Molecular Evolution of the Neuropeptide S Receptor

The neuropeptide S receptor (NPSR) is a recently deorphanized member of the G protein-coupled receptor (GPCR) superfamily and is activated by the neuropeptide S (NPS). NPSR and NPS are widely expressed in central nervous system and are known to have crucial roles in asthma pathogenesis, locomotor activity, wakefulness, anxiety and food intake. The NPS-NPSR system was previously thought to have first evolved in the tetrapods. Here we examine the origin and the molecular evolution of the NPSR using in-silico comparative analyses and document the molecular basis of divergence of the NPSR from its closest vertebrate paralogs. In this study, NPSR-like sequences have been identified in a hemichordate and a cephalochordate, suggesting an earlier emergence of a NPSR-like sequence in the metazoan lineage. Phylogenetic analyses revealed that the NPSR is most closely related to the invertebrate cardioacceleratory peptide receptor (CCAPR) and the group of vasopressin-like receptors. Gene structure features were congruent with the phylogenetic clustering and supported the orthology of NPSR to the invertebrate NPSR-like and CCAPR. A site-specific analysis between the vertebrate NPSR and the well studied paralogous vasopressin-like receptor subtypes revealed several putative amino acid sites that may account for the observed functional divergence between them. The data can facilitate experimental studies aiming at deciphering the common features as well as those related to ligand binding and signal transduction processes specific to the NPSR

Schematic diagram of the human neuropeptide S receptor.

<p>The sequence is drawn and labeled according to the extracellular, intracellular and transmembrane regions. The boundaries of the three regions were based on the definition of these regions for human NPSR [GenBank: NP_997055] given by the Ballesteros-Weinstein nomenclature and TMHMM program. The most conserved residue in each transmembrane helix is denoted with red text. The first and last amino acid residue numbers in each helix is indicated using Ballesteros-Weinstein numbering scheme. Residues that represent sites of functional divergence between the NPSR and the V1AR, V1BR, V2R and OTR subtypes are marked with outlined circles. Residue-wise functional divergence of NPSR with each subtype of vasopressin-like receptor is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034046#pone.0034046.s009" target="_blank">Data S3</a>.</p

Phylogenetic relationship of the NPSR, CCAPR, GnRHR and vasopressin-like receptors from vertebrates and invertebrates.

<p>Bayesian tree of NPSR (red), invertebrate NPSR-like receptor (orange), CCAPR (green), V1AR (blue), V1BR (blue), OTR (blue), V2R (blue), GnRHR (vertebrate and invertebrate Gonadotropin releasing hormone receptor) and VPR (invertebrate vasopressin-like receptor) (blue) sequences. The tree was generated using the Bayesian approach in MrBayes 3.1.2 using JTT+F+I+G model. Bayesian posterior probabilities are marked near branches as a percentage and are used as confidence values of tree branches. Nodes were compressed to represent the animal lineages. Scale bar represents the number of estimated changes per site for a unit of branch length. The receptor group abbreviations, names and accession numbers of the sequences and common and binomial names of the species are as listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034046#pone.0034046.s006" target="_blank">Table S2</a>. In this figure and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034046#pone-0034046-g004" target="_blank">Figure 4</a>, the sequence names marked with * and +symbols represent manually corrected sequences at the N terminus and C terminus, respectively. Sequence names marked with ^‡ symbol in this figure represent fragmented sequences.</p

Type I and Type II functional divergence between the NPSR and vasopressin-like receptor subtypes.

<p><i>Note:</i> θ denotes the coefficient of functional divergence. SE is standard error. z score is the value of confidence and is obtained by θ/SE. P value is the probability of the z score, which had a value of <0.0001 in all comparisons. The symbol - indicates the absence of divergent sites. Abbreviations: TM – Transmembrane helices, ECL – Extracellular loops, ICL – Intracellular loops.</p

