Reinstatement of synaptic plasticity in the aging brain through specific dopamine transporter inhibition.

Aging-related neurological deficits negatively impact mental health, productivity, and social interactions leading to a pronounced socioeconomic burden. Since declining brain dopamine signaling during aging is associated with the onset of neurological impairments, we produced a selective dopamine transporter (DAT) inhibitor to restore endogenous dopamine levels and improve cognitive function. We describe the synthesis and pharmacological profile of (S,S)-CE-158, a highly specific DAT inhibitor, which increases dopamine levels in brain regions associated with cognition. We find both a potentiation of neurotransmission and coincident restoration of dendritic spines in the dorsal hippocampus, indicative of reinstatement of dopamine-induced synaptic plasticity in aging rodents. Treatment with (S,S)-CE-158 significantly improved behavioral flexibility in scopolamine-compromised animals and increased the number of spontaneously active prefrontal cortical neurons, both in young and aging rodents. In addition, (S,S)-CE-158 restored learning and memory recall in aging rats comparable to their young performance in a hippocampus-dependent hole board test. In sum, we present a well-tolerated, highly selective DAT inhibitor that normalizes the age-related decline in cognitive function at a synaptic level through increased dopamine signaling.

In Silico Identification of Potential Druggable Binding Sites on CIN85 SH3 Domain.

Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.

A compact review of molecular property prediction with graph neural networks.

As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations.

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

In vitro and in silico studies of holothurin A on androgen receptor in prostate cancer.

The androgen receptor (AR) plays a crucial role in the growth of prostate cancer, and has long been considered the cancer's primary strategic therapeutic target. However, despite the early susceptibility, patients receiving hormonal therapy targeting AR are likely to develops resistance to the treatment and progresses to the castration-resistant stage as a consequence of the mutation at the ligand binding pocket of AR. Interestingly, the surface pocket of the AR called binding function 3 (BF3) has been reported as a great benefit for treating a recurrent tumor. Herein, we investigate the potential of using a marine triterpenoid saponin, holothurin A, on targeting AR expression of prostate cancer using in vitro and in silico studies. Holothurin A reduced the PSA expression, leading to the growth inhibition of androgen sensitive prostate cancer cell line through a downregulation of AR activity. The molecular docking study demonstrated that holothurin A could bind strongly in the BF3 pocket by energetically favorable hydrogen acceptor and hydrophobic with a calculated binding affinity of -13.90 kcal/mol. Molecular dynamics simulations provided the additional evidence that holothurin A can form a stable complex with the BF3 pocket through the hydrophobic interactions with VAL676, ILE680, and ALA721. As a consequence, holothurin A modulates the activation function-2 (AF2) site of the AR through repositioning of the residues in the AF2 pocket. Targeting alternatives sites on the surface of AR via holothurin A will provide a potential candidate for future prostate cancer treatment.Communicated by Ramaswamy H. Sarma.

Tricyclic antipsychotics and antidepressants can inhibit α5-containing GABAA receptors by two distinct mechanisms.

BACKGROUND AND PURPOSE: Many psychotherapeutic drugs, including clozapine, display polypharmacology and act on GABAA receptors. Patients with schizophrenia show alterations in function, structure and molecular composition of the hippocampus, and a recent study demonstrated aberrant levels of hippocampal α5 subunit-containing GABAA receptors. The purpose of this study is to investigate the effects of tricyclic compounds on α5 subunit-containing receptor subtypes. EXPERIMENTAL APPROACH: Functional studies of effects by seven antipsychotic and antidepressant medications were performed in several GABAA receptor subtypes by two-electrode voltage-clamp electrophysiology using Xenopus laevis oocytes. Computational structural analysis was employed to design mutated constructs of the α5 subunit, probing a novel binding site. Radioligand displacement data complemented the functional and mutational findings. KEY RESULTS: The antipsychotic drugs clozapine and chlorpromazine exerted functional inhibition on multiple GABAA receptor subtypes, including those containing α5-subunits. Based on a chlorpromazine binding site observed in a GABA-gated bacterial homologue, we identified a novel site in α5 GABAA receptor subunits and demonstrate differential usage of this and the orthosteric sites by these ligands. CONCLUSION AND IMPLICATIONS: Despite high molecular and functional similarities among the tested ligands, they reduce GABA currents by differential usage of allosteric and orthosteric sites. The chlorpromazine site we describe here is a new potential target for optimizing antipsychotic medications with beneficial polypharmacology. Further studies in defined subtypes are needed to substantiate mechanistic links between the therapeutic effects of clozapine and its action on certain GABAA receptor subtypes.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design.

