Статті в журналах: "Bioinformatic, Computational Biology, GPCR"

1

García-Recio, Adrián, Gemma Navarro, Rafael Franco, Mireia Olivella, Ramon Guixà-González, and Arnau Cordomí. "DIMERBOW: exploring possible GPCR dimer interfaces." Bioinformatics 36, no. 10 (February 25, 2020): 3271–72. http://dx.doi.org/10.1093/bioinformatics/btaa117.

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Abstract Motivation G protein-coupled receptors (GPCRs) can form homo-, heterodimers and larger order oligomers that exert different functions than monomers. The pharmacological potential of such complexes is hampered by the limited information available on the type of complex formed and its quaternary structure. Several GPCR structures in the Protein Data Bank display crystallographic interfaces potentially compatible with physiological interactions. Results Here, we present DIMERBOW, a database and web application aimed to visually browse the complete repertoire of potential GPCR dimers present in solved structures. The tool is suited to help finding the best possible structural template to model GPCR homomers. Availability and implementation DIMERBOW is available at http://lmc.uab.es/dimerbow/. Supplementary information Supplementary data are available at Bioinformatics online.

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2

Shigeta, R., M. Cline, G. Liu, and M. A. Siani-Rose. "GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs." Bioinformatics 19, no. 5 (March 22, 2003): 667–68. http://dx.doi.org/10.1093/bioinformatics/btg061.

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3

Davies, M. N., A. Secker, A. A. Freitas, E. Clark, J. Timmis, and D. R. Flower. "Optimizing amino acid groupings for GPCR classification." Bioinformatics 24, no. 18 (August 1, 2008): 1980–86. http://dx.doi.org/10.1093/bioinformatics/btn382.

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4

Sreekumar, K. R., Y. Huang, M. H. Pausch, and K. Gulukota. "Predicting GPCR-G-protein coupling using hidden Markov models." Bioinformatics 20, no. 18 (August 5, 2004): 3490–99. http://dx.doi.org/10.1093/bioinformatics/bth434.

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5

Townsend-Nicholson, Andrea, Nojood Altwaijry, Andrew Potterton, Inaki Morao, and Alexander Heifetz. "Computational prediction of GPCR oligomerization." Current Opinion in Structural Biology 55 (April 2019): 178–84. http://dx.doi.org/10.1016/j.sbi.2019.04.005.

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6

Zhu, Siyu, Meixian Wu, Ziwei Huang, and Jing An. "Trends in application of advancing computational approaches in GPCR ligand discovery." Experimental Biology and Medicine 246, no. 9 (February 27, 2021): 1011–24. http://dx.doi.org/10.1177/1535370221993422.

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G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

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7

Theodoropoulou, Margarita C., Pantelis G. Bagos, Ioannis C. Spyropoulos, and Stavros J. Hamodrakas. "gpDB: a database of GPCRs, G-proteins, effectors and their interactions." Bioinformatics 24, no. 12 (April 25, 2008): 1471–72. http://dx.doi.org/10.1093/bioinformatics/btn206.

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8

Lahvic, Jamie L., Michelle B. Ammerman, Pulin Li, Song Yang, Nan Chiang, Michael Chase, Olivia Weis, Yi Zhou, Charles Serhan, and Leonard I. Zon. "Eicosanoid-GPCR Signaling Enhances Hematopoiesis and Marrow Transplant." Blood 128, no. 22 (December 2, 2016): 495. http://dx.doi.org/10.1182/blood.v128.22.495.495.

