Journal of Molecular Biology最新文献

Human rhinoviruses (RV) are among the most frequent human pathogens. As major causative agents of common colds they originate serious socioeconomic problems and huge expenditure every year, and they also exacerbate severe respiratory diseases. No anti-rhinoviral drugs or vaccines are available so far. Antiviral drug design may benefit from an understanding of the role during the infectious cycle of the interactions in the virion between the capsid and the viral nucleic acid. The genomic RNA inside the human RV virion forms a dodecahedral cage made of 30 double-stranded RNA elements that interact with equivalent sites at the capsid inner wall. RNA dodecahedral cages also occur in distantly related insect and plant viruses. However, the functional role(s) of the interactions between any dodecahedral cage and the capsid remained to be established. Here we describe an extensive structure-function mutational analysis of the capsid-RNA dodecahedral cage interface in the RV virion, to dissect the role of the interactions between the capsid and the cage-forming RNA duplexes in: (i) infection by RV; (ii) virus biological fitness; (iii) virion assembly; (iv) virion stability; and (v) viral RNA uncoating. The results reveal that the capsid-bound dsRNA dodecahedral cage in the human RV virion is a multifunctional structural element. Two structurally overlapping subsets of RNA duplex-capsid interactions promote virus infectivity and biological fitness by respectively facilitating virion assembly or restraining the untimely, unproductive uncoating of the viral RNA genome. These results provide new insights into virion morphogenesis and genome uncoating, and have implications for antiviral drug design.

Transcription is a key cell process that consists of synthesizing several copies of RNA from a gene DNA sequence. This process is highly regulated and closely linked to the ability of transcription factors to bind specifically to DNA. TFinder is an easy-to-use Python web portal allowing the identification of Individual Motifs (IM) such as Transcription Factor Binding Sites (TFBS). Using the NCBI API, TFinder extracts either promoter or gene terminal regulatory regions, through a simple query of NCBI gene name or ID. It enables simultaneous analysis across five different species for an unlimited number of genes. TFinder searches for Individual Motifs in different formats, including IUPAC codes and JASPAR entries. Moreover, TFinder also allows de novo generations of a Position Weight Matrix (PWM) and the use of already established PWM. Finally, the data are provided in a tabular and a graph format showing the relevance and the P-value of the Individual Motifs found as well as their location relative to the Transcription Start Site (TSS) or the terminal region of the gene. The results are then sent by email to users facilitating the subsequent data analysis and sharing. TFinder is written in Python and freely available on GitHub under the MIT license: https://github.com/Jumitti/TFinder. It can be accessed as a web application implemented in Streamlit at https://tfinder-ipmc.streamlit.app. Resources are available on Streamlit "Resources" tab. TFINDER strength is that it relies on an all-in-one intuitive tool allowing users inexperienced with bioinformatics tools to retrieve gene regulatory regions sequences in multiple species and to search for individual motifs in a huge number of genes.

Aβ-amyloid plaques and cerebral amyloid angiopathy (CAA) in the brain are pathological hallmarks of Alzheimer's disease (AD) and vascular dementia. The spreading of Aβ amyloidosis in the brain appears to be mediated by a seeding mechanism, where preformed fibrils (called seeds) accelerate Aβ fibril formation by bypassing the rate-determining nucleation step. Several studies have demonstrated that Aβ amyloidosis can be induced in transgenic mice, producing human Aβ, by injecting Aβ-rich brain extracts (seeds) derived from transgenic mice and human AD brains. However, studies on recombinant seeds are limited. Therefore, we investigated the seeding activity of pure recombinant human Aβ fibrils of different compositions. Seeds were inoculated into APP23 mice at the age of 3 months and were analyzed after 6 months of incubation. Recombinant fibril seeds made from Aβ-peptides with an N-terminal methionine (i.e. (preformed fibrils from AβM1-42, AβM1-40, and AβM1-40 + AβM1-42) accelerated Aβ-amyloid plaque formation in vivo compared to non-inoculated transgenic control mice of the same age. In addition, all seeds induced CAA pathology. Interestingly, AβM1-42 containing seeds produced significantly more CAA and amyloid plaques than seeds containing pure AβM1-40, which was surprising given that APP23 mice produce approximately four-fold more Aβ1-40 substrate than Aβ1-42. This study showed that AβM1-42 fibrils are highly potent in seeding CAA and implies that conformational templating occurs in amyloid plaque as deduced by comparative amyloid ligand staining. Our results verify that recombinant Aβ fibrils are transmissible amyloids, and that in vivo seeding can accelerate, and redirect Aβ amyloidosis patterns compared to spontaneous age dependent amyloidosis.

