Study of the affinity between the protein kinase PKA and homoarginine-containing peptides derived from kemptide: Free energy perturbation (FEP) calculations

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

Exploitation of dihydroorotate dehydrogenase (DHODH) and p53 activation as therapeutic targets: A case study in polypharmacology

The tenovins are a frequently studied class of compounds capable of inhibiting sirtuin activity, which is thought to result in increased acetylation and protection of the tumor suppressor p53 from degradation. However, as we and other laboratories have shown previously, certain tenovins are also capable of inhibiting autophagic flux, demonstrating the ability of these compounds to engage with more than one target. In this study, we present two additional mechanisms by which tenovins are able to activate p53 and kill tumor cells in culture. These mechanisms are the inhibition of a key enzyme of the de novo pyrimidine synthesis pathway, dihydroorotate dehydrogenase (DHODH), and the blockage of uridine transport into cells. These findings hold a 3-fold significance: first, we demonstrate that tenovins, and perhaps other compounds that activate p53, may activate p53 by more than one mechanism; second, that work previously conducted with certain tenovins as SirT1 inhibitors should additionally be viewed through the lens of DHODH inhibition as this is a major contributor to the mechanism of action of the most widely used tenovins; and finally, that small changes in the structure of a small molecule can lead to a dramatic change in the target profile of the molecule even when the phenotypic readout remains static.

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 Inhibitor-Thermolysin 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 ligand-Thermolysin 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.

Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility

Large and flexible ligands gain increasing interest in the development of bioactive agents. They challenge the applicability of computational ligand optimization strategies originally developed for small molecules. Free energy perturbation (FEP) is often used for predicting binding affinities of small molecule ligands, however, its use for more complex ligands remains limited. Herein, we report the structure-based design of peptide macrocycles targeting the protein binding site of human adaptor protein 14-3-3. We observe a surprisingly strong dependency of binding affinities on relatively small variations in substituent size. FEP was performed to rationalize observed trends. To account for insufficient convergence of FEP, restrained calculations were performed and complemented with extensive REST MD simulations of the free ligands. These calculations revealed that changes in affinity originate both from altered direct interactions and conformational changes of the free ligand. In addition, MD simulations provided the basis to rationalize unexpected trends in ligand lipophilicity. We also verified the anticipated interaction site and binding mode for one of the high affinity ligands by X-ray crystallography. The introduced fully-atomistic simulation protocol can be used to rationalize the development of structurally complex ligands which will support future ligand maturation efforts.

Mycobacterium tuberculosis serine/threonine protein kinases: structural information for the design of their specific ATP-competitive inhibitors

In the last decades, human protein kinases (PKs) have been relevant as targets in the development of novel therapies against many diseases, but the study of Mycobacterium tuberculosis PKs (MTPKs) involved in tuberculosis pathogenesis began much later and has not yet reached an advanced stage of development. To increase knowledge of these enzymes, in this work we studied the structural features of MTPKs, with focus on their ATP-binding sites and their interactions with inhibitors. PknA, PknB, and PknG are the most studied MTPKs, which were previously crystallized; ATP-competitive inhibitors have been designed against them in the last decade. In the current work, reported PknA, PknB, and PknG inhibitors were extracted from literature and their orientations inside the ATP-binding site were proposed by using docking method. With this information, interaction fingerprints were elaborated, which reveal the more relevant residues for establishing chemical interactions with inhibitors. The non-crystallized MTPKs PknD, PknF, PknH, PknJ, PknK, and PknL were also studied; their three-dimensional structural models were developed by using homology modeling. The main characteristics of MTPK ATP-binding sites (the non-crystallized and crystallized MTPKs, including PknE and PknI) were accounted; schemes of the main polar and nonpolar groups inside their ATP-binding sites were constructed, which are suitable for a major understanding of these proteins as antituberculotic targets. These schemes could be used for establishing comparisons between MTPKs and human PKs in order to increase selectivity of MTPK inhibitors. As a key tool for guiding medicinal chemists interested in the design of novel MTPK inhibitors, our work provides a map of the structural elements relevant for the design of more selective ATP-competitive MTPK inhibitors.