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Abstract Small molecule treatment of hematopoietic stem cells ex vivo has the potential to expand these cells or increase their engraftability. Previously, we discovered that ex vivo treatment of marrow with 11,12-epoxyeicosatrienoic acid (EET) enhances the engraftment of hematopoietic stem and progenitor cells in both zebrafish and mammals. Additionally, EET treatment promotes specification of HSPC from the hemogenic endothelium, suggesting a broad pro-hematopoietic role of this molecule. Indeed, bioactive lipids play an important role as signaling molecules both during embryo development and adult tissue homeostasis. However, due to their small-molecule nature, identifying their receptors biochemically has been a long-standing challenge which impedes the understanding of the biological processes they regulate. The identity of the EET receptor remains unknown despite more than a decade of research. Here, we utilized a novel bioinformatic approach to identify candidate EET receptors and identified a candidate functional in cell culture, zebrafish and mouse assays. EET signaling is known to be G-protein dependent, suggesting its receptor is a G-protein coupled receptor (GPCR). We performed RNAseq on U937 monocytes, EaHy endothelial cells, and PC3M-LN4 prostate cancer cells, three human cell lines with clear EET-responsive phenotypes. These three cell lines expressed 37 GPCR in common at a basal level of greater than or equal to 0.3 fragments per kilobase per million reads (FPKM). 27 of these GPCR were also expressed in a non-EET-responsive cell line, HEK293, leaving only 10 candidate EET receptors. We screened 7 of these candidates for EET-responsiveness using a cell-culture based β-arrestin recruitment assay. Of these, only GPR132 exhibited EET-dependent recruitment of β-arrestin to the cell membrane, indicating GPCR activation. GPR132 was previously identified as a receptor for a variety of small oxygenated fatty acids, and we confirmed that these related molecules induce GPR132-dependent β-arrestin recruitment. We additionally treated developing zebrafish embryos with these molecules. Like EET, these GPR132 ligands increased HSPC numbers in the zebrafish aorta-gonad-mesonephros (AGM) and caused ectopic expression of the HSPC marker runx1 in the zebrafish tail, a phenotype that was previously seen only with EET treatment. To test the requirement of GPR132 for EET signaling, we knocked down the zebrafish ortholog of GPR132 by morpholino injection, which prevented the EET-induced increase of runx1in both the AGM and tail. Finally, we performed competitive whole bone marrow transplant using wildtype and GPR132-/- mice as donors and found that while treatment with EET increases engraftment of WT donor cells, no such improvement is seen in GPR132-/- cells. GPR132 is thus required in both zebrafish and mice for EET phenotypes. Combining bioinformatic, biochemical, and genetic approaches, we identified GPR132 as a receptor for EET involved in regulating hematopoiesis and marrow transplant. GPR132 thus represents a therapeutic target for the enhancement of hematopoietic stem cell transplant, and genetic manipulation of GPR132 could help illuminate the endogenous roles of its fatty acid ligands. Disclosures Zon: Fate, Inc.: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder; Marauder Therapeutics: Equity Ownership, Other: Founder; Scholar Rock: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder.

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9

Yang, Zi, and George Michailidis. "Quantifying heterogeneity of expression data based on principal components." Bioinformatics 35, no. 4 (July 28, 2018): 553–59. http://dx.doi.org/10.1093/bioinformatics/bty671.

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Abstract Motivation The diversity of biological omics data provides richness of information, but also presents an analytic challenge. While there has been much methodological and theoretical development on the statistical handling of large volumes of biological data, far less attention has been devoted to characterizing their veracity and variability. Results We propose a method of statistically quantifying heterogeneity among multiple groups of datasets, derived from different omics modalities over various experimental and/or disease conditions. It draws upon strategies from analysis of variance and principal component analysis in order to reduce dimensionality of the variability across multiple data groups. The resulting hypothesis-based inference procedure is demonstrated with synthetic and real data from a cell line study of growth factor responsiveness based on a factorial experimental design. Availability and implementation Source code and datasets are freely available at https://github.com/yangzi4/gPCA. Supplementary information Supplementary data are available at Bioinformatics online.

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10

Lazim, Raudah, Donghyuk Suh, Jai Woo Lee, Thi Ngoc Lan Vu, Sanghee Yoon, and Sun Choi. "Structural Characterization of Receptor–Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers." International Journal of Molecular Sciences 22, no. 6 (March 22, 2021): 3241. http://dx.doi.org/10.3390/ijms22063241.

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G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).

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11

Sgourakis, N. G., P. G. Bagos, and S. J. Hamodrakas. "Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks." Bioinformatics 21, no. 22 (September 20, 2005): 4101–6. http://dx.doi.org/10.1093/bioinformatics/bti679.

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12

Mishra, Shital Kumar, and Han Wang. "Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish." Biology 10, no. 5 (April 26, 2021): 371. http://dx.doi.org/10.3390/biology10050371.

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Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730–6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

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13

Song, Catharine, Aseem Kumar, and Mazen Saleh. "Bioinformatic Comparison of Bacterial Secretomes." Genomics, Proteomics & Bioinformatics 7, no. 1-2 (June 2009): 37–46. http://dx.doi.org/10.1016/s1672-0229(08)60031-5.

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14

Fox, Jamie C., Monica A. Thomas, Acacia F. Dishman, Olav Larsen, Takashi Nakayama, Osamu Yoshie, Mette Marie Rosenkilde, and Brian F. Volkman. "Structure-function guided modeling of chemokine-GPCR specificity for the chemokine XCL1 and its receptor XCR1." Science Signaling 12, no. 597 (September 3, 2019): eaat4128. http://dx.doi.org/10.1126/scisignal.aat4128.

