Scatter plot of the experimental activities versus predicted activities for model CoMFA-SDA: (●) training set predictions (○) LOO cross-validated predictions (red ▲) test set predictions.

<p>Scatter plot of the experimental activities versus predicted activities for model CoMFA-SDA: (●) training set predictions (○) LOO cross-validated predictions (red ▲) test set predictions.</p

Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR.

Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure-activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure-activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors

Predicted MM-GBSA free energies (kcal/mol) and individual energy terms of the thrombin-inhibitor complexes for selected compounds.

<p>a Experimental ΔΔG was calculated using ΔG of compound <b>133</b> as reference.</p><p>Predicted MM-GBSA free energies (kcal/mol) and individual energy terms of the thrombin-inhibitor complexes for selected compounds.</p

Experimental and predicted thrombin inhibitory activities (log(10³/Ki) (nM)) using CoMSIA-SDA model.

<p>^a QSAR outliers.</p><p>^b test set compounds.</p><p>^c compounds selected for MM-GBSA calculations</p><p>Experimental and predicted thrombin inhibitory activities (log(10³/Ki) (nM)) using CoMSIA-SDA model.</p

Stepwise development of CoMSIA models by using SAMPLS and different field combinations.

<p>NC is the number of components from PLS analysis; R² is the square of the correlation coefficient; S is the standard deviation of the regression; F is the Fischer ratio; Q² and S_cv are the correlation coefficient and standard deviation of the leave-one-out (LOO) cross-validation, respectively. The best model is indicated in boldface.</p><p>Stepwise development of CoMSIA models by using SAMPLS and different field combinations.</p

CoMSIA contour maps for TIs deriving from model CoMSIA-SDA.

<p>The amino acid residues located close to the binding pocket of thrombin are represented for comparing their position with the position of isopleths derived from the model. Compound <b>134</b> is shown inside the field. <b>(A)</b> Steric field: green isopleths indicate regions where bulky groups enhance the activity and yellow isopleths indicate regions where bulky groups disfavor the activity. <b>(B)</b>. HB donor field: cyan isopleths indicate regions where HB donors favor the activity, and purple isopleths indicate regions where HB donors disfavor the activity. <b>(C)</b> HB acceptor fields: magenta isopleths indicate regions where HB acceptors enhance the activity, and red isopleths indicate regions where HB acceptors decrease the activity.</p

Alignment of inhibitor docked structures on inhibitor X-ray reference structures, for the TI complexes.

<p><b>(A)</b> Compound <b>13</b> (PDB: 1T4U); <b>(B)</b> compound <b>24</b> (PDB: 1T4V); <b>(C)</b> compound <b>128</b> (PDB: 3C27), <b>(D)</b> compound <b>151</b> (PDB: 2R2M); <b>(E)</b> compound <b>165</b> (PDB: 3LDX). Crystal structures are represented in yellow, and docking results are represented in purple. Docking accuracy is reported by means of RMSD values.</p

Evaluation of new antihypertensive drugs designed in silico using Thermolysin as a target

The search for new therapies for the treatment of Arterial hypertension is a major concern in the scientific community. Here, we employ a computational biochemistry protocol to evaluate the performance of six compounds (Lig783, Lig1022, Lig1392, Lig2177, Lig3444 and Lig6199) to act as antihypertensive agents. This protocol consists of Docking experiments, efficiency calculations of ligands, molecular dynamics simulations, free energy, pharmacological and toxicological properties predictions (ADME-Tox) of the six ligands against Thermolysin. Our results show that the docked structures had an adequate orientation in the pocket of the Thermolysin enzymes, reproducing the X-ray crystal structure of InhibitorThermolysin complexes in an acceptable way. The most promising candidates to act as antihypertensive agents among the series are Lig2177 and Lig3444. These compounds form the most stable ligandThermolysin complexes according to their binding free energy values obtained in the docking experiments as well as MM-GBSA decomposition analysis calculations. They present the lowest values of Ki, indicating that these ligands bind strongly to Thermolysin. Lig2177 was oriented in the pocket of Thermolysin in such a way that both OH of the dihydroxyl-amino groups to establish hydrogen bond interactions with Glu146 and Glu166. In the same way, Lig3444 interacts with Asp150, Glu143 and Tyr157. Additionally, Lig2177 and Lig3444 fulfill all the requirements established by Lipinski Veber and Pfizer 3/75 rules, indicating that these compounds could be safe compounds to be used as antihypertensive agents. We are confident that our computational biochemistry protocol can be used to evaluate and predict the behavior of a broad range of compounds designed in silicoagainst a protein target. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of King Saud University

Structures of TIs.

<p>Structures of TIs.</p

Theoretical Evaluation of Novel Thermolysin Inhibitors from Bacillus thermoproteolyticus. Possible Antibacterial Agents

The search for new antibacterial agents that could decrease bacterial resistance is a subject in continuous development. Gram-negative and Gram-positive bacteria possess a group of metalloproteins belonging to the MEROPS peptidase (M4) family, which is the main virulence factor of these bacteria. In this work, we used the previous results of a computational biochemistry protocol of a series of ligands designed in silico using thermolysin as a model for the search of antihypertensive agents. Here, thermolysin from Bacillus thermoproteolyticus, a metalloprotein of the M4 family, was used to determine the most promising candidate as an antibacterial agent. Our results from docking, molecular dynamics simulation, molecular mechanics Poisson-Boltzmann (MM-PBSA) method, ligand efficiency, and ADME-Tox properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) indicate that the designed ligands were adequately oriented in the thermolysin active site. The Lig783, Lig2177, and Lig3444 compounds showed the best dynamic behavior; however, from the ADME-Tox calculated properties, Lig783 was selected as the unique antibacterial agent candidate amongst the designed ligands