Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin.
Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TF...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010583 |
| _version_ | 1811197443676569600 |
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| author | Annie M Westerlund Akshay Sridhar Leo Dahl Alma Andersson Anna-Yaroslava Bodnar Lucie Delemotte |
| author_facet | Annie M Westerlund Akshay Sridhar Leo Dahl Alma Andersson Anna-Yaroslava Bodnar Lucie Delemotte |
| author_sort | Annie M Westerlund |
| collection | DOAJ |
| description | Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases. |
| first_indexed | 2024-04-12T01:13:45Z |
| format | Article |
| id | doaj.art-de1f3494b1c14b96a419a69eeb53a105 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T01:13:45Z |
| publishDate | 2022-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-de1f3494b1c14b96a419a69eeb53a1052022-12-22T03:54:01ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-10-011810e101058310.1371/journal.pcbi.1010583Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin.Annie M WesterlundAkshay SridharLeo DahlAlma AnderssonAnna-Yaroslava BodnarLucie DelemotteCalmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases.https://doi.org/10.1371/journal.pcbi.1010583 |
| spellingShingle | Annie M Westerlund Akshay Sridhar Leo Dahl Alma Andersson Anna-Yaroslava Bodnar Lucie Delemotte Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. PLoS Computational Biology |
| title | Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. |
| title_full | Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. |
| title_fullStr | Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. |
| title_full_unstemmed | Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. |
| title_short | Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin. |
| title_sort | markov state modelling reveals heterogeneous drug inhibition mechanism of calmodulin |
| url | https://doi.org/10.1371/journal.pcbi.1010583 |
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