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A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins.

Cheng TM, Goehring L, Jeffery L, Lu YE, Hayles J, Novák B, Bates PA - PLoS Comput. Biol. (2012)

Bottom Line: We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway.We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior.Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

View Article: PubMed Central - PubMed

Affiliation: Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom.

ABSTRACT
Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

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(A)–(D) shows the SIF cores of the mutations studied in the MAPK model.(A) The reduced model; (B) the reduced model with initial conditions from Fujioka et al; (C) the reduced model with initial conditions from Fujioka et al and parameters optimized by fitting to the time course data in Fujioka et al; (D) the original non-reduced model. (E) A scheme shows the relationship between the key proteins and their clinical syndromes.
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pcbi-1002738-g005: (A)–(D) shows the SIF cores of the mutations studied in the MAPK model.(A) The reduced model; (B) the reduced model with initial conditions from Fujioka et al; (C) the reduced model with initial conditions from Fujioka et al and parameters optimized by fitting to the time course data in Fujioka et al; (D) the original non-reduced model. (E) A scheme shows the relationship between the key proteins and their clinical syndromes.

Mentions: Unlike missense mutations in the yeast G2-M model, there are no quantitative measurements of the physiological outcomes for the mutations in the MAPK pathway that can be used to calculate the correlation with SIF scores. Hence, as an indirect way to evaluate the relationship between mutations and clinical symptoms, each mutation is represented by three SIF scores calculated according to the systemic impact on the wild-type Erk expression curve: measured as amplitude, duration and peak time differences. The trajectory of the SIFs corresponding to each mutation as a function of these three target quantities shows that mutations in Raf1, B-Raf and Mek are more likely to be overlapped in a similar region, whereas mutations in H-Ras tend to distribute in a very different trajectory to the direction of the other mutations (Figure 5A). To determine if the different distribution of H-Ras mutations is a robust feature, a different set of initial concentrations that were measured experimentally in HeLa cells by Fujioka et al. [26] is used to derive two new parameter sets: one produces expression curves similar to those of the original model, whilst the other one produces curves fitted to the in vivo FRET data measured by Fujioka et al [26] (the parameters of both models are available in Text S1). As shown in Figure 5B and 5C, both parameter sets distribute H-Ras mutations in a trajectory different from other mutations, which suggests that the separation of H-Ras is not sensitive to variations to initial concentrations and parameter space. As a benchmark, the three dimensional SIF scores from the original model are also presented (Figure 5D). Consistently, H-Ras mutations are distributed into a distinctly different group.


A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins.

Cheng TM, Goehring L, Jeffery L, Lu YE, Hayles J, Novák B, Bates PA - PLoS Comput. Biol. (2012)

(A)–(D) shows the SIF cores of the mutations studied in the MAPK model.(A) The reduced model; (B) the reduced model with initial conditions from Fujioka et al; (C) the reduced model with initial conditions from Fujioka et al and parameters optimized by fitting to the time course data in Fujioka et al; (D) the original non-reduced model. (E) A scheme shows the relationship between the key proteins and their clinical syndromes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475653&req=5

pcbi-1002738-g005: (A)–(D) shows the SIF cores of the mutations studied in the MAPK model.(A) The reduced model; (B) the reduced model with initial conditions from Fujioka et al; (C) the reduced model with initial conditions from Fujioka et al and parameters optimized by fitting to the time course data in Fujioka et al; (D) the original non-reduced model. (E) A scheme shows the relationship between the key proteins and their clinical syndromes.
Mentions: Unlike missense mutations in the yeast G2-M model, there are no quantitative measurements of the physiological outcomes for the mutations in the MAPK pathway that can be used to calculate the correlation with SIF scores. Hence, as an indirect way to evaluate the relationship between mutations and clinical symptoms, each mutation is represented by three SIF scores calculated according to the systemic impact on the wild-type Erk expression curve: measured as amplitude, duration and peak time differences. The trajectory of the SIFs corresponding to each mutation as a function of these three target quantities shows that mutations in Raf1, B-Raf and Mek are more likely to be overlapped in a similar region, whereas mutations in H-Ras tend to distribute in a very different trajectory to the direction of the other mutations (Figure 5A). To determine if the different distribution of H-Ras mutations is a robust feature, a different set of initial concentrations that were measured experimentally in HeLa cells by Fujioka et al. [26] is used to derive two new parameter sets: one produces expression curves similar to those of the original model, whilst the other one produces curves fitted to the in vivo FRET data measured by Fujioka et al [26] (the parameters of both models are available in Text S1). As shown in Figure 5B and 5C, both parameter sets distribute H-Ras mutations in a trajectory different from other mutations, which suggests that the separation of H-Ras is not sensitive to variations to initial concentrations and parameter space. As a benchmark, the three dimensional SIF scores from the original model are also presented (Figure 5D). Consistently, H-Ras mutations are distributed into a distinctly different group.

Bottom Line: We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway.We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior.Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

View Article: PubMed Central - PubMed

Affiliation: Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom.

ABSTRACT
Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

Show MeSH
Related in: MedlinePlus