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Abstract Signaling bias makes reference to the capacity of G-protein coupled receptor GPCR ligands to direct pharmacological stimuli to a subset of effectors among all of those controlled by the receptor. This new signaling modality has added texture to the classical notion of efficacy. In doing so, it has opened new avenues for the development of therapeutic GPCR ligands that specifically modulate signals underlying desired effects while sparing those that support undesired drug actions. Essential to taking advantage of this texture is the ability to identify, quantify and represent bias in a reliable and intuitive manner that ensures comparison among ligands.
Here, we present a practical guide on how the operational model may be used to evaluate ligand efficiency to induce different responses, how differences in response may be used to estimate bias and how quantitative information derived from this analysis may be graphically represented to recreate a drug's unique signaling footprint. The approach used is discussed in terms of data interpretation and limitations that may influence the conclusions drawn from the analysis.
Funding Canadian Institutes of Health Research. MOP Read Article at publisher's site. How does Europe PMC derive its citations network? Protein Interactions. Although, most of the models showed strong goodness-of-fit R 2 and predictivity Q 2 , the addition of cross-terms led to a lower predictive capability of the PCM models. This may be because it is still difficult to fully translate receptor-ligand interfaces to a descriptor value.
HTS has undergone technological advances and innovations that has rendered it as the principal method of drug discovery for years. However, it did not necessarily lead to a great leap forward in the discovery of NCEs as the hit rate for this method is frequently low, in addition to the enormous costs and efforts involved. In turn, computer-aided drug design CADD have been recognized and continuously receives increase in interest and usage such that most of GPCR drug discovery research efforts make use of one or more computational tools, especially in the initial stages of drug design. Furthermore, increasing knowledge of GPCR systems has led to the rising popularity of cheminformatics and chemogenomics as evidenced by the growing number of publicly available databases, which can provide structural or interaction information regarding receptor and its associated ligands.
There are several cheminformatics softwares and web servers available to identify lead compounds targeting GPCRs Khan et al. If there are already known NMR and X-ray crystal structures or reliable homology models available, computational methods based on target protein structures can be exploited Lyne, These tools are related with several computational approaches, including molecular docking, VS, pharmacophore generation, and binding pocket detection.
In cases where no protein structures are available, ligand-based virtual screening LBVS can be utilized. LBVS can be further sub-classified into three: pharmacophore-, similarity-, and machine learning-based VS Basith et al. In the last several years, the increasing number of high resolution GPCR structures has unlocked new avenues for structure-based GPCR drug discovery and design. However, several obstacles remain, including rapid identification of novel fragment-like compounds and structure-based elucidation of GPCR ligand function to name a few.
With the recent innovations in high-throughput, computer, and software technologies, as well as the upsurge of publicly available data, cheminformatics methodologies has no doubt become an essential part of most drug discovery efforts to date. However, a major flaw is seen during cheminformatics model development, wherein the experimental data used is assumed to be correct. In contrast to this assumption, databases can contain errors for ligand structures, bioactivity, activity types, and other information, which often results in ambiguous models leading to erroneous findings.
Several recent articles Fourches et al. A study by Olah et al. Another more recent study by Tiikkainen et al. It is therefore important to carefully and manually curate chemical and biological databases, since even minor errors can cause a substantial decrease in the predictive capability of generated models.
Moreover, while the increasing sophistication of computer programs has allowed researchers an atomistic view of several GPCR systems, approximations of crucial energy terms that cannot be computationally explored at present has greatly limited the accuracy in the perception of these systems. Because of these, researchers should constantly gauge findings against their own scientific knowledge to see whether the results are significant or not. It should always be remembered that computational tools are created and continuously developed to assist in making the drug discovery process more efficient, but nothing can replace a researcher's own knowledge and experience.
Moreover, insights about GPCR structure, function, and binding partners have increased significantly compared to a few decades ago. Despite this, a great deal of information is still beyond our fingertips, such as protein structures of hundreds of unique GPCRs and ligand information for orphan GPCRs. It is imperative not lose fervor in gathering new knowledge to further enhance our understanding of GPCR structures and functions. In the nineteenth century, chemical space exploration was initiated as a counting game to estimate its size Reymond, However, the advent of cheminformatics field and powerful in silico technologies assisted in the exploration of uncharted ligand space from large chemical libraries.
