1
|
Czech C, Lang T, Graßl A, Steuer A, Di Pizio A, Behrens M, Lang R. Identification of mozambioside roasting products and their bitter taste receptor activation. Food Chem 2024; 446:138884. [PMID: 38432139 DOI: 10.1016/j.foodchem.2024.138884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
Arabica coffee contains the bitter-tasting diterpene glycoside mozambioside, which degrades during coffee roasting, leading to yet unknown structurally related degradation products with possibly similar bitter-receptor-activating properties. The study aimed at the generation, isolation, and structure elucidation of individual pyrolysis products of mozambioside and characterization of bitter receptor activation by in vitro analysis in HEK 293T-Gα16gust44 cells. The new compounds 17-O-β-d-glucosyl-11-hydroxycafestol-2-on, 11-O-β-d-glucosyl-16-desoxycafestol-2-on, 11-O-β-d-glucosyl-(S)-16-desoxy-17-oxocafestol-2-on, 11-O-β-d-glucosyl-15,16-dehydrocafestol-2-on, and 11-O-β-d-glucosyl-(R)-16-desoxy-17-oxocafestol-2-on were isolated from pyrolyzed mozambioside by HPLC and identified by NMR and UHPLC-ToF-MS. Roasting products 11-O-β-d-glucosyl-(S)-16-desoxy-17-oxocafestol-2-on, 11-O-β-d-glucosyl-15,16-dehydrocafestol-2-on, and 11-O-β-d-glucosyl-(R)-16-desoxy-17-oxocafestol-2-on had lower bitter receptor activation thresholds compared to mozambioside. Molecular docking simulations revealed the binding modes of the compounds 11-O-β-d-glucosyl-15,16-dehydrocafestol-2-on and 11-O-β-d-glucosyl-(R)-16-desoxy-17-oxocafestol-2-on and their aglycone 11-hydroxycafestol-2-on in the two cognate receptors TAS2R43 and TAS2R46. The newly discovered roasting products 17-O-β-d-glucosyl-11-hydroxycafestol-2-on, 11-O-β-d-glucosyl-(S)-16-desoxy-17-oxocafestol-2-on, 11-O-β-d-glucosyl-15,16-dehydrocafestol-2-on, and 11-O-β-d-glucosyl-(R)-16-desoxy-17-oxocafestol-2-on were detected in authentic roast coffee brew by UHPLC-ToF-MS and could contribute to coffee's bitter taste impression.
Collapse
Affiliation(s)
- Coline Czech
- TUM Graduate School, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354 Freising, Germany; Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Tatjana Lang
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Angelika Graßl
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Alexandra Steuer
- TUM Graduate School, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354 Freising, Germany; Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany; Chemoinformatics and Protein Modelling, School of Life Science, Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Maik Behrens
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| | - Roman Lang
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354 Freising, Germany.
| |
Collapse
|
2
|
Birgül Iyison N, Abboud C, Abboud D, Abdulrahman AO, Bondar AN, Dam J, Georgoussi Z, Giraldo J, Horvat A, Karoussiotis C, Paz-Castro A, Scarpa M, Schihada H, Scholz N, Güvenc Tuna B, Vardjan N. ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system. Br J Pharmacol 2024. [PMID: 38825750 DOI: 10.1111/bph.16389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/09/2024] [Accepted: 02/24/2024] [Indexed: 06/04/2024] Open
Abstract
G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.
Collapse
Affiliation(s)
- Necla Birgül Iyison
- Department of Molecular Biology and Genetics, University of Bogazici, Istanbul, Turkey
| | - Clauda Abboud
- Laboratory of Molecular Pharmacology, GIGA-Molecular Biology of Diseases, University of Liege, Liege, Belgium
| | - Dayana Abboud
- Laboratory of Molecular Pharmacology, GIGA-Molecular Biology of Diseases, University of Liege, Liege, Belgium
| | | | - Ana-Nicoleta Bondar
- Faculty of Physics, University of Bucharest, Magurele, Romania
- Forschungszentrum Jülich, Institute for Computational Biomedicine (IAS-5/INM-9), Jülich, Germany
| | - Julie Dam
- Institut Cochin, CNRS, INSERM, Université Paris Cité, Paris, France
| | - Zafiroula Georgoussi
- Laboratory of Cellular Signalling and Molecular Pharmacology, Institute of Biosciences and Applications, National Center for Scientific Research "Demokritos", Athens, Greece
| | - Jesús Giraldo
- Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anemari Horvat
- Laboratory of Neuroendocrinology - Molecular Cell Physiology, Institute of Pathophysiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Laboratory of Cell Engineering, Celica Biomedical, Ljubljana, Slovenia
| | - Christos Karoussiotis
- Laboratory of Cellular Signalling and Molecular Pharmacology, Institute of Biosciences and Applications, National Center for Scientific Research "Demokritos", Athens, Greece
| | - Alba Paz-Castro
- Molecular Pharmacology of GPCRs research group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela, Santiago, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago, Spain
| | - Miriam Scarpa
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Hannes Schihada
- Department of Pharmaceutical Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Nicole Scholz
- Rudolf Schönheimer Institute of Biochemistry, Division of General Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Bilge Güvenc Tuna
- Department of Biophysics, School of Medicine, Yeditepe University, Istanbul, Turkey
| | - Nina Vardjan
- Laboratory of Neuroendocrinology - Molecular Cell Physiology, Institute of Pathophysiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Laboratory of Cell Engineering, Celica Biomedical, Ljubljana, Slovenia
| |
Collapse
|
3
|
Pacalon J, Audic G, Magnat J, Philip M, Golebiowski J, Moreau CJ, Topin J. Elucidation of the structural basis for ligand binding and translocation in conserved insect odorant receptor co-receptors. Nat Commun 2023; 14:8182. [PMID: 38081900 PMCID: PMC10713630 DOI: 10.1038/s41467-023-44058-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
In numerous insects, the olfactory receptor family forms a unique class of heteromeric cation channels. Recent progress in resolving the odorant receptor structures offers unprecedented opportunities for deciphering their molecular mechanisms of ligand recognition. Unexpectedly, these structures in apo or ligand-bound states did not reveal the pathway taken by the ligands between the extracellular space and the deep internal cavities. By combining molecular modeling with electrophysiological recordings, we identified amino acids involved in the dynamic entry pathway and the binding of VUAA1 to Drosophila melanogaster's odorant receptor co-receptor (Orco). Our results provide evidence for the exact location of the agonist binding site and a detailed and original mechanism of ligand translocation controlled by a network of conserved residues. These findings would explain the particularly high selectivity of Orcos for their ligands.
