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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.


Protein Kinase A cyclic nucleotide binding domain Markov state model. This figure shows a graph repensantion the transtions between metastable states of the CBD with cAMP bound. Each node repesents the conformational state. The edges the transition between the node with their thickness being proportional to the probiblity of transtion.
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Figure 2: Protein Kinase A cyclic nucleotide binding domain Markov state model. This figure shows a graph repensantion the transtions between metastable states of the CBD with cAMP bound. Each node repesents the conformational state. The edges the transition between the node with their thickness being proportional to the probiblity of transtion.

Mentions: An atomic-scale MSM describes the conformational ensemble of a protein as the probability of transitioning between discrete collections of conformational states at a fixed time (Pande et al., 2010; Chodera and Noé, 2014). This can be visualized as a bidirectional graph, (see Figure 2), where each node represents a cluster of similar conformations. The probability of transition between states is indicated by the thickness of the connecting lines in Figure 2. If the conformational states and the transitions can be accurately determined, then the MSM describes the thermodynamics and the kinetics of the system's conformational ensemble. Thus, one can derive the key parameters required for higher scale models with a MSM (Prinz et al., 2011a).


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)

Protein Kinase A cyclic nucleotide binding domain Markov state model. This figure shows a graph repensantion the transtions between metastable states of the CBD with cAMP bound. Each node repesents the conformational state. The edges the transition between the node with their thickness being proportional to the probiblity of transtion.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Protein Kinase A cyclic nucleotide binding domain Markov state model. This figure shows a graph repensantion the transtions between metastable states of the CBD with cAMP bound. Each node repesents the conformational state. The edges the transition between the node with their thickness being proportional to the probiblity of transtion.
Mentions: An atomic-scale MSM describes the conformational ensemble of a protein as the probability of transitioning between discrete collections of conformational states at a fixed time (Pande et al., 2010; Chodera and Noé, 2014). This can be visualized as a bidirectional graph, (see Figure 2), where each node represents a cluster of similar conformations. The probability of transition between states is indicated by the thickness of the connecting lines in Figure 2. If the conformational states and the transitions can be accurately determined, then the MSM describes the thermodynamics and the kinetics of the system's conformational ensemble. Thus, one can derive the key parameters required for higher scale models with a MSM (Prinz et al., 2011a).

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.