Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling

Proteochemometrics modeling for prediction of the interactions between caspase isoforms and their inhibitors

Caspases (cysteine-aspartic proteases) play critical roles in inflammation and the programming of cell death in the form of necroptosis, apoptosis, and pyroptosis. The name of these enzymes has been chosen in accordance with their cysteine protease activity. They act as cysteines in nucleophilically active sites to attack and cleave target proteins in the aspartic acid and amino acid C-terminal. Based on the substrate's structure and the specificity, the physiological activity of caspases is divided. However, in apoptosis, the division of caspases into initiating caspases (caspase 2, 8, 9, and 10) and executive caspases (caspase 3, 6, and 7) is essential. The present study aimed to perform Proteochemometrics Modeling to generalize the data on caspases, which could predict ligand and protein interactions. In this study, we employed protein and ligand descriptors. Moreover, protein descriptors were computed using the Protr R package, while PADEL-Descriptor was employed for the computation of ligand descriptors. In addition, NCA (Neighborhood Component Analyses) was used for descriptor selection, and SVR, decision tree, and ensemble methods were utilized for the proteochemometrics modeling. This study shows that the ensemble model demonstrates superior performance compared with other models in terms of R², Q², and RMSE criteria.

In silico investigations of some Cyperus rotundus compounds as potential anti-inflammatory inhibitors of 5-LO and LTA4H enzymes

The present study aimed to experimentally identify the essential oil of Algerian Cyperus rotundus L. and to model the interaction of some known anti-inflammatory molecules with two key enzymes involved in inflammation, 5-Lypoxygenase (5-LO) and leukotriene A4 hydrolase (LTA4H). Gas chromatography/gas chromatography-mass spectrometry (GC/GC-MS) revealed that 92.7% of the essential oil contains 35 compounds, including oxygenated sesquiterpenes (44.2%), oxygenated monoterpenes (30.2%), monoterpene hydrocarbons (11.8%) and sesquiterpene hydrocarbons (6.5%). The major identified oxygenated terpenes are humulene oxide II, caryophyllene oxide, khusinol, agarospirol, spathulinol and trans-pinocarveol Myrtenol and α-terpineol are known to exhibit anti-inflammatory activities. Several complexes obtained after docking the natural terpenes with 5-LO and LTA4H have shown strong hydrogen bonding interactions. The best docking energies were found with α-terpineol, Myrtenol and khusinol. The interaction between the natural products and amino-acid residues HIS367, ILE673 and GLN363 appears to be critical for 5-LO inhibition, while the interaction with residues GLU271, HIS295, TYR383, TYR378, GLU318, GLU296 and ASP375 is critical for LTA4H inhibition. Molecular dynamics (MD) trajectories of the selected docked complexes showed stable backbone root mean square deviation (RMSD), supporting the stability of the natural product-enzyme interaction.Communicated by Ramaswamy H. Sarma.

International Union of Basic and Clinical Pharmacology. CXI. Pharmacology, Signaling, and Physiology of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors respond to glutamate, the major excitatory neurotransmitter in the mammalian brain, mediating a modulatory role that is critical for higher-order brain functions such as learning and memory. Since the first mGlu receptor was cloned in 1992, eight subtypes have been identified along with many isoforms and splice variants. The mGlu receptors are transmembrane-spanning proteins belonging to the class C G protein-coupled receptor family and represent attractive targets for a multitude of central nervous system disorders. Concerted drug discovery efforts over the past three decades have yielded a wealth of pharmacological tools including subtype-selective agents that competitively block or mimic the actions of glutamate or act allosterically via distinct sites to enhance or inhibit receptor activity. Herein, we review the physiologic and pathophysiological roles for individual mGlu receptor subtypes including the pleiotropic nature of intracellular signal transduction arising from each. We provide a comprehensive analysis of the in vitro and in vivo pharmacological properties of prototypical and commercially available orthosteric agonists and antagonists as well as allosteric modulators, including ligands that have entered clinical trials. Finally, we highlight emerging areas of research that hold promise to facilitate rational design of highly selective mGlu receptor-targeting therapeutics in the future. SIGNIFICANCE STATEMENT: The metabotropic glutamate receptors are attractive therapeutic targets for a range of psychiatric and neurological disorders. Over the past three decades, intense discovery efforts have yielded diverse pharmacological tools acting either competitively or allosterically, which have enabled dissection of fundamental biological process modulated by metabotropic glutamate receptors and established proof of concept for many therapeutic indications. We review metabotropic glutamate receptor molecular pharmacology and highlight emerging areas that are offering new avenues to selectively modulate neurotransmission.