Collapse
Affiliation(s)
- Jody Pacalon
- Université Côte d'Azur, Institut de Chimie de Nice UMR7272, CNRS, Nice, France
| | | | | | - Manon Philip
- Univ. Grenoble Alpes, CNRS, CEA, IBS, Grenoble, France
| | - Jérôme Golebiowski
- Department of Brain & Cognitive Sciences, DGIST, 333, Techno JungAng, Daero, HyeongPoong Myeon, Daegu, Republic of Korea
| | | | - Jérémie Topin
- Université Côte d'Azur, Institut de Chimie de Nice UMR7272, CNRS, Nice, France.
| |
Collapse
|
4
|
Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
Collapse
Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | | |
Collapse
|
5
|
Ziegler F, Steuer A, Di Pizio A, Behrens M. Physiological activation of human and mouse bitter taste receptors by bile acids. Commun Biol 2023; 6:612. [PMID: 37286811 DOI: 10.1038/s42003-023-04971-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/23/2023] [Indexed: 06/09/2023] Open
Abstract
Beside the oral cavity, bitter taste receptors are expressed in several non-gustatory tissues. Whether extra-oral bitter taste receptors function as sensors for endogenous agonists is unknown. To address this question, we devised functional experiments combined with molecular modeling approaches to investigate human and mouse receptors using a variety of bile acids as candidate agonists. We show that five human and six mouse receptors are responsive to an array of bile acids. Moreover, their activation threshold concentrations match published data of bile acid concentrations in human body fluids, suggesting a putative physiological activation of non-gustatory bitter receptors. We conclude that these receptors could serve as sensors for endogenous bile acid levels. These results also indicate that bitter receptor evolution may not be driven solely by foodstuff or xenobiotic stimuli, but also depend on endogenous ligands. The determined bitter receptor activation profiles of bile acids now enable detailed physiological model studies.
Collapse
Affiliation(s)
- Florian Ziegler
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Alexandra Steuer
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany
| | - Maik Behrens
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, Freising, Germany.
| |
Collapse
|
6
|
Tiroch J, Dunkel A, Sterneder S, Zehentner S, Behrens M, Di Pizio A, Ley JP, Lieder B, Somoza V. Human Gingival Fibroblasts as a Novel Cell Model Describing the Association between Bitter Taste Thresholds and Interleukin-6 Release. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:5314-5325. [PMID: 36943188 PMCID: PMC10080686 DOI: 10.1021/acs.jafc.2c06979] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Human gingival fibroblast cells (HGF-1 cells) present an important cell model to investigate the gingiva's response to inflammatory stimuli such as lipopolysaccharides from Porphyromonas gingivalis (Pg-LPS). Recently, we demonstrated trans-resveratrol to repress the Pg-LPS evoked release of the pro-inflammatory cytokine interleukin-6 (IL-6) via involvement of bitter taste sensing receptor TAS2R50 in HGF-1 cells. Since HGF-1 cells express most of the known 25 TAS2Rs, we hypothesized an association between a compound's bitter taste threshold and its repressing effect on the Pg-LPS evoked IL-6 release by HGF-1 cells. To verify our hypothesis, 11 compounds were selected from the chemical bitter space and subjected to the HGF-1 cell assay, spanning a concentration range between 0.1 μM and 50 mM. In the first set of experiments, the specific role of TAS2R50 was excluded by results from structurally diverse TAS2R agonists and antagonists and by means of a molecular docking approach. In the second set of experiments, the HGF-1 cell response was used to establish a linear association between a compound's effective concentration to repress the Pg-LPS evoked IL-6 release by 25% and its bitter taste threshold concentration published in the literature. The Pearson correlation coefficient revealed for this linear association was R2 = 0.60 (p < 0.01), exceeding respective data for the test compounds from a well-established native cell model, the HGT-1 cells, with R2 = 0.153 (p = 0.263). In conclusion, we provide a predictive model for bitter tasting compounds with a potential to act as anti-inflammatory substances.