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS-CoV-2.

The current pandemic threat of COVID-19, caused by the novel coronavirus SARS-CoV-2, not only gives rise to a high number of deaths around the world but also has immense consequences for the worldwide health systems and global economy. Given the fact that this pandemic is still ongoing and there are currently no drugs or vaccines against this novel coronavirus available, this in silico study was conducted to identify a potential novel SARS-CoV-2-inhibitor. Two different approaches were pursued: 1) The Docking Consensus Approach (DCA) is a novel approach, which combines molecular dynamics simulations with molecular docking. 2) The Common Hits Approach (CHA) in contrast focuses on the combination of the feature information of pharmacophore modeling and the flexibility of molecular dynamics simulations. The application of both methods resulted in the identification of 10 compounds with high coronavirus inhibition potential.

A Combination of Pharmacophore and Docking-based Virtual Screening to Discover new Tyrosinase Inhibitors.

Melanogenesis controls the formation of melanin pigment whose overproduction is related to various hyperpigmentary disorders in humans. Tyrosinase is a type-3 copper enzyme involved in the rate limiting step of melanin synthesis, therefore its inhibition could represent an efficient way for the development of depigmenting agents. In this work, a combination of pharmacophore and docking-based studies has been employed to screen two in-house 3D compound databases containing about 2,000 molecules from natural and synthetic sources. As result we selected two "hit compounds" which proved to inhibit tyrosinase activity showing IC50 values in the micromolar range.

The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design.

Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.

Computational Identification of Novel Kir6 Channel Inhibitors.

KATP channels consist of four Kir6.x pore-forming subunits and four regulatory sulfonylurea receptor (SUR) subunits. These channels couple the metabolic state of the cell to membrane excitability and play a key role in physiological processes such as insulin secretion in the pancreas, protection of cardiac muscle during ischemia and hypoxic vasodilation of arterial smooth muscle cells. Abnormal channel function resulting from inherited gain or loss-of-function mutations in either the Kir6.x and/or SUR subunits are associated with severe diseases such as neonatal diabetes, congenital hyperinsulinism, or Cantú syndrome (CS). CS is an ultra-rare genetic autosomal dominant disorder, caused by dominant gain-of-function mutations in SUR2A or Kir6.1 subunits. No specific pharmacotherapeutic treatment options are currently available for CS. Kir6 specific inhibitors could be beneficial for the development of novel drug therapies for CS, particular for mutations, which lack high affinity for sulfonylurea inhibitor glibenclamide. By applying a combination of computational methods including atomistic MD simulations, free energy calculations and pharmacophore modeling, we identified several novel Kir6.1 inhibitors, which might be possible candidates for drug repurposing. The in silico predictions were confirmed using inside/out patch-clamp analysis. Importantly, Cantú mutation C166S in Kir6.2 (equivalent to C176S in Kir6.1) and S1020P in SUR2A, retained high affinity toward the novel inhibitors. Summarizing, the inhibitors identified in this study might provide a starting point toward developing novel therapies for Cantú disease.

GRAIL: GRids of phArmacophore Interaction fieLds.

In the absence of experimentally derived, three-dimensional structures of receptors in complex with active ligands, it is of high value to be able to gain knowledge about energetically favorable interaction sites solely from the structure of the receptor binding site. For de novo ligand design as well as for lead optimization, this information retrieved from the protein is inevitable. The herein presented method called GRAIL combines the advantages of traditional grid-based approaches for the identification of interaction sites and the power of the pharmacophore concept. A reduced pharmacophoric abstraction of the target system enables the computation of all relevant interaction grid maps in short amounts of time. This allows one to extend the utility of a grid-based method for the analysis of large amounts of coordinate sets obtained by long-time MD simulations. In this way it is possible to assess conformation dependent characteristics of key interactions over time. Furthermore, conformational changes of the protein can be taken into account easily and information thus obtained well-guides a rational ligand design process. A study employing MD trajectories of the oncology target heat shock protein 90 showcases how well our novel approach GRAIL performs for a set of different inhibitors bound to their target protein and how molecular features of the inhibitors are subject to optimization.