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Chemokines interact with their G protein–coupled receptors (GPCRs) through a two-step, two-site mechanism and, through this interaction, mediate various homeostatic and immune response mechanisms. Upon initial recognition of the chemokine by the receptor, the amino terminus of the chemokine inserts into the orthosteric pocket of the GPCR, causing conformational changes that trigger intracellular signaling. There is considerable structural and functional evidence to suggest that the amino acid composition and length of the chemokine amino terminus is critical for GPCR activation, complementing the size and amino acid composition of the orthosteric pocket. However, very few structures of a native chemokine-receptor complex have been solved. Here, we used a hybrid approach that combines structure-function data with Rosetta modeling to describe key contacts within a chemokine-GPCR interface. We found that the extreme amino-terminal residues of the chemokine XCL1 (Val1, Gly2, Ser3, and Glu4) contribute a large fraction of the binding energy to its receptor XCR1, whereas residues near the disulfide bond–forming residue Cys11 modulate XCR1 activation. Alterations in the XCL1 amino terminus changed XCR1 activation, as determined by assessing inositol triphosphate accumulation, intracellular calcium release, and directed cell migration. Computational analysis of XCL1-XCR1 interactions revealed functional contacts involving Glu4 of XCL1 and Tyr117 and Arg273 of XCR1. Subsequent mutation of Tyr117 and Arg273 led to diminished binding and activation of XCR1 by XCL1. These findings demonstrate the utility of a hybrid approach, using biological data and homology modeling, to study chemokine-GPCR interactions.

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15

Noonan, Theresa, Katrin Denzinger, Valerij Talagayev, Yu Chen, Kristina Puls, Clemens Alexander Wolf, Sijie Liu, Trung Ngoc Nguyen, and Gerhard Wolber. "Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence." Pharmaceuticals 15, no. 11 (October 22, 2022): 1304. http://dx.doi.org/10.3390/ph15111304.

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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.

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DeMars, Geneva, Francesca Fanelli та David Puett. "The Extreme C-Terminal Region of Gαs Differentially Couples to the Luteinizing Hormone and β2-Adrenergic Receptors". Molecular Endocrinology 25, № 8 (1 серпня 2011): 1416–30. http://dx.doi.org/10.1210/me.2011-0009.

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The mechanisms of G protein coupling to G protein-coupled receptors (GPCR) share general characteristics but may exhibit specific interactions unique for each GPCR/G protein partnership. The extreme C terminus (CT) of G protein α-subunits has been shown to be important for association with GPCR. Hypothesizing that the extreme CT of Gαs is an essential component of the molecular landscape of the GPCR, human LH receptor (LHR), and β2-adrenergic receptor (β2-AR), a model cell system was created for the expression and manipulation of Gαs subunits in LHR+ s49 ck cells that lack endogenous Gαs. On the basis of studies involving truncations, mutations, and chain extensions of Gαs, the CT was found to be necessary for LHR and β2-AR signaling. Some general similarities were found for the responses of the two receptors, but significant differences were also noted. Computational modeling was performed with a combination of comparative modeling, molecular dynamics simulations, and rigid body docking. The resulting models, focused on the Gαs CT, are supported by the experimental observations and are characterized by the interaction of the four extreme CT amino acid residues of Gαs with residues in LHR and β2-AR helix 3, (including R of the DRY motif), helix 6, and intracellular loop 2. This portion of Gαs recognizes the same regions of the two GPCR, although with differences in the details of selected interactions. The predicted longer cytosolic extensions of helices 5 and 6 of β2-AR are expected to contribute significantly to differences in Gαs recognition by the two receptors.

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17

Osipowski, Paweł, Magdalena Pawełkowicz, Michał Wojcieszek, Agnieszka Skarzyńska, Zbigniew Przybecki, and Wojciech Pląder. "A high-quality cucumber genome assembly enhances computational comparative genomics." Molecular Genetics and Genomics 295, no. 1 (October 16, 2019): 177–93. http://dx.doi.org/10.1007/s00438-019-01614-3.

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Abstract Genetic variation is expressed by the presence of polymorphisms in compared genomes of individuals that can be transferred to next generations. The aim of this work was to reveal genome dynamics by predicting polymorphisms among the genomes of three individuals of the highly inbred B10 cucumber (Cucumis sativus L.) line. In this study, bioinformatic comparative genomics was used to uncover cucumber genome dynamics (also called real-time evolution). We obtained a new genome draft assembly from long single molecule real-time (SMRT) sequencing reads and used short paired-end read data from three individuals to analyse the polymorphisms. Using this approach, we uncovered differentiation aspects in the genomes of the inbred B10 line. The newly assembled genome sequence (B10v3) has the highest contiguity and quality characteristics among the currently available cucumber genome draft sequences. Standard and newly designed approaches were used to predict single nucleotide and structural variants that were unique among the three individual genomes. Some of the variant predictions spanned protein-coding genes and their promoters, and some were in the neighbourhood of annotated interspersed repetitive elements, indicating that the highly inbred homozygous plants remained genetically dynamic. This is the first bioinformatic comparative genomics study of a single highly inbred plant line. For this project, we developed a polymorphism prediction method with optimized precision parameters, which allowed the effective detection of small nucleotide variants (SNVs). This methodology could significantly improve bioinformatic pipelines for comparative genomics and thus has great practical potential in genomic metadata handling.

