Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery

The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez et al., 2014). The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method out-performs classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches

<em>De novo</em> design of protein kinase inhibitors by<em> in silico</em> identification of hinge region-binding fragments

[Image: see text] Protein kinases constitute an attractive family of enzyme targets with high relevance to cell and disease biology. Small molecule inhibitors are powerful tools to dissect and elucidate the function of kinases in chemical biology research and to serve as potential starting points for drug discovery. However, the discovery and development of novel inhibitors remains challenging. Here, we describe a structure-based de novo design approach that generates novel, hinge-binding fragments that are synthetically feasible and can be elaborated to small molecule libraries. Starting from commercially available compounds, core fragments were extracted, filtered for pharmacophoric properties compatible with hinge-region binding, and docked into a panel of protein kinases. Fragments with a high consensus score were subsequently short-listed for synthesis. Application of this strategy led to a number of core fragments with no previously reported activity against kinases. Small libraries around the core fragments were synthesized, and representative compounds were tested against a large panel of protein kinases and subjected to co-crystallization experiments. Each of the tested compounds was active against at least one kinase, but not all kinases in the panel were inhibited. A number of compounds showed high ligand efficiencies for therapeutically relevant kinases; among them were MAPKAP-K3, SRPK1, SGK1, TAK1, and GCK for which only few inhibitors are reported in the literature

Hit expansion for ligand 8.

<p>For chemical structures see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035792#pone-0035792-g016" target="_blank">Figure 16</a>.</p>*<p>average values of three independent measurements, standard deviation in brackets.</p>a<p>no inhibition at solubility limit measured.</p

Hit expansion for ligand 4.

<p>For chemical structures see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035792#pone-0035792-g013" target="_blank">Figure 13</a>.</p>*<p>average values of three independent measurements, standard deviation in brackets.</p>a<p>no inhibition at solubility limit measured.</p

Docking ranks, physico-chemical properties, inhibition values, and ligand efficiencies for virtual screening hits.

<p>For chemical structures see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035792#pone-0035792-g007" target="_blank">Figure 7</a>.</p>1<p>using the total score of docking for ranking (after application of the pharmacophore filter).</p>2<p>using the score divided by the number of heavy atoms of the molecule for ranking (after application of the pharmacophore filter).</p>3<p>setup 1: His25 protonated at ND, no ADP present; setup 2: His25 protonated at ND, ADP present; setup 3: His25 protonated at NE, no ADP present; setup 4: His25 protonated at NE, ADP present.</p>4<p>average values of three independent measurements, standard deviation in brackets.</p

Investigation of specificity determinants in bacterial tRNA-guanine transglycosylase reveals queuine, the substrate of its eucaryotic counterpart, as inhibitor.

Bacterial tRNA-guanine transglycosylase (Tgt) catalyses the exchange of the genetically encoded guanine at the wobble position of tRNAs(His,Tyr,Asp,Asn) by the premodified base preQ1, which is further converted to queuine at the tRNA level. As eucaryotes are not able to synthesise queuine de novo but acquire it through their diet, eucaryotic Tgt directly inserts the hypermodified base into the wobble position of the tRNAs mentioned above. Bacterial Tgt is required for the efficient pathogenicity of Shigella sp, the causative agent of bacillary dysentery and, hence, it constitutes a putative target for the rational design of anti-Shigellosis compounds. Since mammalian Tgt is known to be indirectly essential to the conversion of phenylalanine to tyrosine, it is necessary to create substances which only inhibit bacterial but not eucaryotic Tgt. Therefore, it seems of utmost importance to study selectivity-determining features within both types of proteins. Homology models of Caenorhabditis elegans Tgt and human Tgt suggest that the replacement of Cys158 and Val233 in bacterial Tgt (Zymomonas mobilis Tgt numbering) by valine and accordingly glycine in eucaryotic Tgt largely accounts for the different substrate specificities. In the present study we have created mutated variants of Z. mobilis Tgt in order to investigate the impact of a Cys158Val and a Val233Gly exchange on catalytic activity and substrate specificity. Using enzyme kinetics and X-ray crystallography, we gained evidence that the Cys158Val mutation reduces the affinity to preQ1 while leaving the affinity to guanine unaffected. The Val233Gly exchange leads to an enlarged substrate binding pocket, that is necessary to accommodate queuine in a conformation compatible with the intermediately covalently bound tRNA molecule. Contrary to our expectations, we found that a priori queuine is recognised by the binding pocket of bacterial Tgt without, however, being used as a substrate

Hit expansion for ligand 3.

<p>For chemical structures see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035792#pone-0035792-g011" target="_blank">Figure 11</a>.</p>*<p>average values of three independent measurements, standard deviation in brackets.</p

Chemical structures for analogues of ligand 4.

<p>Chemical structures for analogues of ligand 4.</p

Substrate binding site of <i>Aa</i>IspE (PDB code 2v2z).

<p>The catalytic residues Lys9 and Asp130 are labelled together with other residues important for ligand binding. The cytidine moiety of the substrate forms hydrogen bonds with Lys145 and His25, π-stacking interactions with Tyr175 and edge-face interactions with Tyr24.</p

Physico-chemical properties, inhibition values, and ligand efficiencies for <i>in vitro</i> screening hits.

<p>For chemical structures see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035792#pone-0035792-g009" target="_blank">Figure 9</a>.</p>*<p>average values of three independent measurements, standard deviation in brackets.</p