The Predicted Ensemble of Low‐Energy Conformations of Human Somatostatin Receptor Subtype 5 and the Binding of Antagonists

Dong, Sijia S.; Abrol, Ravinder; Goddard, William A.

doi:10.1002/cmdc.201500023

Cited by 6 publications

(10 citation statements)

References 82 publications

(87 reference statements)

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“…Using the predicted binding sites for a series of five known antagonists, they predicted binding energies consistent with experimental results reported in the literature for Human somatostatin receptor subtype 5 (hSSTR5). 418…”

Section: Protein Interactions and Reactions Under Ensemble Controlmentioning

confidence: 99%

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

et al. 2016

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All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

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Section: Protein Interactions and Reactions Under Ensemble Controlmentioning

confidence: 99%

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

et al. 2016

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“…36 GenDock has been shown to be able to predict protein-ligand binding sites and energy consistent with experimental findings for a number of cases including G protein-coupled receptors 34,37 and cyclindependent kinase 5. 36 GenDock has been shown to be able to predict protein-ligand binding sites and energy consistent with experimental findings for a number of cases including G protein-coupled receptors 34,37 and cyclindependent kinase 5.…”

Section: Molecular Dockingmentioning

confidence: 58%

Computational predictions of corroles as a class of Hsp90 inhibitors

et al. 2015

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Corroles have been shown experimentally to cause cell cycle arrest, and there is some evidence that this might be attributed to an inhibitory effect of corroles on Heat shock protein 90 (Hsp90), which is known to play a vital role in cancer cell proliferation. In this study, we used molecular dynamics to examine the interaction of gallium corroles with Hsp90, and found that they can bind preferentially to the ATP-binding N-terminal site. We also found that structural variations of the corrole ring can influence the binding energies and affinities of the corrole to Hsp90. We predict that both the bis-carboxylated corrole (4-Ga) and a proposed 3,17-bis-sulfonated corrole (7-Ga) are promising alternatives to Ga(III) 5,10,15-tris(pentafluorophenyl)-2,17-bis(sulfonic acid)-corrole (1-Ga) as anti-cancer agents.

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“…50 and Ga i 's C351 G.H5. 23 replace the inactive state's R151-D136 3.49 -R137 3.50 -T247 6.34 network. An additional saltbridge network is formed between the carboxylate group on the Ga i C-terminal residue F354 G.H5.26 and hSSTR5's K245 6.32 on TM6 and R239 on ICL3.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…We applied the ActiveGEnSeM-BLE method to the hSSTR5 receptor, for which no experimental structures are available. The exact procedures of steps 1-3 follow the ActiveGEnSeMBLE protocol mentioned above and are described in detail in our previous SSTR5 publication (23). The ligands (L-817,818 and F21) were docked to each of the five predicted hSSTR5 structures (InactiveConf1,2,3 and ActiveConf1,2) as described in Supporting Materials and Methods.…”

Section: Applicationmentioning

confidence: 99%

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Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

Dong

Goddard

Abrol

2016

Biophysical Journal

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We present a hybrid computational methodology to predict multiple energetically accessible conformations for G protein-coupled receptors (GPCRs) that might play a role in binding to ligands and different signaling partners. To our knowledge, this method, termed ActiveGEnSeMBLE, enables the first quantitative energy profile for GPCR activation that is consistent with the qualitative profile deduced from experiments. ActiveGEnSeMBLE starts with a systematic coarse grid sampling of helix tilts/rotations (∼13 trillion transmembrane-domain conformations) and selects the conformational landscape based on energy. This profile identifies multiple potential active-state energy wells, with the TM3-TM6 intracellular distance as an approximate activation coordinate. These energy wells are then sampled locally using a finer grid to find locally minimized conformation in each energy well. We validate this strategy using the inactive and active experimental structures of β2 adrenergic receptor (hβ2AR) and M2 muscarinic acetylcholine receptor. Structures of membrane-embedded hβ2AR along its activation coordinate are subjected to molecular-dynamics simulations for relaxation and interaction energy analysis to generate a quantitative energy landscape for hβ2AR activation. This landscape reveals several metastable states along this coordinate, indicating that for hβ2AR, the agonist alone is not enough to stabilize the active state and that the G protein is necessary, consistent with experimental observations. The method's application to somatostatin receptor SSTR5 (no experimental structure available) shows that to predict an active conformation it is better to start from an inactive structure template based on a close homolog than to start from an active template based on a distant homolog. The energy landscape for hSSTR5 activation is consistent with hβ2AR in the role of the G protein. These results demonstrate the utility of the ActiveGEnSeMBLE method for predicting multiple conformations along the pathways for activating GPCRs and the corresponding energy landscapes, thereby providing detailed structural insights into the initial molecular events of GPCR function that are not easily accessible by experiments.

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The Predicted Ensemble of Low‐Energy Conformations of Human Somatostatin Receptor Subtype 5 and the Binding of Antagonists

Cited by 6 publications

References 82 publications

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

Computational predictions of corroles as a class of Hsp90 inhibitors

Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

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