A Selectivity Study of FFAR4/FFAR1 Agonists by Molecular Modeling

Influence of the Water Model on the Structure and Interactions of the GPR40 Protein with the Lipid Membrane and the Solvent: Rigid versus Flexible Water Models

G protein-coupled receptors (GPCR) are responsible for modulating various physiological functions and are thus related to the pathophysiology of different diseases. Being potential therapeutic targets, multiple computational methodologies have been developed to analyze their behavior and interactions with other species. The solvent, on the other hand, has received much less attention. In this work, we analyzed the effect of four explicit water models on the structure and interactions of the GPR40 receptor in its apo form. We employed the rigid SPC/E and TIP4P models, and their flexible versions, the FBA/ϵ and TIP4P/ϵflex. We explored the structural changes and their correlation with some bulk dynamic properties of water. Our results showed an adverse effect on the conservation of the secondary structure of the receptor with all the models due to the breaking of the intramolecular hydrogen bond network, being more evident for the TIP4P models. Notably, all four models brought the receptor to states similar to the active one, modifying the intracellular part of the TM5 and TM6 domains in a "hinge" type movement, allowing the opening of the structure. Regarding the dynamic properties, the rigid models showed results comparable to those obtained in other studies on membrane systems. However, flexible models exhibit disparities in the molecular representation of systems. Surprisingly, the FBA/ϵ model improves the molecular picture of several properties, even though their agreement with bulk diffusion is poorer. These findings reinforce our idea that exploring other water models or improving the current ones, to better represent the membrane interface, can lead to a positive impact on the description of the signal transduction mechanisms and the search of new drugs by targeting these receptors.

Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions

Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

Immune regulation of poly unsaturated fatty acids and free fatty acid receptor 4

Fatty acid metabolism contributes to energy supply and plays an important role in regulating immunity. Free fatty acids (FFAs) bind to free fatty acid receptors (FFARs) on the cell surface and mediate effects through the intra-cellular FFAR signaling pathways. FFAR4, also known as G-protein coupled receptor 120 (GPR120), has been identified as the primary receptor of omega-3 polyunsaturated fatty acids (ω-3 PUFAs). FFAR4 is a promising target for treating metabolic and inflammatory disorders due to its immune regulatory functions and the discovery of highly selective and efficient agonists. This review summarizes the reported immune regulatory functions of ω-3 PUFAs and FFAR4 in immune cells and immune-related diseases. We also speculate possible involvements of ω-3 PUFAs and FFAR4 in other types of inflammatory disorders.

Replacing arginine 99 with leucine to study the kinetics of interconnected allosteric interactions between FFAR4 and naturally occurring fatty acids

The long-chain fatty acid receptor FFAR4 is the main G-protein-coupled receptor in the body for detecting long-chain fatty acids. It has been shown that Arg99 may be an important residue for fatty acid recognition and for the activation of hFFAR4, though direct evidence is still lacking. In this study, Arg99 on hFFAR4 was substituted with leucine by genetic manipulation, and a double-layer gold nanoparticle biosensor based on hFFAR4 (Arg99 → Leu) was constructed. The interconnected allosteric interaction between 11 naturally occurring fatty acid ligands and the receptor was determined. The results showed that Arg99 is the key residue on hFFAR4 for the recognition of the carboxyl group on fatty acids. This study offered direct quantitative evidence for the role played by different residues in receptor-ligand recognition and interconnected allosterism, providing a new approach for investigating the mechanisms and kinetics of interconnected receptor-ligand allosterism.

Structure based prediction of a novel GPR120 antagonist based on pharmacophore screening and molecular dynamics simulations

•

Hypothesis of the important residues in conserving the GPR120S “ionic-lock”.

•

Computational model targeting W277 and N313 for virtual screening of GPR120S ligands.

•

Cpd 9 emerged as a potential GPR120S antagonist and anti-cancer treatment.

The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. Recent implications of its role in cancer progression have presented GPR120 as an attractive oncogenic drug target. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. Some agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited.

