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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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Correlation between SIF and in vivo cell length of missense mutations in the G2-M model.The experimentally measured cell lengths and the calculated SIF scores at 25°C and 30°C are shown in grey and black, respectively. The x-axis error bars show the standard error of cell lengths; the y-axis error bars show the standard error of SIF scores, resulting from the evaluation of ΔΔG.
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pcbi-1002738-g002: Correlation between SIF and in vivo cell length of missense mutations in the G2-M model.The experimentally measured cell lengths and the calculated SIF scores at 25°C and 30°C are shown in grey and black, respectively. The x-axis error bars show the standard error of cell lengths; the y-axis error bars show the standard error of SIF scores, resulting from the evaluation of ΔΔG.

Mentions: For the eight missense mutation studies presented here, their SIF values are calculated (Table 3) and the length of their host yeast cells are measured at septation (Methods and Material section ‘Yeast strains and cell length measurement’). As shown in Figure 2, the in silico SIF score generally reflects the in vivo cell length well: at the semi-restrictive temperature (30°C) a medium-to-strong correlation R2 = 0.69 (p value = 0.04; all the p values shown in this study are based on the two-tailed model) is obtained.


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)

Correlation between SIF and in vivo cell length of missense mutations in the G2-M model.The experimentally measured cell lengths and the calculated SIF scores at 25°C and 30°C are shown in grey and black, respectively. The x-axis error bars show the standard error of cell lengths; the y-axis error bars show the standard error of SIF scores, resulting from the evaluation of ΔΔG.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002738-g002: Correlation between SIF and in vivo cell length of missense mutations in the G2-M model.The experimentally measured cell lengths and the calculated SIF scores at 25°C and 30°C are shown in grey and black, respectively. The x-axis error bars show the standard error of cell lengths; the y-axis error bars show the standard error of SIF scores, resulting from the evaluation of ΔΔG.
Mentions: For the eight missense mutation studies presented here, their SIF values are calculated (Table 3) and the length of their host yeast cells are measured at septation (Methods and Material section ‘Yeast strains and cell length measurement’). As shown in Figure 2, the in silico SIF score generally reflects the in vivo cell length well: at the semi-restrictive temperature (30°C) a medium-to-strong correlation R2 = 0.69 (p value = 0.04; all the p values shown in this study are based on the two-tailed model) is obtained.

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