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18

Harini, K., S. Jayashree, Vikas Tiwari, Sneha Vishwanath, and Ramanathan Sowdhamini. "Ligand Docking Methods to Recognize Allosteric Inhibitors for G-Protein-Coupled Receptors." Bioinformatics and Biology Insights 15 (January 2021): 117793222110377. http://dx.doi.org/10.1177/11779322211037769.

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G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug Administration (FDA)-approved GPCR-targeting drugs, there is a continuing challenge of side effects owing to the nonspecific nature of drug binding. We have investigated the diversity of the ligand binding site for this class of proteins against their cognate ligands using computational docking, even if their structures are known already in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site with better score than the binding at the conservative site. Interestingly, amino acid residues at such allosteric binding site are not conserved across GPCR subfamilies. Such a computational approach can assist in the prediction of specific allosteric binders for GPCRs.

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19

Ichioka, Hanae, Yoshihiko Hirohashi, Tatsuya Sato, Masato Furuhashi, Megumi Watanabe, Yosuke Ida, Fumihito Hikage, Toshihiko Torigoe, and Hiroshi Ohguro. "G-Protein-Coupled Receptors Mediate Modulations of Cell Viability and Drug Sensitivity by Aberrantly Expressed Recoverin 3 within A549 Cells." International Journal of Molecular Sciences 24, no. 1 (January 1, 2023): 771. http://dx.doi.org/10.3390/ijms24010771.

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To elucidate the currently unknown molecular mechanisms responsible for the aberrant expression of recoverin (Rec) within cancerous cells, we examined two-dimensional (2D) and three-dimensional (3D) cultures of Rec-negative lung adenocarcinoma A549 cells which had been transfected with a plasmid containing human recoverin cDNA (A549 Rec) or an empty plasmid as a mock control (A549 MOCK). Using these cells, we measured cytotoxicity by several anti-tumor agents (2D), cellular metabolism including mitochondrial and glycolytic functions by a Seahorse bio-analyzer (2D), the physical properties, size and stiffness of the 3D spheroids, trypsin sensitivities (2D and 3D), and RNA sequencing analysis (2D). Compared with the A549 MOCK, the A549 Rec cells showed (1) more sensitivity toward anti-tumor agents (2D) and a 0.25% solution of trypsin (3D); (2) a metabolic shift from glycolysis to oxidative phosphorylation; and (3) the formation of larger and stiffer 3D spheroids. RNA sequencing analysis and bioinformatic analyses of the differentially expressed genes (DEGs) using Gene Ontology (GO) enrichment analysis suggested that aberrantly expressed Rec is most likely associated with several canonical pathways including G-protein-coupled receptor (GPCR)-mediated signaling and signaling by the cAMP response element binding protein (CREB). The findings reported here indicate that the aberrantly expressed Rec-induced modulation of the cell viability and drug sensitivity may be GPCR mediated.

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20

Aladağ, Ahmet Emre, Cesim Erten, and Melih Sözdinler. "Reliability-Oriented bioinformatic networks visualization." Bioinformatics 27, no. 11 (April 9, 2011): 1583–84. http://dx.doi.org/10.1093/bioinformatics/btr178.

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21

Napolitano, Francesco, and Xin Gao. "Special issue on computational biology and bioinformatic applications to the COVID-19 pandemic." Quantitative Biology 10, no. 2 (2022): 123. http://dx.doi.org/10.15302/j-qb-022-0293.

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22

Mario, Manto, Grimaldi Giuliana, Lorivel Thomas, Farina Dario, Popovic Lana, Conforto Silvia, D'Alessio Tommaso, Belda-Lois Juan-Manuel, Pons Jose-Luis, and Rocon Eduardo. "Bioinformatic Approaches Used in Modelling Human Tremor." Current Bioinformatics 4, no. 2 (May 1, 2009): 154–72. http://dx.doi.org/10.2174/157489309788184747.

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23

Hu, Jin-Wu, Guang-Yu Ding, Pei-Yao Fu, Wei-Guo Tang, Qi-Man Sun, Xiao-Dong Zhu, Ying-Hao Shen, et al. "Identification of FOS as a Candidate Risk Gene for Liver Cancer by Integrated Bioinformatic Analysis." BioMed Research International 2020 (March 23, 2020): 1–10. http://dx.doi.org/10.1155/2020/6784138.