Herein, following homology modelling of the receptor a pharmacophore hypothesis was derived from 300 ns all-atomic molecular dynamics (MD) simulations on apo, TUG891-bound and Compound39-bound GPR120S (short isoform) receptor models embedded in a water solvated lipid bilayer system. We performed comparative MD analysis on protein–ligand interactions between the two agonist and apo simulations on the stability of the “ionic lock” – a Class A GPCRs characteristic of receptor activation and inactivation. The detailed analysis predicted that ligand interactions with W277 and N313 are critical to conserve the “ionic-lock” conformation (R136 of Helix 3) and prevent GPR120S receptor activation. The results led to generation of a W277 and N313 focused pharmacophore hypothesis and the screening of the ZINC15 database using ZINCPharmer through the structure-based pharmacophore.

100 ns all-atomic molecular dynamics (MD) simulations were performed on 9 small molecules identified and Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) was predicted to be a small-molecule GPR120S antagonist. The conformational results from the collective all-atomic MD analysis provided structural information for further identification and optimisation of novel druggable inhibitors of GPR120S using this rational design approach, which could have future potential for anti-cancer drug development studies.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Recent Updates on Free Fatty Acid Receptor 1 (GPR-40) Agonists for the Treatment of Type 2 Diabetes Mellitus

BACKGROUND

The global incidence of type 2 diabetes mellitus (T2DM) has enthused the development of new antidiabetic targets with low toxicity and long-term stability. In this respect, free fatty acid receptor 1 (FFAR1), which is also recognized as a G protein-coupled receptor 40 (GPR40), is a novel target for the treatment of T2DM. FFAR1/GPR40 has a high level of expression in β-cells of the pancreas, and the requirement of glucose for stimulating insulin release results in immense stimulation to utilise this target in the medication of T2DM.

METHODS

The data used for this review is based on the search of several scienctific databases as well as various patent databases. The main search terms used were free fatty acid receptor 1, FFAR1, FFAR1 agonists, diabetes mellitus, G protein-coupled receptor 40 (GPR40), GPR40 agonists, GPR40 ligands, type 2 diabetes mellitus and T2DM.

RESULTS

The present review article gives a brief overview of FFAR1, its role in T2DM, recent developments in small molecule FFAR1 (GPR40) agonists reported till now, compounds of natural/plant origin, recent patents published in the last few years, mechanism of FFAR1 activation by the agonists, and clinical status of the FFAR1/GPR40 agonists.

CONCLUSION

The agonists of FFAR1/GRP40 showed considerable potential for the therapeutic control of T2DM. Most of the small molecule FFAR1/GPR40 agonists developed were aryl alkanoic acid derivatives (such as phenylpropionic acids, phenylacetic acids, phenoxyacetic acids, and benzofuran acetic acid derivatives) and thiazolidinediones. Some natural/plant-derived compounds, including fatty acids, sesquiterpenes, phenolic compounds, anthocyanins, isoquinoline, and indole alkaloids, were also reported as potent FFAR1 agonists. The clinical investigations of the FFAR1 agonists demonstrated their probable role in the improvement of glucose control. Though, there are some problems still to be resolved in this field as some FFAR1 agonists terminated in the late phase of clinical studies due to "hepatotoxicity." Currently, PBI-4050 is under clinical investigation by Prometic. Further investigation of pharmacophore scaffolds for FFAR1 full agonists as well as multitargeted modulators and corresponding clinical investigations will be anticipated, which can open up new directions in this area.

GPR120/FFAR4 Pharmacology: Focus on Agonists in Type 2 Diabetes Mellitus Drug Discovery

The G-protein coupled receptors (GPCRs) activated by free fatty acids (FFAs) have emerged as new and exciting drug targets, due to their plausible translation from pharmacology to medicines. This perspective aims to report recent research about GPR120/FFAR4 and its involvement in several diseases, including cancer, inflammatory conditions, and central nervous system disorders. The focus is to highlight the importance of GPR120 in Type 2 diabetes mellitus (T2DM). GPR120 agonists, useful in T2DM drug discovery, have been widely explored from a structure–activity relationship point of view. Since the identification of the first reported synthetic agonist TUG-891, the research has paved the way for the development of TUG-based molecules as well as new and different chemical entities. These molecules might represent the starting point for the future discovery of GPR120 agonists as antidiabetic drugs.