Collapse
Affiliation(s)
- Johanna Tiroch
- Department
of Physiological Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna 1090, Austria
| | - Andreas Dunkel
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Freising 85354, Germany
| | - Sonja Sterneder
- Department
of Physiological Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna 1090, Austria
| | - Sofie Zehentner
- Department
of Physiological Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna 1090, Austria
| | - Maik Behrens
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Freising 85354, Germany
| | - Antonella Di Pizio
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Freising 85354, Germany
| | | | - Barbara Lieder
- Department
of Physiological Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
| | - Veronika Somoza
- Department
of Physiological Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, Freising 85354, Germany
- Chair
for Nutritional Systems Biology, Technical
University Munich, Freising 85354, Germany
| |
Collapse
|
7
|
Mordalski S, Kościółek T. Homology Modeling of the G Protein-Coupled Receptors. Methods Mol Biol 2023; 2627:167-181. [PMID: 36959447 DOI: 10.1007/978-1-0716-2974-1_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
G protein-coupled receptors (GPCRs) are therapeutically important family of membrane proteins. Despite growing number of experimental structures available for GPCRs, homology modeling remains a relevant method for studying these receptors and for discovering new ligands for them. Here we describe the state-of-the-art methods for modeling GPCRs, starting from template selection, through fine-tuning sequence alignment to model refinement.
Collapse
Affiliation(s)
- Stefan Mordalski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology Polish Academy of Sciences, Krakow, Poland.
| | - Tomasz Kościółek
- Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| |
Collapse
|
8
|
Cai T, Xie L, Zhang S, Chen M, He D, Badkul A, Liu Y, Namballa HK, Dorogan M, Harding WW, Mura C, Bourne PE, Xie L. End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins. PLoS Comput Biol 2023; 19:e1010851. [PMID: 36652496 PMCID: PMC9886305 DOI: 10.1371/journal.pcbi.1010851] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 01/30/2023] [Accepted: 01/05/2023] [Indexed: 01/19/2023] Open
Abstract
Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.
Collapse
Affiliation(s)
- Tian Cai
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, United States of America
| | - Li Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Shuo Zhang
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, United States of America
| | - Muge Chen
- Master Program in Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
| | - Di He
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, United States of America
| | - Amitesh Badkul
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Hari Krishna Namballa
- Department of Chemistry, Hunter College, The City University of New York, New York, New York, United States of America
| | - Michael Dorogan
- Department of Chemistry, Hunter College, The City University of New York, New York, New York, United States of America
| | - Wayne W. Harding
- Department of Chemistry, Hunter College, The City University of New York, New York, New York, United States of America
| | - Cameron Mura
- School of Data Science & Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Philip E. Bourne
- School of Data Science & Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Lei Xie
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, New York, United States of America
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, New York, United States of America
| |
Collapse
|
9
|
Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals (Basel) 2022; 15:ph15111304. [DOI: 10.3390/ph15111304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/15/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
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.
Collapse
|
10
|
Veríssimo GC, Serafim MSM, Kronenberger T, Ferreira RS, Honorio KM, Maltarollo VG. Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern. Expert Opin Drug Discov 2022; 17:929-947. [PMID: 35983695 DOI: 10.1080/17460441.2022.2114451] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed. AREAS COVERED One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future. EXPERT OPINION Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.
Collapse
Affiliation(s)
- Gabriel C Veríssimo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Mateus Sá M Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Thales Kronenberger
- Department of Medical Oncology and Pneumology, Internal Medicine VIII, University Hospital of Tübingen, Tübingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia M Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
| | - Vinícius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| |
Collapse
|
11
|
Tan RK, Liu Y, Xie L. Reinforcement learning for systems pharmacology-oriented and personalized drug design. Expert Opin Drug Discov 2022; 17:849-863. [PMID: 35510835 PMCID: PMC9824901 DOI: 10.1080/17460441.2022.2072288] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complex diseases. AREAS COVERED In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed. EXPERT OPINION In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.
Collapse
Affiliation(s)
- Ryan K. Tan
- Department of Computer Science, Hunter College, The City University of New York
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York,Ph.D. Program in Computer Science, Biology & Biochemistry, The Graduate Center, The City University of New York,Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University,Correspondence should be addressed to Lei Xie -
| |
Collapse
|
12
|
Computational approaches for the design of novel dopamine D2 and serotonin 5-HT2A receptor dual antagonist towards schizophrenia. In Silico Pharmacol 2022; 10:7. [PMID: 35433192 PMCID: PMC8990614 DOI: 10.1007/s40203-022-00121-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 02/03/2022] [Indexed: 10/26/2022] Open
|
13
|
Targowska-Duda KM, Maj M, Drączkowski P, Budzyńska B, Boguszewska-Czubara A, Wróbel TM, Laitinen T, Kaczmar P, Poso A, Kaczor AA. WaterMap guided structure-based virtual screening for acetylcholinesterase inhibitors. ChemMedChem 2022; 17:e202100721. [PMID: 35157366 DOI: 10.1002/cmdc.202100721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/11/2022] [Indexed: 11/11/2022]
Abstract
Structure-based virtual screening of the Enamine database of 1.7 million compounds followed by WaterMap calculations (a molecular dynamics simulation-based method) was applied to identify novel AChE inhibitors. The inhibitory potency of 29 selected compounds against electric eel (ee) AChE was determined using the Ellman's method. Three compounds were found active (success rate 10%). For the most potent compound (~40% of inhibition at 10 μM), 20 derivatives were discovered based on the Enamine similarity search. Finally, five compounds were found promising (IC 50 ranged from 6.3 µM to 17.5 µM) inhibitors of AChE. The performed similarity and fragment analysis confirmed significant structural novelty of novel AChE inhibitors. Toxicity/safety of selected compounds was determined in zebrafish model.