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Liver cancer is a lethal disease that is associated with poor prognosis. In order to identify the functionally important genes associated with liver cancer that may reveal novel therapeutic avenues, we performed integrated analysis to profile miRNA and mRNA expression levels for liver tumors compared to normal samples in The Cancer Genome Atlas (TCGA) database. We identified 405 differentially expressed genes and 233 differentially expressed miRNAs in tumor samples compared with controls. In addition, we also performed the pathway analysis and found that mitogen-activated protein kinases (MAPKs) and G-protein coupled receptor (GPCR) pathway were two of the top significant pathway nodes dysregulated in liver cancer. Furthermore, by examining these signaling networks, we discovered that FOS (Fos proto-oncogene, AP-1 transcription factor subunit), LAMC2 (laminin subunit gamma 2), and CALML3 (calmodulin like 3) were the most significant gene nodes with high degrees involved in liver cancer. The expression and disease prediction accuracy of FOS, LAMC2, CALML3, and their interacting miRNAs were further performed using a HCC cohort. Finally, we investigated the prognostic significance of FOS in another HCC cohort. Patients with higher FOS expression displayed significantly shorter time to recurrence (TTR) and overall survival (OS) compared with patients with lower expression. Collectively, our study demonstrates that FOS is a potential prognostic marker for liver cancer that may reveal a novel therapeutic avenue in this lethal disease.

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Pi, Min, Karan Kapoor, Yunpeng Wu, Ruisong Ye, Susan E. Senogles, Satoru K. Nishimoto, Dong-Jin Hwang, et al. "Structural and Functional Evidence for Testosterone Activation of GPRC6A in Peripheral Tissues." Molecular Endocrinology 29, no. 12 (December 1, 2015): 1759–73. http://dx.doi.org/10.1210/me.2015-1161.

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Abstract G protein-coupled receptor (GPCR) family C group 6 member A (GPRC6A) is a multiligand GPCR that is activated by cations, L-amino acids, and osteocalcin. GPRC6A plays an important role in the regulation of testosterone (T) production and energy metabolism in mice. T has rapid, transcription-independent (nongenomic) effects that are mediated by a putative GPCR. We previously found that T can activate GPRC6A in vitro, but the possibility that T is a ligand for GPRC6A remains controversial. Here, we demonstrate direct T binding to GPRC6A and construct computational structural models of GPRC6A that are used to identify potential binding poses of T. Mutations of the predicted binding site residues were experimentally found to block T activation of GPRC6A, in agreement with the modeling. Using Gpr6ca−/− mice, we confirmed that loss of GPRC6A resulted in loss of T rapid signaling responses and elucidated several biological functions regulated by GPRC6A-dependent T rapid signaling, including T stimulation of insulin secretion in pancreatic islets and enzyme expression involved in the biosynthesis of T in Leydig cells. Finally, we identified a stereo-specific effect of an R-isomer of a selective androgen receptor modulator that is predicted to bind to and shown to activate GPRC6A but not androgen receptor. Together, our data show that GPRC6A directly mediates the rapid signaling response to T and uncovers previously unrecognized endocrine networks.

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Papanicolaou, A., and D. G. Heckel. "The GMOD Drupal Bioinformatic Server Framework." Bioinformatics 26, no. 24 (October 22, 2010): 3119–24. http://dx.doi.org/10.1093/bioinformatics/btq599.

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Tang, Min, Ling Kui, Guanyi Lu, and Wenqiang Chen. "Disease-Associated Circular RNAs: From Biology to Computational Identification." BioMed Research International 2020 (August 18, 2020): 1–21. http://dx.doi.org/10.1155/2020/6798590.

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Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer’s disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.

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Kapla, Jon, Ismael Rodríguez-Espigares, Flavio Ballante, Jana Selent, and Jens Carlsson. "Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?" PLOS Computational Biology 17, no. 5 (May 13, 2021): e1008936. http://dx.doi.org/10.1371/journal.pcbi.1008936.

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The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

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28

Burger, Wessel A. C., Patrick M. Sexton, Arthur Christopoulos, and David M. Thal. "Toward an understanding of the structural basis of allostery in muscarinic acetylcholine receptors." Journal of General Physiology 150, no. 10 (September 6, 2018): 1360–72. http://dx.doi.org/10.1085/jgp.201711979.