The availability of large public and commercial chemical databases, as well as ligand chemical space exploration tools, provide researchers the ease of accessibility to handle and explore huge chemical data. Cheminformatics is a complex field of study that translates large data into useful knowledge for drug design and optimization protocols. The expansion of GPCR structures and ligands over the past decade is mainly due to the progress in its structural biology and theoretical advancements.
Modelling Of Gpcrs A Practical Handbook 2013
These structural and in silico breakthroughs have led to the implementation of cheminformatics approaches in GPCR drug discovery pipeline. In the GPCR drug discovery protocol, ligand- and structure-based approaches are the most commonly applied ones. LBDD is known as a fast and simple technique for the identification of vital chemical functionalities required for biological activity. However, absence of binding pocket information limits its ability in incorporating several important factors, such as receptor flexibility and ligand bioactive conformation, thereby restricting the discovery of candidate leads to only the ligand classes used in model development Saxena et al.
But due to the prolonged absence of GPCR structures, researchers relied heavily on ligand-based methods for drug discovery and lead optimization, leading to copious ligand structural information for these targets. Following the crystallization of bRho in Palczewski et al. While the current available structures are unable to cover the structural diversity of GPCR protein family members, there is enough that can be used as templates for homology modeling to perform SBDD. In contrast to ligand-based techniques, SBDD can be used to predict ligand bioactive conformation, thus providing a better understanding of receptor-ligand interactions and allowing the discovery of NCEs.
Furthermore, recent researches underpin the significance of emerging integrated approaches in GPCR drug design and discovery. All the cheminformatics approaches discussed in this review are focused toward the identification of novel ligands for GPCR targets based on the structural and ligand data, where several case studies signify the importance of VS.
The evolution of cheminformatics techniques and their synergy in GPCR drug discovery pipeline is the driving force that will facilitate cost-effective and prolific outcomes in the exploration of uncharted GPCR ligand space. Yet, an expert human touch is entailed to authenticate and tame the computer-generated outcome. SB and MC summarized the literature, wrote the manuscript, and prepared the figures. SM wrote part of the manuscript, prepared the figures, and revised the manuscript. JP and NC prepared the tables.
SK and SC supervised all the works, provided critical comments, and wrote the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. National Center for Biotechnology Information , U. Journal List Front Pharmacol v. Front Pharmacol. Published online Mar 9. Macalino , Jongmi Park , Nina A. Author information Article notes Copyright and License information Disclaimer. Edited by: Leonardo G. Sun Choi rk. This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology.
Received Dec 8; Accepted Feb 6. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
This article has been cited by other articles in PMC. Abstract 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. Keywords: GPCR, cheminformatics, drug discovery, ligand-based drug design, structure-based drug design.
Introduction Rational drug design is the inventive process of identifying pharmaceutically-relevant drug candidates based on the information garnered from a biological target Jazayeri et al. Open in a separate window. Figure 1. Figure 2. Figure 3. Figure 4. Representative chemical structures of various GPCR modulators. Cheminformatics and virtual screening In silico screening method started to become popularly used after the integration of high throughput screening HTS and information technology Coudrat et al.
Cheminformatics and de novo ligand design Typically, ligand-based de novo drug design utilizes approved drugs or known inhibitors as reference structures or a source of pharmacophores that are relevant for bioactivity to build new chemical structures. Cheminformatics and chemical genomics While the number of currently available GPCR structures is increasing, it only covers a small portion of this protein superfamily and several other pharmaceutically relevant members are not yet elucidated. Cheminformatics, polypharmacology, drug repositioning, and repurposing Recently, pharmaceutical research focuses not only on the discovery of novel compounds for a known target but also on the discovery of new indications for currently approved drugs.
Figure 5. Overview of the typical workflow of structure-based virtual screening SBVS. Integration of ligand- and structure-based cheminformatics approaches The use of cheminformatics in drug discovery provides an excellent foundation for the integration of structure- and ligand-based strategies due to its application in different stages of drug discovery. Table 3 Cheminformatics tools for structure-based drug discovery. Table 4 Cheminformatics tools for ligand-based drug discovery.
Table 5 Available chemical database for high-throughput virtual screening. Conclusions In the nineteenth century, chemical space exploration was initiated as a counting game to estimate its size Reymond, Author contributions SB and MC summarized the literature, wrote the manuscript, and prepared the figures. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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