Mg2+ Effect on Argonaute and RNA Duplex by Molecular Dynamics and Bioinformatics Implications

RNA interference (RNAi), mediated by small non-coding RNAs (e.g., miRNAs, siRNAs), influences diverse cellular functions. Highly complementary miRNA-target RNA (or siRNA-target RNA) duplexes are recognized by an Argonaute family protein (Ago2), and recent observations indicate that the concentration of Mg2+ ions influences miRNA targeting of specific mRNAs, thereby modulating miRNA-mRNA networks. In the present report, we studied the thermodynamic effects of differential [Mg2+] on slicing (RNA silencing cycle) through molecular dynamics simulation analysis, and its subsequent statistical analysis. Those analyses revealed different structural conformations of the RNA duplex in Ago2, depending on Mg2+ concentration. We also demonstrate that cation effects on Ago2 structural flexibility are critical to its catalytic/functional activity, with low [Mg2+] favoring greater Ago2 flexibility (e.g., greater entropy) and less miRNA/mRNA duplex stability, thus favoring slicing. The latter finding was supported by a negative correlation between expression of an Mg2+ influx channel, TRPM7, and one miRNA’s (miR-378) ability to downregulate its mRNA target, TMEM245. These results imply that thermodynamics could be applied to siRNA-based therapeutic strategies, using highly complementary binding targets, because Ago2 is also involved in RNAi slicing by exogenous siRNAs. However, the efficacy of a siRNA-based approach will differ, to some extent, based on the Mg2+ concentration even within the same disease type; therefore, different siRNA-based approaches might be considered for patient-to-patient needs

Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates.

Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing

Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

Objectives The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable time-series model. Results The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based time-series classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies

Protein NMR Structures Refined without NOE Data

<div><p>The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal “width” parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.</p></div

Frequency of the best structures in the (A) training and (B) test sets as a function of flat-bottom width from 0 to 10 Å.

<p>Frequency of the best structures in the (A) training and (B) test sets as a function of flat-bottom width from 0 to 10 Å.</p

Comparison between our refinement and the re-refinement structures^a^,^b.

a<p> See the footnotes in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108888#pone-0108888-t001" target="_blank">Table 1</a>.</p>b<p> A total of 24 NMR structures were used (lists are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108888#pone.0108888.s011" target="_blank">Table S9</a>). Because no corresponding X-ray structures exist, the TM-score cannot be measured.</p><p>Comparison between our refinement and the re-refinement structures^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108888#nt108" target="_blank">a</a>}^,^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108888#nt109" target="_blank">b</a>}.</p

Two flat-bottom distance potential functions: the same equilibrium distance (<i>d</i>) of 5 Å and two flat-bottom widths (<i>w</i>), 0 (blue) and 4 (red line).

<p>The used parameters are defined in Method section.</p

Secondary structure schemes of three conformations (original, refined, and native (X-ray)) of PDB (A) 1KOT and (B) 1FA4.

<p>The black dashed lines indicate the refined regions.</p

Total score change in the training set (A) considering all quality assessment scores (TM-score, NOE violations and “protein-like” scores), and (B) considering only “protein-like” scores.

<p>The optimal width for (A) is 4.0 Å, while it is gradually improved in (B).</p

Comparison of refined structures using the optimal width with original NMR structures.

a<p> Four Structural similarities are measured by TM-score program (reference atom: C_α): TM-score, RMSD, GDT-TS, and GDT-HA. The used reference structure is X-ray structure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108888#pone.0108888.s004" target="_blank">Table S2</a>).</p>b<p> Bold font numbers indicate better scores.</p>c<p> Number of violated NOE distances over 0.5, 1.0, and 2.0 Å. The NOE violations are measured with the experimental NOE data obtained from BMRB (Biological Magnetic Resonance Bank).</p>d<p> Ramachandran appearance measured using MolProbity.</p>e<p> Ramachandran appearance measured using PROCHECK.</p>f<p> Ramachandran appearance measured using WHAT_CHECK.</p><p>Comparison of refined structures using the optimal width with original NMR structures.</p