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Recent breakthroughs and developments in structural biology have led to a spate of crystal structures for G protein–coupled receptors (GPCRs). This is the case for the muscarinic acetylcholine receptors (mAChRs) where inactive-state structures for four of the five subtypes and two active-state structures for one subtype are available. These mAChR crystal structures have provided new insights into receptor mechanisms, dynamics, and allosteric modulation. This is highly relevant to the mAChRs given that these receptors are an exemplar model system for the study of GPCR allostery. Allosteric mechanisms of the mAChRs are predominantly consistent with a two-state model, albeit with some notable recent exceptions. Herein, we discuss the mechanisms for positive and negative allosteric modulation at the mAChRs and compare and contrast these to evidence offered by pharmacological, biochemical, and computational approaches. This analysis provides insight into the fundamental pharmacological properties exhibited by GPCR allosteric modulators, such as enhanced subtype selectivity, probe dependence, and biased modulation while highlighting the current challenges that remain. Though complex, enhanced molecular understanding of allosteric mechanisms will have considerable influence on our understanding of GPCR activation and signaling and development of therapeutic interventions.

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29

Linke, Burkhard, Robert Giegerich, and Alexander Goesmann. "Conveyor: a workflow engine for bioinformatic analyses." Bioinformatics 27, no. 7 (January 28, 2011): 903–11. http://dx.doi.org/10.1093/bioinformatics/btr040.

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30

Rincón-Riveros, Andrés, Duvan Morales, Josefa Antonia Rodríguez, Victoria E. Villegas, and Liliana López-Kleine. "Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions." International Journal of Molecular Sciences 22, no. 21 (October 22, 2021): 11397. http://dx.doi.org/10.3390/ijms222111397.

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Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

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31

García-García, Natalia, Javier Tamames, and Fernando Puente-Sánchez. "M&Ms: a versatile software for building microbial mock communities." Bioinformatics 38, no. 7 (January 12, 2022): 2057–59. http://dx.doi.org/10.1093/bioinformatics/btab882.

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Abstract Summary Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for ‘in silico’ microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. Availability and implementation The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License). Supplementary information Supplementary data are available at Bioinformatics online.

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32

Dankwah, Kwabena Owusu, Jonathon E. Mohl, Khodeza Begum, and Ming-Ying Leung. "What Makes GPCRs from Different Families Bind to the Same Ligand?" Biomolecules 12, no. 7 (June 21, 2022): 863. http://dx.doi.org/10.3390/biom12070863.

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G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.

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33

Gao, Bei, Liang Chi, Yixin Zhu, Xiaochun Shi, Pengcheng Tu, Bing Li, Jun Yin, Nan Gao, Weishou Shen, and Bernd Schnabl. "An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies." Biomolecules 11, no. 4 (April 2, 2021): 530. http://dx.doi.org/10.3390/biom11040530.

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The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.

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34

Ono, Toshihide, and Haretsugu Hishigaki. "Prediction of GPCR-G Protein Coupling Specificity Using Features of Sequences and Biological Functions." Genomics, Proteomics & Bioinformatics 4, no. 4 (2006): 238–44. http://dx.doi.org/10.1016/s1672-0229(07)60004-7.

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35

Szachniuk, Marta. "RNApolis: Computational Platform for RNA Structure Analysis." Foundations of Computing and Decision Sciences 44, no. 2 (June 1, 2019): 241–57. http://dx.doi.org/10.2478/fcds-2019-0012.

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Abstract In the 1970s, computer scientists began to engage in research in the field of structural biology. The first structural databases, as well as models and methods supporting the analysis of biomolecule structures, started to be created. RNA was put at the centre of scientific interest quite late. However, more and more methods dedicated to this molecule are currently being developed. This paper presents RNApolis - a new computing platform, which offers access to seven bioinformatic tools developed to support the RNA structure study. The set of tools include a structural database and systems for predicting, modelling, annotating and evaluating the RNA structure. RNApolis supports research at different structural levels and allows the discovery, establishment, and validation of relationships between the primary, secondary and tertiary structure of RNAs. The platform is freely available at http://rnapolis.pl

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36

Chakiachvili, Marc, Sylvain Milanesi, Anne-Muriel Arigon Chifolleau, and Vincent Lefort. "WAVES: a web application for versatile enhanced bioinformatic services." Bioinformatics 35, no. 1 (July 25, 2018): 140–42. http://dx.doi.org/10.1093/bioinformatics/bty639.

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37

Durand, Pierre Marcel, and Theresa Louise Coetzer. "Utility of Computational Methods to Identify the Apoptosis Machinery in Unicellular Eukaryotes." Bioinformatics and Biology Insights 2 (January 2008): BBI.S430. http://dx.doi.org/10.4137/bbi.s430.

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Apoptosis is the phenotypic result of an active, regulated process of self-destruction. Following various cellular insults, apoptosis has been demonstrated in numerous unicellular eukaryotes, but very little is known about the genes and proteins that initiate and execute this process in this group of organisms. A bioinformatic approach presents an array of powerful methods to direct investigators in the identification of the apoptosis machinery in protozoans. In this review, we discuss some of the available computational methods and illustrate how they may be applied using the identification of a Plasmodium falciparum metacaspase gene as an example.