Collapse
Affiliation(s)
| | - Maciej Maj
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Department of Biopharmacy, POLAND
| | - Piotr Drączkowski
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Department of Synthesis and Chemical Technology of Pharmaceutical Substances, POLAND
| | - Barbara Budzyńska
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Independent Laboratory of Behavioral Studies, POLAND
| | - Anna Boguszewska-Czubara
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Department of Medical Chemistry, POLAND
| | - Tomasz M Wróbel
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Department of Synthesis and Chemical Technology of Pharmaceutical Substances, POLAND
| | - Tuomo Laitinen
- University of Eastern Finland - Kuopio Campus: Ita-Suomen yliopisto - Kuopion kampus, School of Pharmacy, FINLAND
| | - Patrycja Kaczmar
- Medical University of Lublin: Uniwersytet Medyczny w Lublinie, Department of Biopharmacy, POLAND
| | - Antti Poso
- University of Eastern Finland - Kuopio Campus: Ita-Suomen yliopisto - Kuopion kampus, School of Pharmacy, FINLAND
| | - Agnieszka Anna Kaczor
- Medical University of Lublin, Department of Synthesis and Chemical Technology of Pharmaceutical Substances, 4A Chodzki St, 20093, Lublin, POLAND
| |
Collapse
|
14
|
Nicoli A, Dunkel A, Giorgino T, de Graaf C, Di Pizio A. Classification Model for the Second Extracellular Loop of Class A GPCRs. J Chem Inf Model 2022; 62:511-522. [PMID: 35113559 DOI: 10.1021/acs.jcim.1c01056] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The extracellular loop 2 (ECL2) is the longest and the most diverse loop among class A G protein-coupled receptors (GPCRs). It connects the transmembrane (TM) helices 4 and 5 and contains a highly conserved cysteine through which it is bridged with TM3. In this paper, experimental ECL2 structures were analyzed based on their sequences, shapes, and intramolecular contacts. To take into account the flexibility, we incorporated into our analyses information from the molecular dynamics trajectories available on the GPCRmd website. Despite the high sequence variability, shapes of the analyzed structures, defined by the backbone volume overlaps, can be clustered into seven main groups. Conformational differences within the clusters can be then identified by intramolecular interactions with other GPCR structural domains. Overall, our work provides a reorganization of the structural information of the ECL2 of class A GPCR subfamilies, highlighting differences and similarities on sequence and conformation levels.
Collapse
Affiliation(s)
- Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Andreas Dunkel
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Toni Giorgino
- Biophysics Institute, National Research Council (CNR-IBF), 20133 Milan, Italy
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG, U.K
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| |
Collapse
|
15
|
Felsztyna I, Villarreal MA, García DA, Miguel V. Insect RDL Receptor Models for Virtual Screening: Impact of the Template Conformational State in Pentameric Ligand-Gated Ion Channels. ACS OMEGA 2022; 7:1988-2001. [PMID: 35071887 PMCID: PMC8771969 DOI: 10.1021/acsomega.1c05465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
The RDL receptor is one of the most relevant protein targets for insecticide molecules. It belongs to the pentameric ligand-gated ion channel (pLGIC) family. Given that the experimental structures of pLGICs are difficult to obtain, homology modeling has been extensively used for these proteins, particularly for the RDL receptor. However, no detailed assessments of the usefulness of homology models for virtual screening (VS) have been carried out for pLGICs. The aim of this study was to evaluate which are the determinant factors for a good VS performance using RDL homology models, specially analyzing the impact of the template conformational state. Fifteen RDL homology models were obtained based on different pLGIC templates representing the closed, open, and desensitized states. A retrospective VS process was performed on each model, and their performance in the prioritization of active ligands was assessed. In addition, the three best-performing models among each of the conformations were subjected to molecular dynamics simulations (MDS) in complex with a representative active ligand. The models showed variations in their VS performance parameters that were related to the structural properties of the binding site. VS performance tended to improve in more constricted binding cavities. The best performance was obtained with a model based on a template in the closed conformation. MDS confirmed that the closed model was the one that best represented the interactions with an active ligand. These results imply that different templates should be evaluated and the structural variations between their channel conformational states should be specially examined, providing guidelines for the application of homology modeling for VS in other proteins of the pLGIC family.
Collapse
Affiliation(s)
- Iván Felsztyna
- Facultad
de Ciencias Exactas, Físicas y Naturales, Departamento de Química.
