Conformation Transition of Intracellular Part of Glucagon Receptor in Complex With Agonist Glucagon by Conventional and Accelerated Molecular Dynamics Simulations

Bai, Qifeng; Tan, Shuoyan; Pérez‐Sánchez, Horacio; Feng, Hui; Feng, Liya; Liu, Huanxiang; Yao, Xiaojun

doi:10.3389/fchem.2019.00851

Cited by 6 publications

(9 citation statements)

References 30 publications

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“…In order to demonstrate the 3D ligand design by MolAICal, the membrane target glucagon receptor (GCGR) and non-membrane target SARS-CoV-2 main protease (M pro ) are chosen as the drug design targets (see Figure 5 ). The GCGR [ 61 , 62 ] is a potential target of type 2 diabetes which is a member of the class B family of G protein-coupled receptors. The SARS-CoV-2 M pro plays an important role in processing the polyproteins of viral RNA which is a potential drug target of coronavirus disease 2019 (COVID-19) [ 63 , 64 ].…”

Section: Resultsmentioning

confidence: 99%

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Bai

Tan

et al. 2020

Briefings in Bioinformatics

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177

127

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Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

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Section: Resultsmentioning

confidence: 99%

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Bai

Tan

et al. 2020

Briefings in Bioinformatics

Self Cite

177

127

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“…Previously, the inactive and active conformations of GCGR have been mapped onto a conformational energy landscape described by the root-mean-squared deviation (RMSD) of the entire receptor and the distance between Cα atoms of L253 3.58b on TM3 and Y343 6.34b on TM6 . The distance between TM3 and TM6 act as an indicator in intracellular activation, since TM6 kinks outward upon activation around the conserved Pro 6.47 -X-X-Gly 6.50 motif intracellularly, along with reorganization of TM5 in order to make room for the G-protein to bind. , The mean distance between residue L253 3.58b on TM3 and Y343 6.34b on TM6 for the inactive conformation was found to lie around 11 Å, while the mean distance for the active conformation lies around 14 Å . The corresponding energy landscapes for the three PI(4,5)P 2 systems and the reference system for the current study are depicted in Figure b.…”

Section: Resultsmentioning

confidence: 99%

“…The corresponding ensemble of the receptor structures were mapped into a 2D free energy surface, calculated as where the coordinates x and y represent the distances between Cα atoms of residues 253 and 343 and the RMSD of all Cα atoms in GCGR, respectively …”

Section: Methodsmentioning

confidence: 99%

Lipid Modulation of a Class B GPCR: Elucidating the Modulatory Role of PI(4,5)P₂ Lipids

Kjølbye

Sørensen

Yan

et al. 2022

J. Chem. Inf. Model.

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Phosphatidylinositol 4,5-bisphosphate (PI(4,5)P 2 ) lipids have been shown to stabilize an active conformation of class A G-protein coupled receptors (GPCRs) through a conserved binding site, not present in class B GPCRs. For class B GPCRs, previous molecular dynamics (MD) simulation studies have shown PI(4,5)P 2 interacting with the Glucagon receptor (GCGR), which constitutes an important target for diabetes and obesity therapeutics. In this work, we applied MD simulations supported by native mass spectrometry (nMS) to study lipid interactions with GCGR. We demonstrate how tail composition plays a role in modulating the binding of PI(4,5)P 2 lipids to GCGR. Specifically, we find the PI(4,5)P 2 lipids to have a higher affinity toward the inactive conformation of GCGR. Interestingly, we find that in contrast to class A GPCRs, PI(4,5)P 2 appear to stabilize the inactive conformation of GCGR through a binding site conserved across class B GPCRs but absent in class A GPCRs. This suggests differences in the regulatory function of PI(4,5)P 2 between class A and class B GPCRs.

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“…to probe the impacts of metal ions on the conformational changes of the preQ1 riboswitch, and their results showcase that Na + ions have stronger competitiveness with Mg 2+ ions than with K + ions . Meanwhile, to avoid the possibility of conformations sampled by conventional MD (cMD) simulations falling into a locally minimal space, aMD and Gaussian aMD (GaMD) simulations have been proposed to enhance the conformational sampling of targets. , Encouragingly, these two simulation technologies have shown great success in deciphering the conformational changes of proteins and RNAs, small molecule identification, and drug resistance, etc. − The aforementioned different simulation technologies provide new approaches for studying the conformational changes of SAM-III induced by ligand binding.…”

Section: Introductionmentioning

confidence: 99%

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

Chen

Zeng

Wang

et al. 2022

J. Chem. Inf. Model.

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S-Adenosyl-L-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S MK ) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand−nucleotide interactions not only uncovers that the π−π, cation−π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

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Conformation Transition of Intracellular Part of Glucagon Receptor in Complex With Agonist Glucagon by Conventional and Accelerated Molecular Dynamics Simulations

Cited by 6 publications

References 30 publications

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Lipid Modulation of a Class B GPCR: Elucidating the Modulatory Role of PI(4,5)P₂ Lipids

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

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Conformation Transition of Intracellular Part of Glucagon Receptor in Complex With Agonist Glucagon by Conventional and Accelerated Molecular Dynamics Simulations

Cited by 6 publications

References 30 publications

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Lipid Modulation of a Class B GPCR: Elucidating the Modulatory Role of PI(4,5)P2 Lipids

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

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Lipid Modulation of a Class B GPCR: Elucidating the Modulatory Role of PI(4,5)P₂ Lipids