Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol

Plante, Ambrose; Weinstein, Harel

doi:10.3390/molecules26103059

Cited by 15 publications

(34 citation statements)

References 49 publications

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“…Furthermore, a long-standing challenge in MD has been to observe rare events that are related to the functional response of a GPCR. Plante et al used unsupervised machine learning to detect such rare events in the dynamic contact network of the 5-HT 2A receptor [ 37 ], comparing the functional impact of its native agonist, 5-HT, and the selective inverse agonist ketanserin. Their analysis method consisted of applying non-negative matrix factorization to the matrix representation of the dynamic contact network.…”

Section: Enhanced Description Of Allosteric Network Using Network Theorymentioning

confidence: 99%

“…This process highlights key events that take place over the simulation while ignoring random noise in the contact network variations. For instance, the authors find that the serotonin-bound 5-HT 2A receptor undergoes a structural rearrangement in the ICL2, which induces an increase in the volume of the intracellular cavity—an activation-related event that is observed in crystal structures when bound to the heterotrimeric G protein [ 37 ].…”

Section: Enhanced Description Of Allosteric Network Using Network Theorymentioning

confidence: 99%

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In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias

Morales-Pastor¹,

Nerín-Fonz²,

Aranda-García³

et al. 2022

IJMS

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

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Section: Enhanced Description Of Allosteric Network Using Network Theorymentioning

confidence: 99%

Section: Enhanced Description Of Allosteric Network Using Network Theorymentioning

confidence: 99%

In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias

Morales-Pastor¹,

Nerín-Fonz²,

Aranda-García³

et al. 2022

IJMS

View full text Add to dashboard Cite

show abstract

“…They also developed a rare event detection (RED) protocol to investigate ligand-dependent conformational transitions in cMD simulations of the serotonin 5-HT2AR receptor. 79 RED was able to identify rare transition events in residue contacts of the GPCR in response to binding of the agonist and inverse agonist. Another ML approach was developed to cluster MD simulations of the dopamine D3 receptor (D3R) for rationalizing the efficacy change induced by binding of four orthosteric ligands.…”

Section: ■ Introductionmentioning

confidence: 99%

“…The X , Y , and Z coordinates of an atom in each simulation frame were converted to the red, green, and blue color values of a pixel in an image. They also developed a rare event detection (RED) protocol to investigate ligand-dependent conformational transitions in cMD simulations of the serotonin 5-HT2AR receptor . RED was able to identify rare transition events in residue contacts of the GPCR in response to binding of the agonist and inverse agonist.…”

Section: Introductionmentioning

confidence: 99%

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

Hung

Wang

Bhattarai

et al. 2022

J. Chem. Theory Comput.

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We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A 1 receptor (A 1 AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A 1 AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.

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“…Our key mechanistic finding of the connection between the movement of CHL among its binding modes in the hydrophobic pocket, and the movements of specific structural motifs of StarD4, emerged from the analysis of the very long MD trajectories with the time-resolved rare event detection algorithm RED (31). These trajectory analyses showed that the connected conformational changes at the β9 C-terminal and H4 N-terminal portions result in the opening of a gate between H4 and Ω1-loop, and a corridor between β9 and β7β8-loop.…”

Section: Discussionmentioning

confidence: 99%

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Xie

Weinstein

2023

Preprint

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Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

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Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol

Cited by 15 publications

References 49 publications

In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias

In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

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