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Bridging scales through multiscale modeling: a case study on protein kinase A.

Boras BW, Hirakis SP, Votapka LW, Malmstrom RD, Amaro RE, McCulloch AD - Front Physiol (2015)

Bottom Line: These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data.These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events.Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.

ABSTRACT
The goal of multiscale modeling in biology is to use structurally based physico-chemical models to integrate across temporal and spatial scales of biology and thereby improve mechanistic understanding of, for example, how a single mutation can alter organism-scale phenotypes. This approach may also inform therapeutic strategies or identify candidate drug targets that might otherwise have been overlooked. However, in many cases, it remains unclear how best to synthesize information obtained from various scales and analysis approaches, such as atomistic molecular models, Markov state models (MSM), subcellular network models, and whole cell models. In this paper, we use protein kinase A (PKA) activation as a case study to explore how computational methods that model different physical scales can complement each other and integrate into an improved multiscale representation of the biological mechanisms. Using measured crystal structures, we show how molecular dynamics (MD) simulations coupled with atomic-scale MSMs can provide conformations for Brownian dynamics (BD) simulations to feed transitional states and kinetic parameters into protein-scale MSMs. We discuss how milestoning can give reaction probabilities and forward-rate constants of cAMP association events by seamlessly integrating MD and BD simulation scales. These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data. These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events. Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

No MeSH data available.


Milestoning applied to unite MD and BD. MD and BD Simulations are run to populate transition times and probilities in a milestoning model of cAMP binding to PKA. BD simulations are used to model an encounter event, and subsequent MD simulations model the details of the actual binding or reaction event.
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Figure 4: Milestoning applied to unite MD and BD. MD and BD Simulations are run to populate transition times and probilities in a milestoning model of cAMP binding to PKA. BD simulations are used to model an encounter event, and subsequent MD simulations model the details of the actual binding or reaction event.

Mentions: To give an example, we examine the hypothetical case where the kon of binding between PKA and cAMP can be predicted. In this milestoning model, we define a set of concentric spheres of different radii, all centered on the binding site of PKA (Figure 4). These concentric spheres define the milestones. MD simulations are started from conformations where cAMP is located on each spherical milestone, and each simulation is similarly terminated once cAMP diffuses to another surface. Thus, to the milestoning model, whichever simulation method is used to populate the transition kernels and incubation time vectors with statistics is of no consequence. The most appropriate simulation method can be chosen when cAMP is started on a particular surface.


Bridging scales through multiscale modeling: a case study on protein kinase A.

Boras BW, Hirakis SP, Votapka LW, Malmstrom RD, Amaro RE, McCulloch AD - Front Physiol (2015)

Milestoning applied to unite MD and BD. MD and BD Simulations are run to populate transition times and probilities in a milestoning model of cAMP binding to PKA. BD simulations are used to model an encounter event, and subsequent MD simulations model the details of the actual binding or reaction event.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4563169&req=5

Figure 4: Milestoning applied to unite MD and BD. MD and BD Simulations are run to populate transition times and probilities in a milestoning model of cAMP binding to PKA. BD simulations are used to model an encounter event, and subsequent MD simulations model the details of the actual binding or reaction event.
Mentions: To give an example, we examine the hypothetical case where the kon of binding between PKA and cAMP can be predicted. In this milestoning model, we define a set of concentric spheres of different radii, all centered on the binding site of PKA (Figure 4). These concentric spheres define the milestones. MD simulations are started from conformations where cAMP is located on each spherical milestone, and each simulation is similarly terminated once cAMP diffuses to another surface. Thus, to the milestoning model, whichever simulation method is used to populate the transition kernels and incubation time vectors with statistics is of no consequence. The most appropriate simulation method can be chosen when cAMP is started on a particular surface.

Bottom Line: These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data.These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events.Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.

ABSTRACT
The goal of multiscale modeling in biology is to use structurally based physico-chemical models to integrate across temporal and spatial scales of biology and thereby improve mechanistic understanding of, for example, how a single mutation can alter organism-scale phenotypes. This approach may also inform therapeutic strategies or identify candidate drug targets that might otherwise have been overlooked. However, in many cases, it remains unclear how best to synthesize information obtained from various scales and analysis approaches, such as atomistic molecular models, Markov state models (MSM), subcellular network models, and whole cell models. In this paper, we use protein kinase A (PKA) activation as a case study to explore how computational methods that model different physical scales can complement each other and integrate into an improved multiscale representation of the biological mechanisms. Using measured crystal structures, we show how molecular dynamics (MD) simulations coupled with atomic-scale MSMs can provide conformations for Brownian dynamics (BD) simulations to feed transitional states and kinetic parameters into protein-scale MSMs. We discuss how milestoning can give reaction probabilities and forward-rate constants of cAMP association events by seamlessly integrating MD and BD simulation scales. These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data. These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events. Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

No MeSH data available.