Pearson’s Correlation among Five Porcine Breast Milk Exosomes Samples.^a

a<p>The correlation was calculated for each pair of samples, based on the counts of identified plant miRNAs. The P-values are given in brackets.</p

The summary of translational parameters calculated in the model.

<p>Column description: () transcript length; () number of gene transcripts; () number of proteins produced from one transcript; () ribosome density in number of ribosomes per 100 codons; () number of ribosomes on a transcript; () initiation time in s; () elongation time in s; () mean elongation time of one transcript codon in ms; and () mean transcript lifetime in min (bacteria, yeast), or in h (humans). For all parameters, except and , the rows 1–15 were calculated for 1738, 4470, and 7494 genes for bacteria, yeast, and humans, respectively. For parameter and , the rows were calculated for 1574, 3425, and 6205 genes, respectively.</p

Workflow of human and pig breast milk exosomes sequencing data analysis.

<p>The reads collected from the 4 <i>H. sapiens</i> and 8 <i>S. scrofa</i> data sets were, each individually, cleaned and matched to known plant miRNAs to select all putative food-derived molecules. The matched tags were further subjected to few filtering steps, which resulted in elimination of all human and pig ncRNAs, repeat-associated RNAs, exon fragments and sequences successfully mapped to reference genomes, respectively. The remained reads were additionally examined to find and discard tags that with high probability represent specific microbiome sequences. As a second part of the analysis, the human targets prediction and annotation were carried out for select plant miRNAs. The aforementioned steps are detail described in the Materials and Methods section. Blue hexagons represent the data used and generated in the following processing/filtering steps (green rectangles) of the analysis.</p

Calculated protein abundance vs experimental studies.

<p>Correlations between protein abundances calculated in our model (as times ) and those obtained in experimental studies <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Newman1" target="_blank">[8]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Lu1" target="_blank">[11]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073943#pone.0073943-Nagaraj1" target="_blank">[13]</a>; n – sample size, – Spearman correlation coefficient and its 95% confidence interval.</p

Summary of data sets and variables used as an input of the model.

<p>Details on data parsing and calculations may be found in the main text. Cell lines and growth conditions (temperature and medium) denote those used in the ribosome profiling experiments. The numbers marked by an asterix were taken from the RNA Tools and Calculators section at the Invitrogen Website (<a href="http://www.invitrogen.com" target="_blank">www.invitrogen.com</a>, accessed April 2013). The coding sequences were downloaded from the following databases: NCBI (<a href="http://www.ncbi.nlm.nih.gov.ftp" target="_blank">www.ncbi.nlm.nih.gov.ftp</a>, accessed May 2012), SGD (<a href="http://www.yeastgenome.org" target="_blank">www.yeastgenome.org</a>, accessed June 2009), and UCSC (<a href="http://genome.ucsc.edu" target="_blank">http://genome.ucsc.edu</a>, accessed July 2012).</p

The lengths distribution examination of the exogenous-origin sRNA sequences.

<p>The summary of the sequence length distribution generated from (A) the <i>S. scrofa</i> and (B) <i>H. sapiens</i> tags, respectively, which remained after all processing and verification steps of the preformed bioinformatics analysis. Most of the generated reads were 21–24 nucleotides long.</p

Translation speed plot generated by Transimulation.

<p>An example plot of translation speed (in aa/sec) in relation to the coding sequence of one of the <i>E.coli</i> genes. To facilitate analysis, the plot was smoothed by calculating translation speed over a 10-codon sliding window. Similar plots for window sizes of 1, 2, 5, 10, 20, 30, and 50 codons are generated for all analyzed genes and sequences uploaded by the user.</p

List of Several Interesting Putative Human Targets for Select Plant miRNAs and Potential Impact of These Food-Derived Molecules on Human Organism.

<p>List of Several Interesting Putative Human Targets for Select Plant miRNAs and Potential Impact of These Food-Derived Molecules on Human Organism.</p

Additional file 1: of Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

Snippets.zip—IPython notebooks containing the code for each example

DataSheet1_Improvement of native structure-based peptides as efficient inhibitors of protein-protein interactions of SARS-CoV-2 spike protein and human ACE2.PDF

New pathogens responsible for novel human disease outbreaks in the last two decades are mainly the respiratory system viruses. Not different was the last pandemic episode, caused by infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One of the extensively explored targets, in the recent scientific literature, as a possible way for rapid development of COVID-19 specific drug(s) is the interaction between the receptor-binding domain of the virus’ spike (S) glycoprotein and human receptor angiotensin-converting enzyme 2 (hACE2). This protein-protein recognition process is involved in the early stages of the SARS-CoV-2 life cycle leading to the host cell membrane penetration. Thus, disrupting this interaction may block or significantly reduce the infection caused by the novel pathogen. Previously we have designed (by in silico structure-based analysis) three very short peptides having sequences inspirited by hACE2 native fragments, which effectively bind to the SARS-CoV-2 S protein and block its interaction with the human receptor. In continuation of the above mentioned studies, here we presented an application of molecular modeling approach resulting in improved binding affinity of the previously proposed ligand and its enhanced ability to inhibit meaningful host-virus protein-protein interaction. The new optimized hexapeptide binds to the virus protein with affinity one magnitude higher than the initial ligand and, as a very short peptide, has also great potential for further drug development. The peptide-based strategy is rapid and cost-effective for developing and optimizing efficient protein-protein interactions disruptors and may be successfully applied to discover antiviral candidates against other future emerging human viral infections.</p