Application of computational methods for class A GPCR Ligand discovery

被引:4
作者
Szwabowski, Gregory L. [1 ]
Baker, Daniel L. [1 ]
Parrill, Abby L. [1 ]
机构
[1] Univ Memphis, Dept Chem, Memphis, TN 38152 USA
关键词
Computer -aided drug design; Docking; Fragment -based drug design; Homology modeling; GPCR; Ligand discovery; Loop modeling; Pharmacophore modeling; Similarity searching; Virtual screening; PROTEIN-STRUCTURE PREDICTION; FINGERPRINT SIMILARITY SEARCH; EMPIRICAL SCORING FUNCTIONS; 2ND EXTRACELLULAR LOOP; DE-NOVO DESIGN; PHARMACOPHORE MODELS; DRUG DISCOVERY; COUPLED RECEPTORS; BINDING-AFFINITY; CHEMICAL SIMILARITY;
D O I
10.1016/j.jmgm.2023.108434
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
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页数:16
相关论文
共 169 条
[1]   Machine learning classification can reduce false positives in structure-based virtual screening [J].
Adeshina, Yusuf O. ;
Deeds, Eric J. ;
Karanicolas, John .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (31) :18477-18488
[2]  
[Anonymous], 2010, Drug Discov Today Technol, V7, pe203, DOI 10.1016/j.ddtec.2010.11.004
[3]  
[Anonymous], 2010, Drug Discov Today Technol, V7, pe203, DOI 10.1016/j.ddtec.2010.10.003
[4]   New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays [J].
Baell, Jonathan B. ;
Holloway, Georgina A. .
JOURNAL OF MEDICINAL CHEMISTRY, 2010, 53 (07) :2719-2740
[5]   Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? [J].
Bajusz, David ;
Racz, Anita ;
Heberger, Kroly .
JOURNAL OF CHEMINFORMATICS, 2015, 7
[6]   Protein structure prediction and structural genomics [J].
Baker, D ;
Sali, A .
SCIENCE, 2001, 294 (5540) :93-96
[7]   Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You? [J].
Ballante, Flavio ;
Kooistra, Albert J. ;
Kampen, Stefanie ;
de Graaf, Chris ;
Carlsson, Jens .
PHARMACOLOGICAL REVIEWS, 2021, 73 (04) :527-565
[8]   A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking [J].
Ballester, Pedro J. ;
Mitchell, John B. O. .
BIOINFORMATICS, 2010, 26 (09) :1169-1175
[9]   Synthesis, biological evaluation, and pharmacophore generation of new pyridazinone derivatives with affinity toward α1- and α2-adrenoceptors [J].
Barbaro, R ;
Betti, L ;
Botta, M ;
Corelli, F ;
Giannaccini, G ;
Maccari, L ;
Manetti, F ;
Strappaghetti, G ;
Corsano, S .
JOURNAL OF MEDICINAL CHEMISTRY, 2001, 44 (13) :2118-2132
[10]   Current approaches to flexible loop modeling [J].
Barozet, Amelie ;
Chacon, Pablo ;
Cortes, Juan .
CURRENT RESEARCH IN STRUCTURAL BIOLOGY, 2021, 3 :187-191