Targeting the Aryl Hydrocarbon Receptor (AhR): A Review of the In-Silico Screening Approaches to Identify AhR Modulators

Aryl hydrocarbon receptor (AhR) is a biological sensor that integrates environmental, metabolic, and endogenous signals to control complex cellular responses in physiological and pathophysiological functions. The full-length AhR encompasses various domains, including a bHLH, a PAS A, a PAS B, and transactivation domains. With the exception of the PAS B and transactivation domains, the available 3D structures of AhR revealed structural details of its subdomains interactions as well as its interaction with other protein partners. Towards screening for novel AhR modulators homology modeling was employed to develop AhR-PAS B domain models. These models were validated using molecular dynamics simulations and binding site identification methods. Furthermore, docking of well-known AhR ligands assisted in confirming these binding pockets and discovering critical residues to host these ligands. In this context, virtual screening utilizing both ligand-based and structure-based methods screened large databases of small molecules to identify novel AhR agonists or antagonists and suggest hits from these screens for validation in an experimental biological test. Recently, machine-learning algorithms are being explored as a tool to enhance the screening process of AhR modulators and to minimize the errors associated with structure-based methods. This chapter reviews all in silico screening that were focused on identifying AhR modulators and discusses future perspectives towards this goal.

Mono-Alkylated Ligands Based on Pyrazole and Triazole Derivatives Tested Against Fusarium oxysporum f. sp. albedinis: Synthesis, Characterization, DFT, and Phytase Binding Site Identification Using Blind Docking/Virtual Screening for Potent Fophy Inhibitors

Twelve recent compounds, incorporating several heterocyclic moieties such as pyrazole, thiazole, triazole, and benzotriazole, made in excellent yield up to 37–99.6%. They were tested against Fusarium oxysporum f. sp. albedinis fungi (Bayoud disease), where the best results are for compounds 2, 4, and 5 with IC50 = 18.8–54.4 μg/mL. Density functional theory (DFT) study presented their molecular reactivity, while the docking simulations to describe the synergies between the trained compounds of dataset containing all the tested compounds (57 molecules) and F. oxysporum phytase domain (Fophy) enzyme as biological target. By comparing the results of the docking studies for the Fophy protein, it is found that compound 5 has the best affinity followed by compounds 2 and 4, so there is good agreement with the experimental results where their IC50 values are in the following order: 74.28 (5) < 150 (2) < 214.10 (4), using Blind docking/virtual screening of the homology modeled protein and two different tools as Autodock Vina and Dockthor web tool that gave us predicted sites for further antifungal drug design.

Spatiotemporal identification of druggable binding sites using deep learning

Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.

The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability

ABSTRACT The main transcriptional regulator of leucine biosynthesis in the yeast Saccharomyces cerevisiae is the transcription factor Leu3. It has previously been reported that Leu3 always binds to its target genes, but requires activation to induce their expression. In a recent large-scale study of high-resolution transcription factor binding site identification, we showed that Leu3 has divergent binding sites in different cultivation conditions, thereby questioning the results of earlier studies. Here, we present a follow-up study using chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate the influence of leucine supplementation on Leu3 binding activity and strength. With this new data set we are able to show that Leu3 exhibits changes in binding activity in response to changing levels of leucine availability.

Spatiotemporal identification of druggable binding sites using deep learning

Identification of novel protein binding sites expands «druggable genome» and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble to object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, named BiteNet, that considers protein conformations as the 3D-images, binding sites as the objects on these images to detect, and conformational ensembles of proteins as the 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding sites in G protein-coupled receptors. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minute to analyze 1000 conformations of a protein with 2000 atoms. BiteNet is available at https://github.com/i-Molecule/bitenet.

DNA shape complements sequence-based representations of transcription factor binding sites

The position weight matrix (PWM) has long been a useful tool for describing variation in the composition of regions of DNA such as transcription factor (TF) binding sites. It is difficult, however, to relate the sequence-based representation of a DNA motif to the biological features of the interaction of a TF with its binding site. Here we present an alternative strategy for representing DNA motifs – called Structural Motif (StruM) – that can easily represent different sets of structural features. Structural features are inferred from dinucleotide properties listed in the Dinucleotide Property Database. StruMs are able to specifically model TF binding sites, using an encoding strategy that is distinct from sequence-based models. This difference in encoding strategies makes StruMs complementary to sequence-based methods of TF binding site identification.

Hotspot Identification on Protein Surfaces Using Probe-Based MD Simulations: Successes and Challenges

Molecular Dynamics (MD) based computational co-solvent mapping methods involve the generation of an ensemble of MD-sampled target protein conformations and using selected small molecule fragments to identify and characterize binding sites on the surface of a target protein. This approach incorporates atomic-level solvation effects and protein mobility. It has shown great promise in the identification of conventional competitive and allosteric binding sites. It is also currently emerging as a useful tool in the early stages of drug discovery. This review summarizes efforts as well as discusses some methodological advances and challenges in binding site identification process through these co-solvent mapping methods.