Cátedra de Química Biológica, Universidad Nacional de Córdoba, Córdoba 5016, Argentina
- Instituto
de Investigaciones Biológicas y Tecnológicas (IIByT), CONICET-Universidad Nacional de Córdoba, Córdoba 5016, Argentina
| | - Marcos A. Villarreal
- Facultad
de Ciencias Químicas, Departamento de Química Teórica
y Computacional, Universidad Nacional de
Córdoba, Córdoba 5016, Argentina
- Instituto
de Investigaciones en Físico-Química de Córdoba
(INFIQC), CONICET-Universidad Nacional de
Córdoba, Córdoba 5016, Argentina
| | - Daniel A. García
- Facultad
de Ciencias Exactas, Físicas y Naturales, Departamento de Química.
Cátedra de Química Biológica, Universidad Nacional de Córdoba, Córdoba 5016, Argentina
- Instituto
de Investigaciones Biológicas y Tecnológicas (IIByT), CONICET-Universidad Nacional de Córdoba, Córdoba 5016, Argentina
| | - Virginia Miguel
- Facultad
de Ciencias Exactas, Físicas y Naturales, Departamento de Química.
Cátedra de Química Biológica, Universidad Nacional de Córdoba, Córdoba 5016, Argentina
- Instituto
de Investigaciones Biológicas y Tecnológicas (IIByT), CONICET-Universidad Nacional de Córdoba, Córdoba 5016, Argentina
| |
Collapse
|
16
|
Krishna Deepak RNV, Verma RK, Hartono YD, Yew WS, Fan H. Recent Advances in Structure, Function, and Pharmacology of Class A Lipid GPCRs: Opportunities and Challenges for Drug Discovery. Pharmaceuticals (Basel) 2021; 15:12. [PMID: 35056070 PMCID: PMC8779880 DOI: 10.3390/ph15010012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/17/2021] [Accepted: 12/17/2021] [Indexed: 01/01/2023] Open
Abstract
Great progress has been made over the past decade in understanding the structural, functional, and pharmacological diversity of lipid GPCRs. From the first determination of the crystal structure of bovine rhodopsin in 2000, much progress has been made in the field of GPCR structural biology. The extraordinary progress in structural biology and pharmacology of GPCRs, coupled with rapid advances in computational approaches to study receptor dynamics and receptor-ligand interactions, has broadened our comprehension of the structural and functional facets of the receptor family members and has helped usher in a modern age of structure-based drug design and development. First, we provide a primer on lipid mediators and lipid GPCRs and their role in physiology and diseases as well as their value as drug targets. Second, we summarize the current advancements in the understanding of structural features of lipid GPCRs, such as the structural variation of their extracellular domains, diversity of their orthosteric and allosteric ligand binding sites, and molecular mechanisms of ligand binding. Third, we close by collating the emerging paradigms and opportunities in targeting lipid GPCRs, including a brief discussion on current strategies, challenges, and the future outlook.
Collapse
Affiliation(s)
- R. N. V. Krishna Deepak
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
| | - Ravi Kumar Verma
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
| | - Yossa Dwi Hartono
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Wen Shan Yew
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Hao Fan
- Bioinformatics Institute, A*STAR, 30 Biopolis Street, Matrix #07-01, Singapore 138671, Singapore; (R.K.V.); (Y.D.H.)
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore;
| |
Collapse
|
17
|
Overcoming Depression with 5-HT2A Receptor Ligands. Int J Mol Sci 2021; 23:ijms23010010. [PMID: 35008436 PMCID: PMC8744644 DOI: 10.3390/ijms23010010] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 01/25/2023] Open
Abstract
Depression is a multifactorial disorder that affects millions of people worldwide, and none of the currently available therapeutics can completely cure it. Thus, there is a need for developing novel, potent, and safer agents. Recent medicinal chemistry findings on the structure and function of the serotonin 2A (5-HT2A) receptor facilitated design and discovery of novel compounds with antidepressant action. Eligible papers highlighting the importance of 5-HT2A receptors in the pathomechanism of the disorder were identified in the content-screening performed on the popular databases (PubMed, Google Scholar). Articles were critically assessed based on their titles and abstracts. The most accurate papers were chosen to be read and presented in the manuscript. The review summarizes current knowledge on the applicability of 5-HT2A receptor signaling modulators in the treatment of depression. It provides an insight into the structural and physiological features of this receptor. Moreover, it presents an overview of recently conducted virtual screening campaigns aiming to identify novel, potent 5-HT2A receptor ligands and additional data on currently synthesized ligands acting through this protein.
Collapse
|
18
|
Ballante F, Kooistra AJ, Kampen S, de Graaf C, Carlsson J. Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You? Pharmacol Rev 2021; 73:527-565. [PMID: 34907092 DOI: 10.1124/pharmrev.120.000246] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.
Collapse
Affiliation(s)
- Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Albert J Kooistra
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Stefanie Kampen
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Chris de Graaf
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden (F.B., S.K., J.C.); Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (A.J.K.); and Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, United Kingdom (C.d.G.)
| |
Collapse
|
19
|
Cai T, Xie L, Chen M, Liu Y, He D, Zhang S, Mura C, Bourne PE, Xie L. Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology. RESEARCH SQUARE 2021:rs.3.rs-1109318. [PMID: 34873596 PMCID: PMC8647653 DOI: 10.21203/rs.3.rs-1109318/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones-a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology's sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry.
