A Comparison of Structural and Evolutionary Attributes of <i>Escherichia coli</i> and <i>Thermus thermophilus</i> Small Ribosomal Subunits: Signatures of Thermal Adaptation

<div><p>Here we compare the structural and evolutionary attributes of <i>Thermus thermophilus</i> and <i>Escherichia coli</i> small ribosomal subunits (SSU). Our results indicate that with few exceptions, thermophilic 16S ribosomal RNA (16S rRNA) is densely packed compared to that of mesophilic at most of the analogous spatial regions. In addition, we have located species-specific cavity clusters (SSCCs) in both species. <i>E. coli</i> SSCCs are numerous and larger compared to <i>T. thermophilus</i> SSCCs, which again indicates densely packed thermophilic 16S rRNA. Thermophilic ribosomal proteins (r-proteins) have longer disordered regions than their mesophilic homologs and they experience larger disorder-to-order transitions during SSU-assembly. This is reflected in the predicted higher conformational changes of thermophilic r-proteins compared to their mesophilic homologs during SSU-assembly. This high conformational change of thermophilic r-proteins may help them to associate with the 16S ribosomal RNA with high complementary interfaces, larger interface areas, and denser molecular contacts, compared to those of mesophilic. Thus, thermophilic protein-rRNA interfaces are tightly associated with 16S rRNA than their mesophilic homologs. Densely packed 16S rRNA interior and tight protein-rRNA binding of <i>T. thermophilus</i> (compared to those of <i>E. coli</i>) are likely the signatures of its thermal adaptation. We have found a linear correlation between the free energy of protein-RNA interface formation, interface size, and square of conformational changes, which is followed in both prokaryotic and eukaryotic SSU. Disorder is associated with high protein-RNA interface polarity. We have found an evolutionary tendency to maintain high polarity (thereby disorder) at protein-rRNA interfaces, than that at rest of the protein structures. However, some proteins exhibit exceptions to this general trend.</p></div

The correlation between sequence conservation at D2O and O2O sites of individual r-proteins has been presented in this table.

<p>The correlation between sequence conservation at D2O and O2O sites of individual r-proteins has been presented in this table.</p

The correlation between sequence conservation at disordered and ordered sites of individual r-proteins are shown in this table.

<p>The correlation between sequence conservation at disordered and ordered sites of individual r-proteins are shown in this table.</p

The ASA(rel) values (expressing the structural flexibility of proteins in their uncomplexed state) and predicted conformational changes (RMSD) of small subunit ribosomal proteins due to association with 16S rRNA are enlisted here.

<p>The ASA(rel) values (expressing the structural flexibility of proteins in their uncomplexed state) and predicted conformational changes (RMSD) of small subunit ribosomal proteins due to association with 16S rRNA are enlisted here.</p

A graphical representation of the gene expression of a maximal homogeneous bicluster (i.e., a <i>MFCHOI</i>) over different samples.

<p>A graphical representation of the gene expression of a maximal homogeneous bicluster (i.e., a <i>MFCHOI</i>) over different samples.</p

Two examples of how significant biomarkers are identified from the maximal homogeneous biclusters (i.e., <i>MFCHOI</i>) for each class-label for each dataset.

<p>Here, we are shown intersection of only four maximal homogeneous biclusters for (a) the class-label <i>AC</i> and (b) the class-label <i>SCC</i>, individually (for Dataset 1). For the class <i>AC</i>, CENPA-, TTK-, KIF11-, KIF18B- and ZNF367- are the top frequent genes as they exist in the four biclusters (see (a)); similarly, for the class <i>SCC</i>, SHROOM3- is top frequent gene as it exists in the four biclusters (see (b)).</p

Comparative performance analysis of the rule-based classifiers on Dataset 4, respectively (at 4-fold CVs repeating for 10 times); where bold font denotes the highest value for each column.

<p>Comparative performance analysis of the rule-based classifiers on Dataset 4, respectively (at 4-fold CVs repeating for 10 times); where bold font denotes the highest value for each column.</p

Comparison of number of significant itemsets between <i>StatBicRM</i> and other existing ARM methods at different minimum support for the two artificial datasets (viz., <i>ArDS</i>5 and <i>ArDS</i>6).

<p>“Significant itemset” refers to <i>MFCHOI</i> for <i>StatBicRM</i>, and <i>FI</i> for the other methods.</p

p-value of Anova 1 between the avg. accuracies of the proposed and other classifiers (pairwise) in DS1, DS2, DS3 and DS4 (where ‘S’ and ‘NS’ refer to significant (p-value ≤ 0.05) and non-significant (p-value > 0.05) p-values respectively).

<p>p-value of Anova 1 between the avg. accuracies of the proposed and other classifiers (pairwise) in DS1, DS2, DS3 and DS4 (where ‘S’ and ‘NS’ refer to significant (p-value ≤ 0.05) and non-significant (p-value > 0.05) p-values respectively).</p

Additional file 3 of Detecting TF-miRNA-gene network based modules for 5hmC and 5mC brain samples: a intra- and inter-species case-study between human and rhesus

Color modules for Human and corresponding TF regulator and MicroRNA targeter where 5hmC is controlledand 5mC is diseased. A table for Generated color modules for human samples where 5hmC is controlled and 5mC is diseased and corresponding predicted TF regulators and miRNAs are also mentioned. (XLSX 13 kb