by kinases and within flexible loops. We report the identification of Thr(253) in α-tubulin as a site that is phosphorylated by protein kinase C ßI (PKCßI). Thr(253) is not part of a linear PKC consensus motif. Instead, Thr(253) occurs within a region on the surface of α-tubulin that resembles a PKC phosphorylation site consensus motif formed by basic residues in different parts of the protein, which come together in the folded protein to form the recognition motif for PKCßI. Mutations of these basic residues decreased substrate phosphorylation, confirming the presence of this "structurally formed" consensus motif and its importance for the protein kinase-substrate interaction. Analysis of previously reported protein kinase A (PKA) and PKC substrates identified sites within structurally formed consensus motifs in many substrates of these two kinase families. Thus, the concept of consensus phosphorylation site motif needs to be expanded to include sites within these structurally formed consensus motifs.

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem.

Rare variants are becoming the new candidates in the search for genetic variants that predispose individuals to a phenotype of interest. Their low prevalence in a population requires the development of dedicated detection and analytical methods. A family-based approach could greatly enhance their detection and interpretation because rare variants are nearly family specific. In this report, we test several distinct approaches for analyzing the information provided by rare and common variants and how they can be effectively used to pinpoint putative candidate genes for follow-up studies. The analyses were performed on the mini-exome data set provided by Genetic Analysis Workshop 17. Eight approaches were tested, four using the trait's heritability estimates and four using QTDT models. These methods had their sensitivity, specificity, and positive and negative predictive values compared in light of the simulation parameters. Our results highlight important limitations of current methods to deal with rare and common variants, all methods presented a reduced specificity and, consequently, prone to false positive associations. Methods analyzing common variants information showed an enhanced sensibility when compared to rare variants methods. Furthermore, our limited knowledge of the use of biological databases for gene annotations, possibly for use as covariates in regression models, imposes a barrier to further research.