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38

Morales-Pastor, Adrian, Francho Nerín-Fonz, David Aranda-García, Miguel Dieguez-Eceolaza, Brian Medel-Lacruz, Mariona Torrens-Fontanals, Alejandro Peralta-García, and Jana Selent. "In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias." International Journal of Molecular Sciences 23, no. 14 (July 15, 2022): 7809. http://dx.doi.org/10.3390/ijms23147809.

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Signaling bias is a promising characteristic of G protein-coupled receptors (GPCRs) as it provides the opportunity to develop more efficacious and safer drugs. This is because biased ligands can avoid the activation of pathways linked to side effects whilst still producing the desired therapeutic effect. In this respect, a deeper understanding of receptor dynamics and implicated allosteric communication networks in signaling bias can accelerate the research on novel biased drug candidates. In this review, we aim to provide an overview of computational methods and techniques for studying allosteric communication and signaling bias in GPCRs. This includes (i) the detection of allosteric communication networks and (ii) the application of network theory for extracting relevant information pipelines and highly communicated sites in GPCRs. We focus on the most recent research and highlight structural insights obtained based on the framework of allosteric communication networks and network theory for GPCR signaling bias.

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39

Wang, Ying, Jen-Fu Chiu, and Qing-Yu He. "Bioinformatic Application in Proteomic Research on Biomarker Discovery and Drug Target Validation." Current Bioinformatics 2, no. 1 (January 1, 2007): 11–20. http://dx.doi.org/10.2174/157489307779314294.

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40

Janovick, Jo Ann, Akshay Patny, Ralph Mosley, Mark T. Goulet, Michael D. Altman, Thomas S. Rush, Anda Cornea, and P. Michael Conn. "Molecular Mechanism of Action of Pharmacoperone Rescue of Misrouted GPCR Mutants: The GnRH Receptor." Molecular Endocrinology 23, no. 2 (February 1, 2009): 157–68. http://dx.doi.org/10.1210/me.2008-0384.

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Abstract The human GnRH receptor (hGnRHR), a G protein-coupled receptor, is a useful model for studying pharmacological chaperones (pharmacoperones), drugs that rescue misfolded and misrouted protein mutants and restore them to function. This technique forms the basis of a therapeutic approach of rescuing mutants associated with human disease and restoring them to function. The present study relies on computational modeling, followed by site-directed mutagenesis, assessment of ligand binding, effector activation, and confocal microscopy. Our results show that two different chemical classes of pharmacoperones act to stabilize hGnRHR mutants by bridging residues D98 and K121. This ligand-mediated bridge serves as a surrogate for a naturally occurring and highly conserved salt bridge (E90–K121) that stabilizes the relation between transmembranes 2 and 3, which is required for passage of the receptor through the cellular quality control system and to the plasma membrane. Our model was used to reveal important pharmacophoric features, and then identify a novel chemical ligand, which was able to rescue a D98 mutant of the hGnRHR that could not be rescued as effectively by previously known pharmacoperones.

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41

De Las Rivas, Javier, and Alberto de Luis. "Interactome Data and Databases: Different Types of Protein Interaction." Comparative and Functional Genomics 5, no. 2 (2004): 173–78. http://dx.doi.org/10.1002/cfg.377.

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In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

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42

Rieder, Dietmar, Georgios Fotakis, Markus Ausserhofer, Geyeregger René, Wolfgang Paster, Zlatko Trajanoski, and Francesca Finotello. "nextNEOpi: a comprehensive pipeline for computational neoantigen prediction." Bioinformatics 38, no. 4 (November 12, 2021): 1131–32. http://dx.doi.org/10.1093/bioinformatics/btab759.

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Abstract Summary Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients’ Human Leukocyte Antigen types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy. Availability and implementation nextNEOpi source code and documentation are available at https://github.com/icbi-lab/nextNEOpi Contact dietmar.rieder@i-med.ac.at or francesca.finotello@uibk.ac.at Supplementary information Supplementary data are available at Bioinformatics online.

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43

Singh, Pooja, Salma Jamal, Faraz Ahmed, Najumu Saqib, Seema Mehra, Waseem Ali, Deodutta Roy, Nasreen Z. Ehtesham, and Seyed E. Hasnain. "Computational modeling and bioinformatic analyses of functional mutations in drug target genes in Mycobacterium tuberculosis." Computational and Structural Biotechnology Journal 19 (2021): 2423–46. http://dx.doi.org/10.1016/j.csbj.2021.04.034.

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44

Hu, Jun, Yang Li, Jing-Yu Yang, Hong-Bin Shen, and Dong-Jun Yu. "GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure." Computational Biology and Chemistry 60 (February 2016): 59–71. http://dx.doi.org/10.1016/j.compbiolchem.2015.11.007.

