In Silico Methods for the Discovery of Orthosteric GABAB Receptor Compounds

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors

Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.

GABAB Receptor Chemistry and Pharmacology: Agonists, Antagonists, and Allosteric Modulators

The GABA_B receptors are metabotropic G protein-coupled receptors (GPCRs) that mediate the actions of the primary inhibitory neurotransmitter, γ-aminobutyric acid (GABA). In the CNS, GABA plays an important role in behavior, learning and memory, cognition, and stress. GABA is also located throughout the gastrointestinal (GI) tract and is involved in the autonomic control of the intestine and esophageal reflex. Consequently, dysregulated GABA_B receptor signaling is associated with neurological, mental health, and gastrointestinal disorders; hence, these receptors have been identified as key therapeutic targets and are the focus of multiple drug discovery efforts for indications such as muscle spasticity disorders, schizophrenia, pain, addiction, and gastroesophageal reflex disease (GERD). Numerous agonists, antagonists, and allosteric modulators of the GABA_B receptor have been described; however, Lioresal^® (Baclofen; β-(4-chlorophenyl)-γ-aminobutyric acid) is the only FDA-approved drug that selectively targets GABA_B receptors in clinical use; undesirable side effects, such as sedation, muscle weakness, fatigue, cognitive deficits, seizures, tolerance and potential for abuse, limit their therapeutic use. Here, we review GABA_B receptor chemistry and pharmacology, presenting orthosteric agonists, antagonists, and positive and negative allosteric modulators, and highlight the therapeutic potential of targeting GABA_B receptor modulation for the treatment of various CNS and peripheral disorders.

Computational Screening of Potential Inhibitors of Desulfobacter postgatei for Pyrite Scale Prevention in Oil and Gas Wells

Sulfate-reducing bacteria (SRB), such as Desulfobacter postgatei are found in oil wells. However, they lead to the release of hydrogen sulfide. This in turn leads to the iron sulfide scale formation (pyrite). ATP sulfurylase is an enzyme present in SRB, which catalyzes the formation of adenylyl sulfate (APS) and inorganic pyrophosphatase (PPi) from ATP and sulfate. This reaction is the first among many in hydrogen sulfide production by D. postgatei . Consensus scoring using molecular docking and machine learning was used to identify three potential inhibitors of ATP sulfurylase from a database of about 40 million compounds. These selected hits ((S,E)-1-(4-methoxyphenyl)-3-(9-((m-tolylimino)methyl)-9,10-dihydroanthracen-9-yl)pyrrolidine-2,5-dione; methyl 2-[[(1S)-5-cyano-2-imino-1-(4-phenylthiazol-2-yl)-3-azaspiro[5.5]undec-4-en-4-yl]sulfanyl]acetate; and (4S)-4-(3-chloro-4-hydroxy-phenyl)-1-(6-hydroxypyridazin-3-yl)-3-methyl-4,5-dihydropyrazolo[3,4-b]pyridin-6-ol), known as A, B, and C, respectively) all had good binding affinities with ATP sulfurylase and were further analyzed for their toxicological properties. Compound A had the highest docking score. However, based on the physicochemical and toxicological properties, only compound C was predicted to be both safe and effective as a potential inhibitor of ATP sulfurylase, hence the preferred choice. The molecular interactions of compound C revealed favorable interactions with the following residues: LEU213, ASP308, ARG307, TRP347, LEU224, GLN212, MET211, and HIS309.

The GABAB Receptor-Structure, Ligand Binding and Drug Development

The γ-aminobutyric acid (GABA) type B receptor (GABA_B-R) belongs to class C of the G-protein coupled receptors (GPCRs). Together with the GABA_A receptor, the receptor mediates the neurotransmission of GABA, the main inhibitory neurotransmitter in the central nervous system (CNS). In recent decades, the receptor has been extensively studied with the intention being to understand pathophysiological roles, structural mechanisms and develop drugs. The dysfunction of the receptor is linked to a broad variety of disorders, including anxiety, depression, alcohol addiction, memory and cancer. Despite extensive efforts, few compounds are known to target the receptor, and only the agonist baclofen is approved for clinical use. The receptor is a mandatory heterodimer of the GABA_B1 and GABA_B2 subunits, and each subunit is composed of an extracellular Venus Flytrap domain (VFT) and a transmembrane domain of seven α-helices (7TM domain). In this review, we briefly present the existing knowledge about the receptor structure, activation and compounds targeting the receptor, emphasizing the role of the receptor in previous and future drug design and discovery efforts.

In silico structure-based design of GABAB receptor agonists using a combination of docking and QSAR

The study of γ-aminobutyric acid B receptor (GABA_B ) activation is of great interest for several brain disorders. The search of new GABA_B receptor agonists has been carried out by many research groups. As a result, Baclofen has become the prototypical GABA_B receptor agonist. However, several attempts have been made to modify its structure to generate derivatives with improved activity. In this work, we carried out a theoretical and computational study for a wide range of GABA_B receptor agonists reported in the literature. Molecular docking and QSAR techniques were combined by using the interaction energies of the agonists with the key residues of GABA_B receptor, as molecular descriptors for the QSAR construction. The resulting mathematical model suggests that the activity of GABA_B receptor agonists is influenced by three factors: their shape and molecular size (PW5 and PJI2), their constitutional features (E_LUMO and T(N…O)) and the energy interaction with GABA_B receptor (E_TRP278 ). This model was validated by the QUIK, REDUNDANCY and OVERFITTING rules, and its predicted ability was tasted by the Q_LOO , Q_ASYM , R 0 2 and r m 2 rules. Finally, six new compounds are proposed (35-40) with high potential to be used as GABA_B receptor agonists.