Collapse
Affiliation(s)
- Tian Cai
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA
| | - Li Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
| | - Muge Chen
- Master Program in Computer Science, Courant Institute of Mathematical Sciences, New York University
| | - Yang Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
| | - Di He
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA
| | - Shuo Zhang
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA
| | - Cameron Mura
- School of Data Science & Department of Biomedical Engineering, University of Virginia, Virginia, 22903, USA
| | - Philip E. Bourne
- School of Data Science & Department of Biomedical Engineering, University of Virginia, Virginia, 22903, USA
| | - Lei Xie
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016, USA
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065, USA
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, 10021, USA
| |
Collapse
|
20
|
Abstract
Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
Collapse
|
21
|
Barreto CAV, Baptista SJ, Preto AJ, Silvério D, Melo R, Moreira IS. Decoding Partner Specificity of Opioid Receptor Family. Front Mol Biosci 2021; 8:715215. [PMID: 34621786 PMCID: PMC8490921 DOI: 10.3389/fmolb.2021.715215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
This paper describes an exciting big data analysis compiled in a freely available database, which can be applied to characterize the coupling of different G-Protein coupled receptors (GPCRs) families with their intracellular partners. Opioid receptor (OR) family was used as case study in order to gain further insights into the physiological properties of these important drug targets, known to be associated with the opioid crisis, a huge socio-economic issue directly related to drug abuse. An extensive characterization of all members of the ORs family (μ (MOR), δ (DOR), κ (KOR), nociceptin (NOP)) and their corresponding binding partners (ARRs: Arr2, Arr3; G-protein: Gi1, Gi2, Gi3, Go, Gob, Gz, Gq, G11, G14, G15, G12, Gssh, Gslo) was carried out. A multi-step approach including models' construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity.
Collapse
Affiliation(s)
- Carlos A. V. Barreto
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - Salete J. Baptista
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
- Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, University of Coimbra, Coimbra, Portugal
| | - António J. Preto
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - Daniel Silvério
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
| | - Rita Melo
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, Cantanhede, Portugal
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal
| | - Irina S. Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| |
Collapse
|
22
|
Bhunia SS, Saxena AK. Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors. Curr Top Med Chem 2021; 21:269-294. [PMID: 32901584 DOI: 10.2174/1568026620666200908165250] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/20/2020] [Accepted: 09/01/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery. OBJECTIVE The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs. METHODS The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures. RESULTS The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures. CONCLUSION The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.
Collapse
Affiliation(s)
- Shome S Bhunia
- Global Institute of Pharmaceutical Education and Research, Kashipur, Uttarakhand, India
| | - Anil K Saxena
- Division of Medicinal and Process Chemistry, CSIR-CDRI, Lucknow 226031, India
| |
Collapse
|
23
|
Mejia-Gutierrez M, Vásquez-Paz BD, Fierro L, Maza JR. In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis. ACS OMEGA 2021; 6:14748-14764. [PMID: 34151057 PMCID: PMC8209794 DOI: 10.1021/acsomega.0c05984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/30/2021] [Indexed: 05/17/2023]
Abstract
We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.
Collapse
Affiliation(s)
- Melissa Mejia-Gutierrez
- Faculty
of Natural and Exact Sciences, Department of Chemistry, and School
of Basic Sciences, Department of Physiological Sciences, Faculty of
Health, Laboratory and Research group - Pharmacology Univalle Group, Universidad del Valle, 25360 Cali, Colombia
| | - Bryan D. Vásquez-Paz
- Faculty
of Natural and Exact Sciences, Department of Chemistry, Laboratory
and Research group - Pharmacology Univalle Group, Universidad del Valle, 25360 Cali, Colombia
| | - Leonardo Fierro
- Faculty
of Health, School of Basic Sciences, Department of Physiological Sciencesh,
Laboratory and Research group - Pharmacology Univalle Group, Universidad del Valle, 25360 Cali, Colombia
| | - Julio R. Maza
- Faculty
of Basic Sciences, Department of Chemistry, Laboratory and Research
group - Organic Chemistry and Biomedical Group, Universidad del Atlántico, 081001 Puerto Colombia, Colombia
| |
Collapse
|
24
|
Radioligand and computational insight in structure - Activity relationship of saccharin derivatives being ipsapirone and revospirone analogues. Bioorg Med Chem Lett 2021; 42:128028. [PMID: 33839253 DOI: 10.1016/j.bmcl.2021.128028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/25/2021] [Accepted: 04/04/2021] [Indexed: 11/24/2022]
Abstract
Schizophrenia and depression are diseases that significantly impede human functioning in society. Current antidepressant drugs are not fully effective. According to literature data, the effect on D2R or 5-HT1AR can effectively reduce the symptoms of depression or schizophrenia. Recent research hypothetized that the synergism of both of these receptors can improve the effectiveness of therapy. Ipsapirone, a representative of long-chain arylpiperazines, is a known 5-HT1AR ligand that has antidepressant effect. This compound has no affinity for the D2R. Bearing in mind, we decided to design ligands with improved affinity to D2R and confirmed that in some cases elongation of the carbon linker or arylpiperazine exchange may have beneficial influence on the binding to D2R and 5-HT1AR. Four groups of ligands being ipsapirone analogues with butyl, pentyl, hexyl and stiffened xylene chains were designed. All compounds were obtained in solvent-free reactions supported by a microwave irradiation with an efficiency mainly above 60%. All ligands containing 1-(2-pyrimidinyl)piperazine exhibited high affinity to 5-HT1AR. In this case, chemical modifications within the chain did not affect the affinity to D2R. In the case of ligands containing 1-phenylpiperazine, 1-(3-trifluoromethylphenyl)piperazine, 1-(1-naphthyl)piperazine, and 1-(4-chlorophenyl)piperazine, elongation of carbon linker increases of affinity to D2R. For ligands containing 1- (2-pyridyl) piperazine, and 1-(2,3-dichlorophenyl)piperazine, we observed an opposite effect. For ligands containing 1-phenylpiperazine, 1-(2-methoxyphenyl)piperazine and 1-(2-pyridyl)piperazine, chain elongation had no effect on 5-HT1AR binding. In turn of ligands containing 1-(3-trifluoromethylphenyl)piperazine and 1- (2,3-dichlorophenyl)piperazine, we observed that elongation of carbon linker has a positive influence to 5-HT1AR. Molecular modelling was used to support the SAR study.
