Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

Chudyk, Ewa I.; Sarrat, Laurie; Aldeghi, Matteo; Fedorov, Dmitri G.; Bodkin, Michael J.; James, Tim; Southey, Michelle; Robinson, Roger; Morao, Iñaki; Heifetz, Alexander

doi:10.1007/978-1-4939-7465-8_8

Cited by 17 publications

(23 citation statements)

References 56 publications

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“…FMO analysis can result in two considerable benefits: (a) complex QM theories are condensed into four simple and intuitively clear quantities, and (b) calculations become much faster than traditional QM approaches. This information can be used to understand the chemical nature of existing receptor-ligand complexes, which in turn can be used to guide mutagenesis experiments or to help optimize ligands in ways that were previously not considered [43,47]. There are increasing effort to extend the application of FMO to structural optimization, protein-protein interactions, molecular dynamics simulations [52] and many others.…”

Section: Discussionmentioning

confidence: 99%

“…To illustrate the typical results obtained through FMO we performed calculations on the complex between the human β 2 -adrenergic receptor (β 2 AR) and the inverse agonist Carazolol (PDB entry 2RH1, Figure 2a), and between β 2 AR and the agonist BI-167107 (PDB entry 4LDE, Figure 2b). We consider any interaction with an absolute PIE greater than or equal to 3.0 kcal/mol to be significant [43]. A comparison of the interactions formed by these two ligands with β 2 AR is shown in Figure 2c.…”

Section: Fmo and Its Application To Gpcr Structure-based Drug Designmentioning

confidence: 99%

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Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach

Heifetz

James

Southey

et al. 2019

Current Opinion in Structural Biology

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There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptorligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum Mechanics approaches (QM) are often too computationally expensive, but the Fragment Molecular Orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.

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Section: Discussionmentioning

confidence: 99%

Section: Fmo and Its Application To Gpcr Structure-based Drug Designmentioning

confidence: 99%

Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach

Heifetz

James

Southey

et al. 2019

Current Opinion in Structural Biology

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“…It offers a considerable speed-up, since quantum mechanics approaches are often too computationally expensive, and it also has the potential to explore key interactions and selectivity that would otherwise be hard to detect. 98,99 For example, Bodkin et al. described how FMO has been applied to the analysis of 18 GPCR-ligand crystal structures representing different branches of the GPCR genome.…”

Section: Ligand-based Rational Designmentioning

confidence: 99%

Trends in application of advancing computational approaches in GPCR ligand discovery

Zhu

Huang

et al. 2021

Exp Biol Med (Maywood)

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

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“…The understanding of binding interactions between any proteins or membrane receptors and their small ligands plays a key role in the rationalization of affinity and selectivity of the ligands [ 98 ]. In this respect, the fragment molecular orbital method (FMO) can compute very large molecular systems with thousands of atoms using ab initio quantum-chemical wave functions.…”

Section: Brief Overview Of Bioinformatics and Cheminformatics Tools Amentioning

confidence: 99%

“…In this respect, the fragment molecular orbital method (FMO) can compute very large molecular systems with thousands of atoms using ab initio quantum-chemical wave functions. The critical advantage of FMO is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding, which would otherwise be difficult to detect without the usage of quantum mechanics approaches [ 98 ].…”

Section: Brief Overview Of Bioinformatics and Cheminformatics Tools Amentioning

confidence: 99%

Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

Avram

Mernea

Limban

et al. 2020

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Background: Alzheimer’s disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects. Methods: Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structure-biological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine. Results: In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage is to be determined. Conclusion: Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

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Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

Cited by 17 publications

References 56 publications

Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach

Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach

Trends in application of advancing computational approaches in GPCR ligand discovery

Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

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