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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.


Brownian dynamics simulation method. BD simulations begin by placing molecules at a distance b from one another, shown here as a b-surface around PKA. When molecules diffuse toward the encounter complex (gold) a “reaction” (green arrow) occurs. Alternatively, molecules can “escape” (red arrow) by diffusing past a distance equal to q, shown here as the q-surface.
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Figure 3: Brownian dynamics simulation method. BD simulations begin by placing molecules at a distance b from one another, shown here as a b-surface around PKA. When molecules diffuse toward the encounter complex (gold) a “reaction” (green arrow) occurs. Alternatively, molecules can “escape” (red arrow) by diffusing past a distance equal to q, shown here as the q-surface.

Mentions: At the start of a simulation, the ligand is placed at a distance b from the receptor, at a location known as the b surface, which is defined as the distance where forces between the two molecules are centrosymmetric. Simulations terminate either upon the molecules reaching the predefined bimolecular encounter complex (a binding event), or when the molecules separate beyond a greater intermolecular distance q. The distance q, the radius of the q surface, is typically 10–50 nm larger than the distance b (Gabdoulline and Wade, 1998). The probability of association vs. escape is then used to calculate the association rate constant (kon). This schematic, including the surfaces at the b and q distances, are depicted using PKA as the receptor and cAMP as the ligand (Figure 3). BD can be used to model the association of cAMP with PKA, and predict features of the binding event, including the route of approach, the encounter complex, and the rate constant of association.


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)

Brownian dynamics simulation method. BD simulations begin by placing molecules at a distance b from one another, shown here as a b-surface around PKA. When molecules diffuse toward the encounter complex (gold) a “reaction” (green arrow) occurs. Alternatively, molecules can “escape” (red arrow) by diffusing past a distance equal to q, shown here as the q-surface.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Brownian dynamics simulation method. BD simulations begin by placing molecules at a distance b from one another, shown here as a b-surface around PKA. When molecules diffuse toward the encounter complex (gold) a “reaction” (green arrow) occurs. Alternatively, molecules can “escape” (red arrow) by diffusing past a distance equal to q, shown here as the q-surface.
Mentions: At the start of a simulation, the ligand is placed at a distance b from the receptor, at a location known as the b surface, which is defined as the distance where forces between the two molecules are centrosymmetric. Simulations terminate either upon the molecules reaching the predefined bimolecular encounter complex (a binding event), or when the molecules separate beyond a greater intermolecular distance q. The distance q, the radius of the q surface, is typically 10–50 nm larger than the distance b (Gabdoulline and Wade, 1998). The probability of association vs. escape is then used to calculate the association rate constant (kon). This schematic, including the surfaces at the b and q distances, are depicted using PKA as the receptor and cAMP as the ligand (Figure 3). BD can be used to model the association of cAMP with PKA, and predict features of the binding event, including the route of approach, the encounter complex, and the rate constant of association.

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.