Collapse
|
25
|
Kapla J, Rodríguez-Espigares I, Ballante F, Selent J, Carlsson J. Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? PLoS Comput Biol 2021; 17:e1008936. [PMID: 33983933 PMCID: PMC8186765 DOI: 10.1371/journal.pcbi.1008936] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 06/08/2021] [Accepted: 04/02/2021] [Indexed: 01/14/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Jon Kapla
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| |
Collapse
|
26
|
Jabeen A, Vijayram R, Ranganathan S. BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling. Front Mol Biosci 2021; 8:617176. [PMID: 33898512 PMCID: PMC8059640 DOI: 10.3389/fmolb.2021.617176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/24/2021] [Indexed: 11/13/2022] Open
Abstract
G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.
Collapse
Affiliation(s)
- Amara Jabeen
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Ramya Vijayram
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| |
Collapse
|
27
|
Aderibigbe AO, Pandey P, Doerksen RJ. Negative allosteric modulators of cannabinoid receptor 1: Ternary complexes including CB1, orthosteric CP55940 and allosteric ORG27569. J Biomol Struct Dyn 2021; 40:5729-5747. [PMID: 33480332 DOI: 10.1080/07391102.2021.1873187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In October 2019, the first X-ray crystal structure of a ternary cannabinoid receptor 1 (CB1) complex (PDB ID: 6KQI) was published, including the well-known orthosteric agonist, CP55940, and the well-studied negative allosteric modulator, ORG27569. Prior to the release of 6KQI, we applied binding pocket analysis and molecular docking to carefully prepared computational models of the ternary CB1 complex, in order to predict the binding site for ORG27569 with the CP55940-bound CB1 receptor. We carefully studied the binding pose of agonist ligands in the CB1 orthosteric pocket, including CP55940. Our computational studies identified the most favorable binding site for ORG27569, in the CP55940-CB1 complex, to be at the intracellular end of the receptor. However, in the 6KQI structure, ORG27569 was found at an extrahelical, intramembrane site on the complex, a site that partially overlaps with the site predicted in our calculations to be second-best. We performed molecular dynamics simulations of the CP55940-bound CB1 complex with ORG27569 at different binding sites. Our analysis of the simulations indicated that ORG27569 bound favorably and stably in each simulation, but, as in the earlier calculations, bound best at the intracellular site, which is different than that found in the crystal structure. These results suggest that the intracellular site might serve as an alternative binding site in CB1. Our studies show that the computational techniques we used are valuable in identifying ligand-binding pockets in proteins, and could be useful for the study of the interaction mode of other allosteric modulators.
Collapse
Affiliation(s)
- AyoOluwa O Aderibigbe
- Department of BioMolecular Sciences, Division of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, USA
| | - Pankaj Pandey
- Department of BioMolecular Sciences, Division of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, USA.,National Center for Natural Products Research, University of Mississippi, University, Mississippi, USA
| | - Robert J Doerksen
- Department of BioMolecular Sciences, Division of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University, MS, USA.,Research Institute of Pharmaceutical Sciences, School of Pharmacy, University of Mississippi, University, MS, USA
| |
Collapse
|
28
|
A comprehensive application: Molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine. Comput Biol Chem 2020; 90:107402. [PMID: 33338839 DOI: 10.1016/j.compbiolchem.2020.107402] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 02/07/2023]
Abstract
Traditional Chinese medicine (TCM) has been used for more than 2000 years in China. TCM has received wide attention recently due to its unique charm. At the same time, its main obstacles have attracted wide attention, including vagueness of drug composition and treatment mechanism. With the development of virtual screening technology, more and more Chinese medicine compounds have been studied to discover the potential active components and mechanisms of action. Molecular docking is a computer technology based on structural design. Network pharmacology establishes powerful and comprehensive databases to understand the relationship between TCM and disease network. In this review, emergent uses and applications of two techniques and further superiorities of the two techniques when embarked to boil down into a tidy system were illustrated. A combination of the two provides a theoretical basis and technical support for the construction of modern TCM based on the compatibility of components and accelerates the realization of two basic elements as well, including the clearness of the pharmacodynamic substances and explanation of the effect of TCM.