AMPylation is a post-translational modification (PTM) whereby adenosine monophosphate (AMP) from adenosine triphosphate (ATP) is transferred onto protein hydroxyl groups of serine, threonine, or tyrosine. Recently, an actin-dependent AMPylase namely LnaB from the bacterial pathogen Legionella pneumophila was found to AMPylate phosphate groups of phosphoribosylated ubiquitin and Src family kinases. LnaB represents an evolutionarily distinct family of AMPylases with conserved active site Ser-His-Glu residues. Here, we capture the structure of the LnaB-actin complex in a putative intermediate state via single-particle cryogenic electron microscopy (cryo-EM) and find that the catalytic histidine of LnaB is covalently attached to AMP through a phosphoramidate linkage at the Nδ1 atom. This observation provides direct structural evidence of histidine AMPylation as a PTM and implies the possibility of covalent catalysis in LnaB-mediated AMPylation, a mechanism distinct from known AMPylases. Subsequent biochemical studies confirm the observed AMP binding site and provide additional insights into the catalytic properties of LnaB. Together, our work highlights the power of cryo-EM in capturing labile PTMs and transient species during enzymatic reactions, while opening new avenues of mechanistic investigation into the LnaB family.

The multidrug transporter P-glycoprotein (P-gp), is pivotal in exporting various chemically dissimilar amphipathic compounds including anti-cancer drugs, thus causing multidrug resistance during cancer treatment. P-gp is composed of two transmembrane domains (TMDs), each containing six homologous transmembrane helices (TMHs). Among these helices, TMH 6 and 12 align oppositely, lining a drug-binding pocket in the transmembrane region which acts as a pathway for drug efflux. Previously, we demonstrated that specific mutations within TMH 6 and 12 resulted in loss of substrate efflux and altered the transport direction from efflux to uptake for some substrates. This suggested the presence of a regulatory switch that governs the direction of transport. In this study, we sought to elucidate the mechanism of switch modulation of the uptake function by engineering several mutants via substituting specific residues in TMH 6 and 12. We discovered that the alanine substitution of four residues (V974, L975, V977, and F978) within the upper region of TMH 12, along with three residues (V334, F336, and F343) within TMH 6, was sufficient to convert P-gp from an efflux to an uptake pump. Additional mutagenesis of the residues in the middle region of TMH 12 revealed that the uptake function, like efflux, is reversible. Further studies, including molecular dynamics simulations, revealed that the switch region appears to act during the substrate translocation step. We propose that the switch region in TMH 6 and 12, which modulates the direction of transport by P-gp, provides a novel approach to selectively target P-gp-expressing cancer cells.

O-linked glycosylation is a crucial post-transcriptional modification that regulates protein function and biological processes. Dysregulation of this process is associated with various diseases, underscoring the need to accurately identify O-linked glycosylation sites on proteins. Current experimental methods for identifying O-linked threonine glycosylation (OTG) sites are often complex and costly. Consequently, developing computational tools that predict these sites based on protein features is crucial. Such tools can complement experimental approaches, enhancing our understanding of the role of OTG dysregulation in diseases and uncovering potential therapeutic targets. In this study, we developed DOGpred, a deep learning-based predictor for precisely identifying human OTGs using high-latent feature representations. Initially, we extracted nine different conventional feature descriptors (CFDs) and nine pre-trained protein language model (PLM)-based embeddings. Notably, each feature was encoded as a 2D tensor, capturing both the sequential and inherent feature characteristics. Subsequently, we designed a stacked convolutional neural network (CNN) module to learn spatial feature representations from CFDs and a stacked recurrent neural network (RNN) module to learn temporal feature representations from PLM-based embeddings. These features were integrated using attention-based fusion mechanisms to generate high-level feature representations for final classification. Ablation analysis and independent tests demonstrated that the optimal model (DOGpred), employing a stacked 1D CNN and a stacked attention-based RNN module with cross-attention feature fusion, achieved the best performance on the training dataset and significantly outperformed machine learning-based single-feature models and state-of-the-art methods on independent datasets. Furthermore, DOGpred is publicly available at https://github.com/JeonRPM/DOGpred/ for free access and usage.

RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various diseases. Accurate identification of ac4C modification sites is essential for unraveling its functional implications. However, currently available experimental methods suffer from drawbacks such as lengthy detection times, complexity, and high costs, resulting in low efficiency and accuracy in prediction. Although several bioinformatics methods have been proposed and have advanced the prediction of ac4C modification sites, there is still ample room for improvement. In this research, we propose a novel deep learning model, ERNIE-ac4C, which combines the ERNIE-RNA language model and a two-dimensional Convolutional Neural Network (CNN). ERNIE-ac4C utilizes the fusion of sequence features and attention map features to predict ac4C modification sites. ERNIE-ac4C surpasses other state-of-the-art deep learning methods, demonstrating superior accuracy and effectiveness. The availability of the code on GitHub (https://github.com/lrlbcxdd/ERNIEac4C.git) and our openness to feedback from the research community contribute to the model's accessibility and its potential for further advancements. Our study provides valuable insights into ac4C research and enhances our understanding of the functional consequences of RNA modifications.

Determining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilising interaction in which the ends of helices in multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than in an inner loop. We introduce a new notion of sparsity, which we call replaceability. Replaceability is a more general condition and applicable in more places than the triangle inequality that is used by previous sparse folding methods. We also introduce non-monotonic candidate lists as an additional sparsification tool. Existing usages of the triangle inequality for sparsification can be thought of as an application of both replaceability and monotonicity together. The modified recurrences along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna, which we show to have the fastest exact (non-heuristic) implementation of RNA folding under the complete Turner 2004 model with coaxial stacking, out of several popular RNA folding tools supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information. The memerna package 0.1 release is available at https://github.com/Edgeworth/memerna/tree/release/0.1.

Protein phosphorylation is a fundamental cellular regulatory mechanism that governs the activation and deactivation of numerous proteins. In two-component signaling transduction pathways, the phosphorylation of response regulator proteins and their subsequent diffusion play pivotal roles in signal transmission. However, the impact of protein phosphorylation on their dispersion properties remains elusive. In this study, using the response regulator CheY in bacterial chemotaxis as a model, we performed comprehensive measurements of the spatial distributions and diffusion characteristics of CheY and phosphorylated CheY through single-molecule tracking within live cells. We discovered that phosphorylation significantly enhances diffusion and mitigates the constraining influence of the cell membrane on these proteins. Moreover, we observed that ATP-dependent fluctuations also promote protein diffusion and reduce the restraining effect of the cell membrane. These findings highlight important effects of phosphorylation beyond protein activation.

Allosteric regulation is crucial for biological processes like signal transduction, transcriptional regulation, and metabolism, yet the mechanisms and macromolecular properties that govern it are still not well understood. Several methods have been developed over the years to study allosterism through different angles. Among the possible ways to study allosterism, information-theoretic approaches, like AlloHubMat or GSAtools, can be particularly effective due to their use of robust statistics and the possibility to be combined with graph analysis. These methods capture local conformational changes associated with global motions from molecular dynamics simulations through the use of a Structural Alphabet, which simplifies the complexity of the Cartesian space by reducing the dimensionality down to a string of encoded fragments, representing sets of internal coordinates that still capture the overall conformation changes. In this work, we present "AllohubPy," an improved and standardized methodology of AlloHubMat and GSAtools coded in Python. We analyse the performance, limitations and sampling requirements of AllohubPy by using extensive molecular dynamics simulations of model allosteric systems and apply convergence analysis techniques to estimate result reliability. Additionally, we expand the methodology to use different dimensionality reduction Structural Alphabets, such as the 3DI alphabet, and integrate Protein Language Models (PLMs) to refine allosteric hub communication detection by monitoring the detected evolutionary constraints. Overall, AllohubPy expands its preceding methods and simplifies the use and reliability of the method to effectively capture dynamic allosteric motions and residue pathways. AllohubPy is freely available on GitHub (https://github.com/Fraternalilab/AlloHubPy) as a package and as a Jupyter Notebook.