The Pandemic, Infodemic, and People’s Resilience in India: Viewpoint

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused widespread fear and stress. The pandemic has affected everyone, everywhere, and created systemic inequities, leaving no one behind. In India alone, more than 34,094,373 confirmed COVID-19 cases and 452,454 related deaths have been reported as of October 19, 2021. Around May 2021, the daily number of new COVID-19 cases crossed the 400,000 mark, seriously hampering the health care system. Despite the devastating situation, the public response was seen through their efforts to come forward with innovative ideas for potential ways to combat the pandemic, for instance, dealing with the shortage of oxygen cylinders and hospital bed availability. With increasing COVID-19 vaccination rates since September 2021, along with the diminishing number of daily new cases, the country is conducting preventive and preparatory measures for the third wave. In this article, we propose the pivotal role of public participation and digital solutions to re-establish our society and describe how Sustainable Development Goals (SDGs) can support eHealth initiatives and mitigate infodemics to tackle a postpandemic situation. This viewpoint reflects that the COVID-19 pandemic has featured a need to bring together research findings across disciplines, build greater coherence within the field, and be a driving force for multi-sectoral, cross-disciplinary collaboration. The article also highlights the various needs to develop digital solutions that can be applied to pandemic situations and be reprocessed to focus on other SDGs. Promoting the use of digital health care solutions to implement preventive measures can be enhanced by public empowerment and engagement. Wearable technologies can be efficiently used for remote monitoring or home-based care for patients with chronic conditions. Furthermore, the development and implementation of informational tools can aid the improvement of well-being and dissolve panic-ridden behaviors contributing toward infodemics. Thus, a call to action for an observatory of digital health initiatives on COVID-19 is required to share the main conclusions and lessons learned in terms of resilience, crisis mitigation, and preparedness

Comparison of ISMBLab-LIG and COACH predictions.

<p>(a) Prediction results are compared for 81 model structures built by MODELLER with template sequence alignment coverage > 80% and sequence ID < 40%. The query sequences are from the S48b dataset (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160315#sec009" target="_blank">Methods</a>). For each model structure, a pair of BDT scores were calculated based on the LBS predictions with ISMBLab-LIG (red triangles) and COACH (black dot). These BDT scores are plotted against RMSD between modeled and actual LBS (x-axis), for which the definition is the same as the x-axis of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160315#pone.0160315.g004" target="_blank">Fig 4</a>. The red dotted line and the black dotted line are the linear regression lines for the ISMBLab-LIG data points and COACH data points respectively; the corresponding R², slope (β) and P-value from F-test are calculated by SigmaPlot 12.0 and colored in red for ISMBLab-LIG predictions and black for COACH predictions. For each pair of predictions on the same model structure, a winner was assigned to either ISMBLab-LIG or COACH based on the BDT score. For each subgroup of the data points divided by the vertical blue dashed lines, the black number versus the red number above the data points in the figure panel indicates the number of winners of COACH prediction (in black) versus the number of winners of ISMBLab-LIG prediction (in red). (b) The description is the same as in (a), except that the data points are plotted against the sequence ID % between the query and the template used in MODELLER comparative modeling.</p

Examples of ligand binding site predictions in S48b testing set.

<p>Panels (a) to (e) show the prediction examples of five proteins from S48b testing set with MCC of 0.9, 0.77, 0.64, 0.5 and 0.35, respectively. The left-hand side structures are color-coded by atom-based prediction confidence level. The color bar at the bottom of this column shows the color scheme for normalized prediction confidence level. The seed atoms are colored in red of various level of depth. The protein structures of the middle column show the residue-based ligand binding site predictions. The residues colored in red or orange represent the positive residues of the predicted patches in the ligand binding sites. The red atoms were predicted with prediction confidence level greater than 0.5; other atoms in the positive residues of predicted patches with prediction confidence level less than 0.5 are colored in orange. The right-hand side structures show the surface atoms in close contact with the ligands. The atom colored in red are within 4.5 Å distance to any heavy atom of corresponding ligand. The PDB code name and the MCC for each of the examples are also shown. The complete prediction benchmarks for all testing sets are available for interactive examination from the ISMBLab web server: <a href="http://ismblab.genomics.sinica.edu.tw/" target="_blank">http://ismblab.genomics.sinica.edu.tw/</a>>benchmark>Protein-Ligand.</p

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

<div><p>Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.</p></div

The definition of protein and ligand atom types.

<p>The definition of protein and ligand atom types.</p

ISMBLab-LIG residue-based prediction performances benchmarked with published datasets.

<p>ISMBLab-LIG residue-based prediction performances benchmarked with published datasets.</p