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45

Söldner, Christian A., Anselm H. C. Horn, and Heinrich Sticht. "A Metadynamics-Based Protocol for the Determination of GPCR-Ligand Binding Modes." International Journal of Molecular Sciences 20, no. 8 (April 22, 2019): 1970. http://dx.doi.org/10.3390/ijms20081970.

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G protein-coupled receptors (GPCRs) are a main drug target and therefore a hot topic in pharmaceutical research. One important prerequisite to understand how a certain ligand affects a GPCR is precise knowledge about its binding mode and the specific underlying interactions. If no crystal structure of the respective complex is available, computational methods can be used to deduce the binding site. One of them are metadynamics simulations which have the advantage of an enhanced sampling compared to conventional molecular dynamics simulations. However, the enhanced sampling of higher-energy states hampers identification of the preferred binding mode. Here, we present a novel protocol based on clustering of multiple walker metadynamics simulations which allows identifying the preferential binding mode from such conformational ensembles. We tested this strategy for three different model systems namely the histamine H1 receptor in combination with its physiological ligand histamine, as well as the β 2 adrenoceptor with its agonist adrenaline and its antagonist alprenolol. For all three systems, the proposed protocol was able to reproduce the correct binding mode known from the literature suggesting that the approach can more generally be applied to the prediction of GPCR ligand binding in future.

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46

Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (November 25, 2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

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47

Omotuyi, Olaposi, and Hiroshi Ueda. "A Novel Unified Ab Initio and Template-Based Approach to GPCR Modeling: Case of EDG-LPA Receptors." Current Bioinformatics 8, no. 5 (October 31, 2013): 603–10. http://dx.doi.org/10.2174/1574893611308050603.

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48

Hu, Wei-Jiang, Sheng-Mei Zhou, Joshua SungWoo Yang, and Fan-Guo Meng. "Computational Simulations to Predict Creatine Kinase-Associated Factors: Protein-Protein Interaction Studies of Brain and Muscle Types of Creatine Kinases." Enzyme Research 2011 (August 3, 2011): 1–12. http://dx.doi.org/10.4061/2011/328249.

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Creatine kinase (CK; EC 2.7.3.2) is related to several skin diseases such as psoriasis and dermatomyositis. CK is important in skin energy homeostasis because it catalyzes the reversible transfer of a phosphoryl group from MgATP to creatine. In this study, we predicted CK binding proteins via the use of bioinformatic tools such as protein-protein interaction (PPI) mappings and suggest the putative hub proteins for CK interactions. We obtained 123 proteins for brain type CK and 85 proteins for muscle type CK in the interaction networks. Among them, several hub proteins such as NFKB1, FHL2, MYOC, and ASB9 were predicted. Determination of the binding factors of CK can further promote our understanding of the roles of CK in physiological conditions.

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Fancello, Laura, Alessandro Guida, Gianmaria Frige, Arnaud Gerard Michel Ceol, Gabriele Babini, Giovanni Luca Scaglione, Mario Zanfardino, et al. "TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power." Bioinformatics 38, no. 6 (December 20, 2021): 1724–26. http://dx.doi.org/10.1093/bioinformatics/btab836.

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Abstract Motivation Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. Results We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. Availability and implementation R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. Supplementary information Supplementary data are available at Bioinformatics online.

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50

Manandhar, Anjela, Mona H. Haron, Michael L. Klein, and Khaled Elokely. "Understanding the Dynamics of the Structural States of Cannabinoid Receptors and the Role of Different Modulators." Life 12, no. 12 (December 18, 2022): 2137. http://dx.doi.org/10.3390/life12122137.

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The cannabinoid receptors CB1R and CB2R are members of the G protein-coupled receptor (GPCR) family. These receptors have recently come to light as possible therapeutic targets for conditions affecting the central nervous system. However, because CB1R is known to have psychoactive side effects, its potential as a drug target is constrained. Therefore, targeting CB2R has become the primary focus of recent research. Using various molecular modeling studies, we analyzed the active, inactive, and intermediate states of both CBRs in this study. We conducted in-depth research on the binding properties of various groups of cannabinoid modulators, including agonists, antagonists, and inverse agonists, with all of the different conformational states of the CBRs. The binding effects of these modulators were studied on various CB structural features, including the movement of the transmembrane helices, the volume of the binding cavity, the internal fluids, and the important GPCR properties. Then, using in vitro experiments and computational modeling, we investigated how vitamin E functions as a lipid modulator to influence THC binding. This comparative examination of modulator binding to CBRs provides significant insight into the mechanisms of structural alterations and ligand affinity, which can directly help in the rational design of selective modulators that target either CB1R or CB2R.

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