QRB Discovery最新文献

α-Synuclein is a small neuronal protein that reversibly associates with lipid membranes. The membrane interactions are believed to be central to the healthy function of this protein involved in synaptic plasticity and neurotransmitter release. α-Synuclein has been speculated to induce vesicle fusion as well as fission, processes which are analogous to each other but proceed in different directions and involve different driving forces. In the current work, we analyse α-synuclein-induced small unilamellar vesicle deformation from a thermodynamics point of view. We show that the structures interpreted in the literature as fusion intermediates are in fact a stable deformed state and neither fusion nor vesicle clustering occurs. We speculate on the driving force for the observed deformation and put forward a hypothesis that α-synuclein self-assembly on the lipid membrane precedes and induces membrane remodelling.

Changing torsional restraints on DNA is essential for the regulation of transcription. Torsional stress, introduced by RNA polymerase, can propagate along chromatin facilitating topological transitions and modulating the specific binding of transcription factors (TFs) to DNA. Despite the importance, the mechanistic details on how torsional stress impacts the TFs-DNA complexation remain scarce. Herein, we address the impact of torsional stress on DNA complexation with homologous human basic helix-loop-helix (BHLH) hetero- and homodimers: MycMax, MadMax and MaxMax. The three TF dimers exhibit specificity towards the same DNA consensus sequence, the E-box response element, while regulating different transcriptional pathways. Using microseconds-long atomistic molecular dynamics simulations together with the torsional restraint that controls DNA total helical twist, we gradually over- and underwind naked and complexed DNA to a maximum of ± 5°/bp step. We observe that the binding of the BHLH dimers results in a similar increase in DNA torsional rigidity. However, under torsional stress the BHLH dimers induce distinct DNA deformations, characterised by changes in DNA grooves geometry and a significant asymmetric DNA bending. Supported by bioinformatics analyses, our data suggest that torsional stress may contribute to the execution of differential transcriptional programs of the homologous TFs by modulating their collaborative interactions.

Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions.

Magnesium ions (Mg²⁺) are vital for RNA structure and cellular functions. Present efforts in RNA structure determination and understanding of RNA functions are hampered by the inability to accurately locate Mg²⁺ ions in an RNA. Here we present a machine-learning method, originally developed for computer visual recognition, to predict Mg²⁺ binding sites in RNA molecules. By incorporating geometrical and electrostatic features of RNA, we capture the key ingredients of Mg²⁺-RNA interactions, and from deep learning, predict the Mg²⁺ density distribution. Five-fold cross-validation on a dataset of 177 selected Mg²⁺-containing structures and comparisons with different methods validate the approach. This new approach predicts Mg²⁺ binding sites with notably higher accuracy and efficiency. More importantly, saliency analysis for eight different Mg²⁺ binding motifs indicates that the model can reveal critical coordinating atoms for Mg²⁺ ions and ion-RNA inner/outer-sphere coordination. Furthermore, implementation of the model uncovers new Mg²⁺ binding motifs. This new approach may be combined with X-ray crystallography structure determination to pinpoint the metal ion binding sites.

Interactions between proteins and single-stranded DNA (ssDNA) are crucial for many fundamental biological processes, including DNA replication and genetic recombination. Thus, understanding detailed mechanisms of these interactions is necessary to uncover regulatory rules occurring in all living cells. The RNA-binding Hfq is a pleiotropic bacterial regulator that mediates many aspects of nucleic acid metabolism. The protein notably mediates mRNA stability and translation efficiency by using stress-related small regulatory RNA as cofactors. In addition, Hfq helps to compact double-stranded DNA. In this paper, we focused on the action of Hfq on ssDNA. A combination of experimental methodologies, including spectroscopy and molecular imaging, has been used to probe the interactions of Hfq and its amyloid C-terminal region with ssDNA. Our analysis revealed that Hfq binds to ssDNA. Moreover, we demonstrate for the first time that Hfq drastically changes the structure and helical parameters of ssDNA, mainly due to its C-terminal amyloid-like domain. The formation of the nucleoprotein complexes between Hfq and ssDNA unveils important implications for DNA replication and recombination.

Chapter 1: COVID-19 pathogenesis poses paradoxes difficult to explain with traditional physiology. For instance, since type II pneumocytes are considered the primary cellular target of SARS-CoV-2; as these produce pulmonary surfactant (PS), the possibility that insufficient PS plays a role in COVID-19 pathogenesis has been raised. However, the opposite of predicted high alveolar surface tension is found in many early COVID-19 patients: paradoxically normal lung volumes and high compliance occur, with profound hypoxemia. That 'COVID anomaly' was quickly rationalised by invoking traditional vascular mechanisms-mainly because of surprisingly preserved alveolar surface in early hypoxemic cases. However, that quick rejection of alveolar damage only occurred because the actual mechanism of gas exchange has long been presumed to be non-problematic, due to diffusion through the alveolar surface. On the contrary, we provide physical chemical evidence that gas exchange occurs by an process of expansion and contraction of the three-dimensional structures of PS and its associated proteins. This view explains anomalous observations from the level of cryo-TEM to whole individuals. It encompasses results from premature infants to the deepest diving seals. Once understood, the COVID anomaly dissolves and is straightforwardly explained as covert viral damage to the 3D structure of PS, with direct treatment implications. As a natural experiment, the SARS-CoV-2 virus itself has helped us to simplify and clarify not only the nature of dyspnea and its relationship to pulmonary compliance, but also the fine detail of the PS including such features as water channels which had heretofore been entirely unexpected.

Chapter 2: For a long time, physical, colloid and surface chemistry have not intersected with physiology and cell biology as much as we might have hoped. The reasons are starting to become clear. The discipline of physical chemistry suffered from serious unrecognised omissions that rendered it ineffective. These foundational defects included omission of specific ion molecular forces and hydration effects. The discipline lacked a predictive theory of self-assembly of lipids and proteins. Worse, theory omitted any role for dissolved gases, O₂, N₂, CO₂, and their existence as stable nanobubbles above physiological salt concentration. Recent developments have gone some way to explaining the foam-like lung surfactant structures and function. It delivers O₂/N₂ as nanobubbles, and efflux of CO₂, and H₂O nanobubbles at the alveolar surface. Knowledge of pulmonary surfactant structure allows an explanation of the mechanism of corona virus entry, and differences in infectivity of different variants. CO₂ nanobubbles, resulting from metabolism passing through the molecular frit provided by the glycocalyx of venous tissue, f

Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties. The new computational tool, AlphaFold2, offers highly accurate predictions of three-dimensional protein structures. The glutamine, threonine and tyrosine (QTY) code provides a systematic method of rendering hydrophobic sequences into hydrophilic ones. Here, we present computational studies of native integral membrane GLUTs with 12 transmembrane helical segments determined by X-ray crystallography and CryoEM, comparing the AlphaFold2-predicted native structure to their water-soluble QTY variants predicted by AlphaFold2. In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, glutamine (Q), threonine (T) and tyrosine (Y) rendering them water-soluble. We present the superposed structures of native GLUTs and their water-soluble QTY variants. The superposed structures show remarkable similar residue mean square distance values between 0.47 and 3.6 Å (most about 1-2 Å) despite >44% transmembrane amino acid differences. We also show the differences of hydrophobicity patches between the native membrane transporters and their QTY variants. We explain the rationale why the membrane protein QTY variants become water-soluble. Our study provides insight into the differences between the hydrophobic helices and hydrophilic helices, and offers confirmation of the QTY method for studying multispan transmembrane proteins and other aggregated proteins through their water-soluble variants.

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.