Continuous constant pH Molecular Dynamics Simulations of Transmembrane Proteins

Huang, Yandong; Henderson, Jack A.; Shen, Jana

doi:10.1101/2020.08.06.239772

Cited by 4 publications

(4 citation statements)

References 83 publications

(122 reference statements)

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“…The membrane-enabled hybrid-solvent CpHMD method 22,23 has been validated for pK a calculations and pH-dependent simulations of transmembrane proteins 23,25,26 . The detailed protocols can be found here 24 .…”

Section: Methodsmentioning

confidence: 99%

“…Towards understanding the molecular mechanism of mOR activation by fentanyl, here we elucidate the detailed fentanyl-mOR binding mechanism by exploiting a morphinanbound mOR crystal structure and several molecular dynamics (MD) methods, including the weighted ensemble (WE) approach [19][20][21] for enhanced path sampling and membraneenabled continuous constant-pH MD (CpHMD) with replicaexchange [22][23][24] . The latter method has been previously applied to calculate pK a s and describe proton-coupled conformational dynamics of membrane channels 25 and transporters 23,26,27 .…”

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confidence: 99%

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How μ-opioid receptor recognizes fentanyl

et al. 2021

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Roughly half of the drug overdose-related deaths in the United States are related to synthetic opioids represented by fentanyl which is a potent agonist of mu-opioid receptor (mOR). In recent years, X-ray crystal structures of mOR in complex with morphine derivatives have been determined; however, structural basis of mOR activation by fentanyl-like opioids remains lacking. Exploiting the X-ray structure of BU72-bound mOR and several molecular simulation techniques, we elucidated the detailed binding mechanism of fentanyl. Surprisingly, in addition to the salt-bridge binding mode common to morphinan opiates, fentanyl can move deeper and form a stable hydrogen bond with the conserved His2976.52, which has been suggested to modulate mOR’s ligand affinity and pH dependence by previous mutagenesis experiments. Intriguingly, this secondary binding mode is only accessible when His2976.52 adopts a neutral HID tautomer. Alternative binding modes may represent a general mechanism in G protein-coupled receptor-ligand recognition.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

How μ-opioid receptor recognizes fentanyl

et al. 2021

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“…Towards understanding the molecular mechanism of mOR activation by fentanyl, here we elucidate the detailed fentanyl-mOR binding mechanism by exploiting a morphinan-bound mOR crystal structure and several state-of-the-art molecular dynamics (MD) methods, including the weighted ensemble (WE) approach 19–21 for enhanced path sampling and membrane-enabled continuous constant-pH MD (CpHMD) with replica-exchange. 22–24 The latter method has been previously applied to calculate p K a ’s and describe protoncoupled conformational dynamics of membrane channels 25 and transporters. 23,26,27 Surprisingly, WE path sampling found that when His297 adopts the HID tautomer, fentanyl can move deeper into the mOR and establish an alternative binding mode through hydrogen bonding with His297.…”

Section: Introductionmentioning

confidence: 99%

Howµ-Opioid Receptor Recognizes Fentanyl

Mahinthichaichan

Shen

et al. 2020

Preprint

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The opioid crisis has escalated during the COVID-19 pandemic. More than half of the overdose-related deaths are related to synthetic opioids represented by fentanyl which is a potent agonist of mu-opioid receptor (mOR). In recent years, crystal structures of mOR complexed with morphine derivatives have been determined; however, structural basis of mOR activation by fentanyl-like synthetic opioids remains lacking. Exploiting the X-ray structure of mOR bound to a morphinan ligand and several state-of-the-art simulation techniques, including weighted ensemble and continuous constant pH molecular dynamics, we elucidated the detailed binding mechanism of fentanyl with mOR. Surprisingly, in addition to the orthosteric site common to morphinan opiates, fentanyl can move deeper and bind mOR through hydrogen bonding with a conserved histidine H297, which has been shown to modulate mOR’s ligand affinity and pH dependence in mutagenesis experiments, but its precise role remains unclear. Intriguingly, the secondary binding mode is only accessible when H297 adopts a neutral HID tautomer. Alternative binding modes and involvement of tautomer states may represent general mechanisms in G protein-coupled receptor (GPCR)-ligand recognition. Our work provides a starting point for understanding mOR activation by fentanyl analogs that are emerging at a rapid pace and assisting the design of safer analgesics to combat the opioid crisis. Current protein simulation studies employ standard protonation and tautomer states; our work demonstrates the need to move beyond the practice to advance our understanding of proteinligand recognition.

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“…This category includes the use of all-atom, constant-pH molecular dynamics (cpHMD) simulations and quantum mechanical (QM) methods. In cpHMD, virtual titrations of multiple protein side chains are performed simultaneously. − This method, like free energy perturbation (FEP) and thermodynamic integration (TI), uses a λ-coupling constant to alchemically drive changes in protonation states. However, in cpHMD, λ is influenced by the environmental energetics.…”

Section: Methodsmentioning

confidence: 99%

Comparative Performance of High-Throughput Methods for Protein pK_a Predictions

Wei,

Hogues,

Sulea

2023

J. Chem. Inf. Model.

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The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pK a prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pK a shifts from the pK a database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pK a units and a correlation coefficient (R 2) of 0.45 to experimental pK a shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pK a prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

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Continuous constant pH Molecular Dynamics Simulations of Transmembrane Proteins

Cited by 4 publications

References 83 publications

How μ-opioid receptor recognizes fentanyl

How μ-opioid receptor recognizes fentanyl

Howµ-Opioid Receptor Recognizes Fentanyl

Comparative Performance of High-Throughput Methods for Protein pK_a Predictions

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Continuous constant pH Molecular Dynamics Simulations of Transmembrane Proteins

Cited by 4 publications

References 83 publications

How μ-opioid receptor recognizes fentanyl

How μ-opioid receptor recognizes fentanyl

Howµ-Opioid Receptor Recognizes Fentanyl

Comparative Performance of High-Throughput Methods for Protein pKa Predictions

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Comparative Performance of High-Throughput Methods for Protein pK_a Predictions