Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task

RNA, whose functionality is largely determined by its structure, plays an important role in many biological activities. The prediction of pairwise structural proximity between each nucleotide of an RNA sequence can characterize the structural information of the RNA. Historically, this problem has been tackled by machine learning models using expert-engineered features and trained on scarce labeled datasets. Here, we find that the knowledge learned by a protein-coevolution Transformer-based deep neural network can be transferred to the RNA contact prediction task. As protein datasets are orders of magnitude larger than those for RNA contact prediction, our findings and the subsequent framework greatly reduce the data scarcity bottleneck. Experiments confirm that RNA contact prediction through transfer learning using a publicly available protein model is greatly improved. Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.Comment: Minor revision. The code is available at https://github.com/yiren-jian/CoT-RNA-Transfe

Exploration of an Actin Promoter-Based Transient Expression Vector to Trace the Cellular Localization of Nucleorhabdovirus Proteins in Leafhopper Cultured Cells

Continuously cultured cell lines derived from planthopper and leafhopper have greatly facilitated the investigation of rice viruses transmitted by these insects. However, the lack of a suitable transient expression vector has limited their utility. Here, by cloning and analyzing the promoter sequence of the gene encoding cytoplasmic actin from the leafhopper Nephotettix cincticeps, we successfully developed the first efficient transient expression vector for cultured leafhopper cells, which can also be used to express exogenous proteins in other insect culture cell lines, including those derived from Recilia dorsalis leafhopper and Spodoptera frugiperda (Sf9). Furthermore, insertion of the Hr5 viral enhancer element and knockdown of the endogenous Dicer2 gene notably improved the vector’s expression efficiency in leafhopper cells. Using the optimized vector, we have for the first time traced the cellular localization of the proteins encoded by rice yellow stunt virus (RYSV) in cells of its insect vector and demonstrated that P6 protein is a component of the viroplasm

Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years

Possible Mechanisms of Lymphopenia in Severe Tuberculosis

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis). In lymphopenia, T cells are typically characterized by progressive loss and a decrease in their count results. Lymphopenia can hinder immune responses and lead to systemic immunosuppression, which is strongly associated with mortality. Lymphopenia is a significant immunological abnormality in the majority of patients with severe and advanced TB, and its severity is linked to disease outcomes. However, the underlying mechanism remains unclear. Currently, the research on the pathogenesis of lymphopenia during M. tuberculosis infection mainly focuses on how it affects lymphocyte production, survival, or tissue redistribution. This includes impairing hematopoiesis, inhibiting T-cell proliferation, and inducing lymphocyte apoptosis. In this study, we have compiled the latest research on the possible mechanisms that may cause lymphopenia during M. tuberculosis infection. Lymphopenia may have serious consequences in severe TB patients. Additionally, we discuss in detail potential intervention strategies to prevent lymphopenia, which could help understand TB immunopathogenesis and achieve the goal of preventing and treating severe TB

Molecular dynamics simulation reveals insights into the mechanism of unfolding by the A130T/V mutations within the MID1 zinc-binding Bbox1 domain.

The zinc-binding Bbox1 domain in protein MID1, a member of the TRIM family of proteins, facilitates the ubiquitination of the catalytic subunit of protein phosphatase 2A and alpha4, a protein regulator of PP2A. The natural mutation of residue A130 to a valine or threonine disrupts substrate recognition and catalysis. While NMR data revealed the A130T mutant Bbox1 domain failed to coordinate both structurally essential zinc ions and resulted in an unfolded structure, the unfolding mechanism is unknown. Principle component analysis revealed that residue A130 served as a hinge point between the structured β-strand-turn-β-strand (β-turn-β) and the lasso-like loop sub-structures that constitute loop1 of the ββα-RING fold that the Bbox1 domain adopts. Backbone RMSD data indicate significant flexibility and departure from the native structure within the first 5 ns of the molecular dynamics (MD) simulation for the A130V mutant (>6 Å) and after 30 ns for A130T mutant (>6 Å). Overall RMSF values were higher for the mutant structures and showed increased flexibility around residues 125 and 155, regions with zinc-coordinating residues. Simulated pKa values of the sulfhydryl group of C142 located near A130 suggested an increased in value to ~9.0, paralleling the increase in the apparent dielectric constants for the small cavity near residue A130. Protonation of the sulfhydryl group would disrupt zinc-coordination, directly contributing to unfolding of the Bbox1. Together, the increased motion of residues of loop 1, which contains four of the six zinc-binding cysteine residues, and the increased pKa of C142 could destabilize the structure of the zinc-coordinating residues and contribute to the unfolding

Structural properties of the MID1 Bbox1 domain.

<p>(A) The ensemble of 13 structures generated from NMR-derived restraints (PDB code: 2FFW). (B) Magnitude of the fluctuation represented as eigenvectors of the Bbox1 ensemble of structures. The conformational fluctuations indicate both magnitude and directions (arrows) and are derived from PCA analysis. (C) The fluctuations of the average NMR structure are shown as a function of residue number. (D) Zoomed-in snapshot of the small cavity near residue A130. The blue dots represent the solvent accessible surface.</p

Structural analysis of the MD simulation results.

<p>(A) Backbone RMSFs for WT (blue), A130T (red), and A130V (orange) during the MD simulations. (B) Time evolution of the secondary structural elements, based on DSSP classification, of the wild type and mutant proteins. (C) The angle formed by the Cα atoms of D129, A130, and V131. (D) The angle fluctuations for WT (blue), A130T (red), and A130V (orange) during the MD simulations with the corresponding histogram to the side.</p