AA1 – Life Sciences

Project

AA1-1

The spatio-temporal modelling of mechanisms underlying pain relief via the µ-opioid receptor

Project Heads

Martin Lohse, Marcus Weber, Christof Schütte, Christoph Stein

Project Members

Vikram Sunkara (FU), Noureldin Saleh (till August 2019, ZIB), Sourav Ray (from January 2020, ZIB)

Project Duration

01.01.2019 – 31.12.2021

Located at

FU Berlin / ZIB

Description

Opioid receptors play a major role in the treatment of severe pain. The project aims at accurate spatio-temporal modelling and simulation of intracellular signaling cascades initiated by opioid receptors, such as G-proteins, ion channels, cAMP, and the arrestin complex in order to design, e.g., non-addictive pain treatments.

Project Webpages

A German documentary about pain relief can be accessed here. M. Weber and C. Stein have contributed to this ARTE TV documentary in 02/2020.

M. Weber and C. Stein have presented a common outreach talk at URANIA in 03/2020. The link is here.

There is a ZIB webpage about this project here.

A website showing the clinical relevance and the experimental anesthesiology approach at Charité can be found here.

Selected Publications

J. Möller, A. Isbilir, T. Sungkaworn, B. Osberg, C. Karathanasis, V. Sunkara, E.O. Grushevskyi, A. Bock, P. Annibale, M. Heilemann, C. Schuette, and M.J. Lohse
Single molecule mu-opioid receptor membrane-dynamics reveal agonist-specific dimer formation with super-resolved precision.
Nature-Chemical Biology (2020), DOI: 10.1038/s41589-020-0566-1
A. Bittracher, C. Schuette
A probabilistic algorithm for aggregating vastly undersampled large Markov chains.
submitted to Physica D (2019)
S. Winkelmann, C. Schütte
Stochastic Dynamics in Computational Biology.
To appear in Springer’s Frontiers in Applied Dynamical Systems (2019)
Ray S., Sunkara V., Schütte C., Weber M.
How to calculate pH-dependent binding rates for receptor-ligand systems based on thermodynamic simulations with different binding motifs.
(submitted)
Meyer J, Del Vecchio G, Seitz V, Massaly N, Stein C:
Modulation of mu-opioid receptor activation by acidic pH is dependent on ligand structure and an ionizable amino acid residue.
Br J Pharmacol 2019; 176:4510-20; DOI: 10.1111/bph.14810
Del Vecchio G, Labuz D, Temp J, Seitz V, Kloner M, Negrete R, Rodriguez-Gaztelumendi A, Weber M, Machelska H, Stein C:
pKa of opioid ligands as a discriminating factor for side effects.
Sci Rep 2019; 9:19344. doi.org/10.1038/s41598-019-55886-1
correction: Sci Rep 2020; 10:4366. doi.org/10.1038/s41598-020-61224-7
Lešnik S, Hodošček M, Bren U, Stein C, Bondar AN:
Potential energy function for fentanyl-based opioid pain killers.
J Chem Inf Model 2020; in press
Massaly N, Temp J, Machelska H, Stein C:
Uncovering the analgesic effects of a pH-dependent mu-opioid receptor agonist using a model of non-evoked ongoing pain.
PAIN 2020 (submitted)

Review Articles:

Stein C:
Schmerzinhibition durch Opioide – neue Konzepte.
Der Schmerz 2019;33:295-302
DOI: 10.1007/s00482-019-0386-y
Stein C, Kopf A:
Pain therapy – are there new options on the horizon?
Best Practice & Research Clinical Rheumatology 2019;33:101420
doi.org/10.1016/j.berh.2019.06.002
Stein C:
Opioid analgesia: recent developments
Curr Opin Supportive & Palliative Care 2020;14:112-117
DOI:10.1097/SPC.0000000000000495

Book Chapters:

Stein C, Gaveriaux-Ruff C:
Opioids and Pain.
In: The Oxford Handbook of the Neurobiology of Pain. ed. by Wood J. Oxford University Press, New York 2020: 729-769.
DOI: 10.1093/oxfordhb/9780190860509.013.9
Stein C:
Analgesics.
In: Encyclopedia of Molecular Pharmacology 3rd Edition, ed. by Offermanns S, Rosenthal W. Springer, Berlin Heidelberg New York 2020: 1-6. doi.org/10.1007/978-3-030-21573-6_6-1
Stein C, Kopf A:
Management of the patient with chronic pain.
In: Miller’s Anesthesia 9th Edition. ed. by Gropper MA. Elsevier, Philadelphia 2020: 1604-21
Rittner HL, Oehler B, Stein C:
Immune system, pain and analgesia.
In: The Senses: A Comprehensive Reference; Vol. 6. Pain, ed. by Schaible HG, Pogatzki-Zahn E. Elsevier, Philadelphia 2020; in press

Selected Pictures

Different binding modes at only one pH value

Binding Mode of Fentanyl at pH7

The binding modes of opioids depend on the pH value of the environment. With changing the pH-value, the protonation state of the opioid and of the receptor changes, too. From a modelling point of view, each protonation event leads to a new mathematical model to describe the dynamics of the molecular system. I.e. for every protonation state of the system we end up with a different modelling and, thus, with a different resulting kinetics. The mathematical problem is given by the fact, that changes in pH-value do not simply change the protonation state, they change the probabilities for each of the states. Hence, the “correct” physical modelling of an opiod-receptor binding process is not given by one mathematical model, it is given by a set of different models combined with different statistical weights. One mathematical goal of this project was to find a way to simulate molecular processes, if the mathematical descripition of the process being used is a statistical mixture of different physical models.

Simulation of protonation states

Simulation of the opioid receptor and opioids

In this project we created all possible different physical models (different protonation states) of the opoid receptor and its binding opioid molecule. Then we simulated the underlying Markov process for each of them separately. For the mixture of the different models, we clustered all Markov states of the different physical models together and created a transition rate matrix (a dicretized Fokker-Planck operator) for the entire process by regarding the probabilities for the single models. The last step of combining the single simulation results was possible by using a certain type of discretization of Fokker-Planck operators.

The outcome of our procedure revealed that Fentanyl compared to NFEPP (two different opioids) have very different binding rates in inflamed compared to healthy tissue. For the first time in opioid research, we were able to plot a continuous curve which shows the pH-dependence of activation rates of opiod-receptors.

We were able to explain the outcome of clinical tests on the basis of this dependence.

There is a transnational challenge in going from in vitro to in situ. We bridging this gap by developing mathematical models to capture the spatio-temporal dynamics of the opioid in situ.

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