Complexity of homolog-based identification of pertinent pockets in proteins.

<p>A) A hypothetical target protein is depicted with three homologous proteins. The target protein consists of two domains, one shown in blue and the other in red. These domains may be represented by complete or partial sequences in homologs. For example, homologs 1 and 2 possess short domains homologous to the target protein's blue domain. On the other hand, homolog 3 possesses a sequence match for the red domain. Each target protein domain possesses pockets (denoted by black, green, red and blue ovoids), which may or may not be identified by the presence of ligands in homologs. Here, the black pocket is also represented in homolog 3, but the green pocket is not. The red pocket is observed in both, homolog 1 and 2, but the blue pocket is only represented in homolog 1. B) Workflow to identify druggable pockets in homologs proteins.</p

Overview of triaging the genes stored in the AEROPATH database to enable structure-based druggability predictions for <i>P</i>. <i>aeruginosa</i> proteins.

<p>Overview of triaging the genes stored in the AEROPATH database to enable structure-based druggability predictions for <i>P</i>. <i>aeruginosa</i> proteins.</p

Desolvation energy is critical for quantitative analysis of GAG–protein interaction.

<p>Neither ΔG_ES (<b>a</b> and <b>b</b>) nor ΔG_DS (<b>c</b> and <b>d</b>) alone explain the change in ΔG_OBS for antithrombin (<b>a</b> and <b>c</b>) and thrombin (<b>b</b> and <b>d</b>) mutants studied to date. Any enthalpic gain due to electrostatics is opposed by desolvation (R² = 0.99) in antithrombin <b>(e)</b> as well as in thrombin <b>(f)</b>, suggesting that desolvation is critical for quantitative analysis of GAG-protein interactions. In all cases, the correlation was found to be significant at α = 0.05.</p

Polar residues present in various GBSs.

<p>These residues form direct interactions with GAGs, as evidenced by analysis of crystal structures.</p

List of <i>P</i>. <i>aeruginosa</i> proteins predicted to possess a druggable pocket.

<p>^<b>α</b><b><i>HA</i>:</b> distinct homologous pockets assessed</p><p>^Ω<b><i>Rank</i>:</b> Chemogenomics-based druggability rank. Ranks obtained when only perturbative proteins are considered are given in brackets.</p><p>List of <i>P</i>. <i>aeruginosa</i> proteins predicted to possess a druggable pocket.</p

Accuracy, recall and precision values for training and validation sets for DrugPred 1 and 2.0.

<p>^<b>Ω</b> Values taken from Krasowski et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137279#pone.0137279.ref021" target="_blank">21</a>]</p><p>Accuracy, recall and precision values for training and validation sets for DrugPred 1 and 2.0.</p

The basis for homolog-based druggability predictions.

<p>(A) Homologous pockets whose classification correctly reflected the druggability of the parent pocket. The data was binned according to percent correct predictions among the scored pockets for each parent homolog. The number of NRDLD proteins that fitted into each category was then plotted (frequency and percentages are both shown). (B) Correct predictions in relationship to sequence identity. The percent identity between NRDLD dataset structures and homologous chains was noted. The homologs were then binned according to their percent sequence identity. The percent of homologs whose predictions matched that of the NRDLD dataset pocket was plotted for each bin. (C) Correct predictions in relationship to sequence identity of binding site residues only. Plotted as described for (B), but instead of the sequence identity only the identity of the binding site residues was used. (D) Percent correct predictions in relationship to number of assessed homologs. (E) Percent consensus in relationship to percentage of correctly predicted NRDLD dataset pockets. The NRDLD dataset pockets were binned into two categories, where either <80% or ≥80% consensus (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137279#sec010" target="_blank">methods</a>) in druggability predictions for their respective homologs was observed. The percentage of NRDLD pockets whose druggability was correctly reflected by consensus amongst their homologs was then plotted for each of these bins.</p

<i>G</i>_<i>ES</i> at arginines and/or lysines does not identify the GBS on a protein.

<p><i>G</i>_<i>ES</i> for Arg/Lys residues are mapped using 2DSE plots for multiple GAG-binding proteins including <b>(a)</b> antithrombin, <b>(b)</b> thrombin, <b>(c)</b> FGF2, <b>(d)</b> HS3ST3A1, <b>(e)</b> HS3ST1 and <b>(f)</b> HS2ST1. The maps reveal that GAG-binding site Arg/Lys residues may not always possess high <i>G</i>_<i>ES</i> and not all Arg/Lys with high <i>G</i>_<i>ES</i> on a protein are part of the GAG-binding site.</p

2DSE plots for <i>G</i>_<i>ES</i> at neutral hydrogen bond donors.

<p>GAGs bind neutral hydrogen bond donors on the protein that possess significantly high G_ES. <b>(a)</b> Asn45 of antithrombin GAG-binding site possesses the highest G_ES within the structure. <b>(b)</b> In contrast, the nonspecific thrombin GAG-binding site demonstrates a diffused G_ES. Similarly, significantly high <i>G</i>_<i>ES</i> are observed at <b>(c)</b> Asn27 of the FGF2 GBS; Asn27Ala mutation affects GAG-binding (ΔΔG~1.1 kcal/mol) almost as much as K125A (ΔΔG~1.7 kcal/mol), which had the largest effect, <b>(d)</b> Asn255 of the HS3ST3A1 GAG-binding site; the N255A mutant is inactive, and <b>(e)</b> Gln163 of HS3ST1; Gln163Ala mutant loses ~65% activity. <b>(f)</b> Diffused G_ES of HS2ST1 may represent its ability to bind low-sulfated GAGs. However, Asn91 and 112 of the HS2ST1 GAG-binding site possess a potential higher than His106, mutation of which is already known to affect GAG-binding.</p

Electrostatic interactions and desolvation energies for AT-heparin pentasaccharide complexes reported in the literature.

<p>Electrostatic interactions and desolvation energies for AT-heparin pentasaccharide complexes reported in the literature.</p