An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models

Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well-calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.

Proteochemometrics - recent developments in bioactivity and selectivity modeling

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.

Insights into the Structural Aspects of the mGlu Receptor Orthosteric Binding Site

The amino terminal domain (ATD) of the metabotropic glutamate (mGlu) receptors contains the orthosteric glutamate recognition site, which is highly conserved across the eight mGlu receptor subtypes. In total, 29 X-ray crystal structures of the mGlu ATD proteins have been reported to date. These structures span across 3 subgroups and 6 subtypes, and include apo, agonist- and antagonist-bound structures. We will discuss the insights gained from the analysis of these structures with the focus on the interactions contributing to the observed group and subtype selectivity for select agonists. Furthermore, we will define the full expanded orthosteric ligand binding pocket (LBP) of the mGlu receptors, and discuss the macroscopic features of the mGlu ATD proteins.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Computationally Guided Identification of Allosteric Agonists of the Metabotropic Glutamate 7 Receptor

The metabotropic glutamate 7 (mGlu₇) receptor belongs to the group III of mGlu receptors. Since the mGlu₇ receptor can control excitatory neurotransmission in the hippocampus and cortex, modulation of the receptor may have therapeutic benefit in several CNS diseases. However, mGlu₇ remains relatively unexplored among the eight known mGlu receptors partly because of the limited availability of tool compounds to interrogate its potential therapeutic utility. Here we report the discovery of a new class of mGlu₇ allosteric agonists. Hits originating from virtual screening were followed up with further analogue searching and screening, leading to a novel series of mGlu₇ allosteric agonists. Guided by docking into a structural model of the mGlu₇ receptor the initial hit 5 was successfully optimized to analogues with comparable potencies and more attractive drug-like attributes than AMN082.

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling

Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins.

Progress toward allosteric ligands of metabotropic glutamate 7 (mGlu7) receptor: 2008-present

Metabotropic glutamate type 7 (mGlu7) receptor is a member of the group III family of mGlu receptors. It is widely distributed in the central nervous system (CNS) and is preferentially expressed on presynaptic nerve terminals where it is thought to play a critical role in modulating normal neuronal function and synaptic transmission, making it particularly relevant in neuropharmacology. The lack of small-molecule mGlu7 ligands with adequate potency, selectivity and drug-like properties has resulted in difficulties in the preclinical validation of mGlu7 modulation in disease models. In the last decade, allosteric modulators of mGlu7 receptors have emerged as valuable tools with good potency, selectivity and physicochemical properties to study and unleash the therapeutic potential of mGlu7 receptors. This review focusses on the medicinal chemistry of mGlu7 receptor allosteric ligands discovered since 2008.

Structural Basis for ( S)-3,4-Dicarboxyphenylglycine (DCPG) As a Potent and Subtype Selective Agonist of the mGlu8 Receptor

( S)-3,4-Dicarboxyphenylglycine (DCPG) was first reported in 2001 as a potent orthosteric agonist with high subtype selectivity for the mGlu₈ receptor, but the structural basis for its high selectivity is not well understood. We have solved a cocrystal structure of recombinant human mGlu₈ amino terminal domain (ATD) protein bound to ( S)-DCPG, which possesses the largest lobe opening angle observed to date among known agonist-bound mGlu ATD crystal structures. The binding conformation of ( S)-DCPG observed in the crystal structure is significantly different from that in the homology model built from an l-glutamate-bound rat mGlu₁ ATD crystal structure, which has a smaller lobe opening angle. This highlights the importance of considering various lobe opening angles when modeling mGlu ATD-ligand complex. New homology models of other mGlu receptors based on the ( S)-DCPG-bound mGlu₈ ATD crystal structure were explored to rationalize ( S)-DCPG's high mGlu₈ receptor subtype selectivity.