Collapse
|
29
|
Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation. Sci Rep 2020; 10:19942. [PMID: 33203907 PMCID: PMC7672096 DOI: 10.1038/s41598-020-77072-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 11/05/2020] [Indexed: 02/07/2023] Open
Abstract
Molecular dynamics (MD) is the common computational technique for assessing efficacy of GPCR-bound ligands. Agonist efficacy measures the capability of the ligand-bound receptor of reaching the active state in comparison with the free receptor. In this respect, agonists, neutral antagonists and inverse agonists can be considered. A collection of MD simulations of both the ligand-bound and the free receptor are needed to provide reliable conclusions. Variability in the trajectories needs quantification and proper statistical tools for meaningful and non-subjective conclusions. Multiple-factor (time, ligand, lipid) ANOVA with repeated measurements on the time factor is proposed as a suitable statistical method for the analysis of agonist-dependent GPCR activation MD simulations. Inclusion of time factor in the ANOVA model is consistent with the time-dependent nature of MD. Ligand and lipid factors measure agonist and lipid influence on receptor activation. Previously reported MD simulations of adenosine A2a receptor (A2aR) are reanalyzed with this statistical method. TM6–TM3 and TM7–TM3 distances are selected as dependent variables in the ANOVA model. The ligand factor includes the presence or absence of adenosine whereas the lipid factor considers DOPC or DOPG lipids. Statistical analysis of MD simulations shows the efficacy of adenosine and the effect of the membrane lipid composition. Subsequent application of the statistical methodology to NECA A2aR agonist, with resulting P values in consistency with its pharmacological profile, suggests that the method is useful for ligand comparison and potentially for dynamic structure-based virtual screening.
Collapse
|
30
|
Odoemelam CS, Percival B, Wallis H, Chang MW, Ahmad Z, Scholey D, Burton E, Williams IH, Kamerlin CL, Wilson PB. G-Protein coupled receptors: structure and function in drug discovery. RSC Adv 2020; 10:36337-36348. [PMID: 35517958 PMCID: PMC9057076 DOI: 10.1039/d0ra08003a] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/22/2020] [Indexed: 12/13/2022] Open
Abstract
The G-protein coupled receptors (GPCRs) superfamily comprise similar proteins arranged into families or classes thus making it one of the largest in the mammalian genome. GPCRs take part in many vital physiological functions making them targets for numerous novel drugs. GPCRs share some distinctive features, such as the seven transmembrane domains, they also differ in the number of conserved residues in their transmembrane domain. Here we provide an introductory and accessible review detailing the computational advances in GPCR pharmacology and drug discovery. An overview is provided on family A-C GPCRs; their structural differences, GPCR signalling, allosteric binding and cooperativity. The dielectric constant (relative permittivity) of proteins is also discussed in the context of site-specific environmental effects.
Collapse
Affiliation(s)
| | - Benita Percival
- Nottingham Trent University 50 Shakespeare St Nottingham NG1 4FQ UK
| | - Helen Wallis
- Nottingham Trent University 50 Shakespeare St Nottingham NG1 4FQ UK
| | - Ming-Wei Chang
- Nanotechnology and Integrated Bioengineering Centre, University of Ulster Jordanstown Campus Newtownabbey BT37 0QB Northern Ireland UK
| | - Zeeshan Ahmad
- De Montfort University The Gateway Leicester LE1 9BH UK
| | - Dawn Scholey
- Nottingham Trent University 50 Shakespeare St Nottingham NG1 4FQ UK
| | - Emily Burton
- Nottingham Trent University 50 Shakespeare St Nottingham NG1 4FQ UK
| | - Ian H Williams
- Department of Chemistry, University of Bath Claverton Down Bath BA1 7AY UK
| | | | | |
Collapse
|
31
|
Torrens-Fontanals M, Stepniewski TM, Aranda-García D, Morales-Pastor A, Medel-Lacruz B, Selent J. How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs. Int J Mol Sci 2020; 21:E5933. [PMID: 32824756 PMCID: PMC7460635 DOI: 10.3390/ijms21165933] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/11/2020] [Accepted: 08/15/2020] [Indexed: 01/08/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.
Collapse
Affiliation(s)
- Mariona Torrens-Fontanals
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Tomasz Maciej Stepniewski
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
- InterAx Biotech AG, PARK innovAARE, 5234 Villigen, Switzerland
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, 02-093 Warsaw, Poland
| | - David Aranda-García
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Adrián Morales-Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Brian Medel-Lacruz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM)—Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain; (M.T.-F.); (T.M.S.); (D.A.-G.); (A.M.-P.); (B.M.-L.)
| |
Collapse
|
32
|
Schneider J, Korshunova K, Si Chaib Z, Giorgetti A, Alfonso-Prieto M, Carloni P. Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach. J Chem Inf Model 2020; 60:5103-5116. [PMID: 32786708 DOI: 10.1021/acs.jcim.0c00661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.
Collapse
Affiliation(s)
- Jakob Schneider
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Ksenia Korshunova
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany
| | - Zeineb Si Chaib
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,RWTH Aachen University, 52062 Aachen, Germany
| | - Alejandro Giorgetti
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Biotechnology, University of Verona, 37314 Verona, Italy.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Cecile and Oskar Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Paolo Carloni
- Computational Biomedicine, Institute for Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,Department of Physics, RWTH Aachen University, 52074 Aachen, Germany.,JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.,JARA-HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| |
Collapse
|