Discovery of novel antagonists of human neurotensin receptor 1 on the basis of ligand and protein structure

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

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.

Fluorescent RGD-based pro-apoptotic peptide conjugates as mitochondria-targeting probes for enhanced anticancer activities

We have designed 2-domain anticancer peptides with RGD-based KLAK bi-functional short motifs (linear and cyclic analogues). RGD tripeptide acts as tumor blood vessel 'homing' motif while KLAK tetrapeptide internalized in mitochondria and causes cell apoptosis. All three peptides (RGDKLAK; HM, cyclic-RGDKLAK; HMC-1, and RGD-cyclic-KLAK; HMC-2) were conjugated with fluorescein isothiocyanate isomer-I (5-FITC; F) for in-vivo and in-vitro optical imaging studies. These fluorescent-peptide (FL-peptide) analogues were analyzed to possess α_vβ₃-integrin targeting affinity, high uptake in in-vitro cell binding assays followed by in-vivo tumor xenograft mice studies. Pharmacological profile reveals that F-HMC-1 analogue exhibited selectively and specifically higher affinity for α_vβ₃-integrin than other analogues in U87MG cells in comparison with HeLa cells. The subcutaneous U87MG tumor xenograft mice models clearly visualized the uptake of F-HMC-1 in tumor tissue in contrast with normal tissues with tumor-to-normal tissue ratio (T/NT = 15.9 ± 1.1) at 2 h post-injection. These results suggested that F-HMC-1 peptide has potential diagnostic applications for targeting α_vβ₃-integrin assessed by optical imaging study in U87MG tumor xenograft mice models.

Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Cell-impedance-based label-free technology for the identification of new drugs

INTRODUCTION

Drug discovery has progressed from relatively simple binding or activity screening assays to high-throughput screening of sophisticated compound libraries with emphasis on miniaturization and automation. The development of functional assays has enhanced the success rate in discovering novel drug molecules. Many technologies, originally based on radioactive labeling, have sequentially been replaced by methods based on fluorescence labeling. Recently, the focus has switched to label-free technologies in cell-based screening assays. Areas covered: Label-free, cell-impedance-based methods comprise of different technologies including surface plasmon resonance, mass spectrometry and biosensors applied for screening of anticancer drugs, G protein-coupled receptors, receptor tyrosine kinase and virus inhibitors, drug and nanoparticle cytotoxicity. Many of the developed methods have been used for high-throughput screening in cell lines. Cell viability and morphological damage prediction have been monitored in three-dimensional spheroid human HT-29 carcinoma cells and whole Schistosomula larvae. Expert opinion: Progress in label-free, cell-impedance-based technologies has facilitated drug screening and may enhance the discovery of potential novel drug molecules through, and improve target molecule identification in, alternative signal pathways. The variety of technologies to measure cellular responses through label-free cell-impedance based approaches all support future drug development and should provide excellent assets for finding better medicines.

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.