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Structural Rigidity and Protein Thermostability in Variants of Lipase A from Bacillus subtilis.

Rathi PC, Jaeger KE, Gohlke H - PLoS ONE (2015)

Bottom Line: Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability.Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability.Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pharmaceutical and Medical Chemistry, Heinrich-Heine-University, Düsseldorf, Germany.

ABSTRACT
Understanding the origin of thermostability is of fundamental importance in protein biochemistry. Opposing views on increased or decreased structural rigidity of the folded state have been put forward in this context. They have been related to differences in the temporal resolution of experiments and computations that probe atomic mobility. Here, we find a significant (p = 0.004) and fair (R2 = 0.46) correlation between the structural rigidity of a well-characterized set of 16 mutants of lipase A from Bacillus subtilis (BsLipA) and their thermodynamic thermostability. We apply the rigidity theory-based Constraint Network Analysis (CNA) approach, analyzing directly and in a time-independent manner the statics of the BsLipA mutants. We carefully validate the CNA results on macroscopic and microscopic experimental observables and probe for their sensitivity with respect to input structures. Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability. Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability. Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

No MeSH data available.


Related in: MedlinePlus

Pairwise correlations of cluster distributions (using 10 clusters) of unfolding pathways of wild type BsLipA and mutants from Rao et al.The upper triangle shows pairwise correlation coefficients as dial plots where a filled portion of a pie indicates the magnitude of the correlation (r) and blue (red) color indicates a positive (negative) correlation. The lower triangle shows 68% data ellipses (depicting the bivariate mean ± 1 standard deviation) [90] and scatterplots of the respective cluster distributions (frequencies of network topologies in each of the 10 clusters) of the two BsLipA variants as red lines smoothed by locally-weighted polynomial regression [91]. Data points and axes for the plots in the lower triangle are omitted for clarity. The blow-up in the right bottom corner depicts exemplarily for the TM versus WT case axes, axes labels, data points, the smoothing line obtained by locally-weighted polynomial regression, and the 68% data ellipsis. The figure was generated using the “corrgram” package [92] of the R program (http://www.r-project.org).
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pone.0130289.g005: Pairwise correlations of cluster distributions (using 10 clusters) of unfolding pathways of wild type BsLipA and mutants from Rao et al.The upper triangle shows pairwise correlation coefficients as dial plots where a filled portion of a pie indicates the magnitude of the correlation (r) and blue (red) color indicates a positive (negative) correlation. The lower triangle shows 68% data ellipses (depicting the bivariate mean ± 1 standard deviation) [90] and scatterplots of the respective cluster distributions (frequencies of network topologies in each of the 10 clusters) of the two BsLipA variants as red lines smoothed by locally-weighted polynomial regression [91]. Data points and axes for the plots in the lower triangle are omitted for clarity. The blow-up in the right bottom corner depicts exemplarily for the TM versus WT case axes, axes labels, data points, the smoothing line obtained by locally-weighted polynomial regression, and the 68% data ellipsis. The figure was generated using the “corrgram” package [92] of the R program (http://www.r-project.org).

Mentions: Next, we investigated why the thermostabilities of the wild type and the mutant 6B were predicted falsely. Since the precision of the computations shown in Fig 4A is high (the standard error in the mean is < 0.38 K in all cases), we reasoned that the false prediction must arise from a systematic difference between the wild type and 6B versus all other mutants of Rao et al. Thus, we mutually compared all unfolding pathways of the systems as described in “Materials and Methods”. After partitioning unfolding pathways of BsLipA variants characterized on a residue basis by the percolation index pi into 10 clusters (see Fig 1 for the pi profiles of the 10 cluster medoids), we calculated correlation coefficients from the resulting cluster distributions for all pairs of variants (Fig 5; Tables A and B in S1 File).


Structural Rigidity and Protein Thermostability in Variants of Lipase A from Bacillus subtilis.

Rathi PC, Jaeger KE, Gohlke H - PLoS ONE (2015)

Pairwise correlations of cluster distributions (using 10 clusters) of unfolding pathways of wild type BsLipA and mutants from Rao et al.The upper triangle shows pairwise correlation coefficients as dial plots where a filled portion of a pie indicates the magnitude of the correlation (r) and blue (red) color indicates a positive (negative) correlation. The lower triangle shows 68% data ellipses (depicting the bivariate mean ± 1 standard deviation) [90] and scatterplots of the respective cluster distributions (frequencies of network topologies in each of the 10 clusters) of the two BsLipA variants as red lines smoothed by locally-weighted polynomial regression [91]. Data points and axes for the plots in the lower triangle are omitted for clarity. The blow-up in the right bottom corner depicts exemplarily for the TM versus WT case axes, axes labels, data points, the smoothing line obtained by locally-weighted polynomial regression, and the 68% data ellipsis. The figure was generated using the “corrgram” package [92] of the R program (http://www.r-project.org).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4493141&req=5

pone.0130289.g005: Pairwise correlations of cluster distributions (using 10 clusters) of unfolding pathways of wild type BsLipA and mutants from Rao et al.The upper triangle shows pairwise correlation coefficients as dial plots where a filled portion of a pie indicates the magnitude of the correlation (r) and blue (red) color indicates a positive (negative) correlation. The lower triangle shows 68% data ellipses (depicting the bivariate mean ± 1 standard deviation) [90] and scatterplots of the respective cluster distributions (frequencies of network topologies in each of the 10 clusters) of the two BsLipA variants as red lines smoothed by locally-weighted polynomial regression [91]. Data points and axes for the plots in the lower triangle are omitted for clarity. The blow-up in the right bottom corner depicts exemplarily for the TM versus WT case axes, axes labels, data points, the smoothing line obtained by locally-weighted polynomial regression, and the 68% data ellipsis. The figure was generated using the “corrgram” package [92] of the R program (http://www.r-project.org).
Mentions: Next, we investigated why the thermostabilities of the wild type and the mutant 6B were predicted falsely. Since the precision of the computations shown in Fig 4A is high (the standard error in the mean is < 0.38 K in all cases), we reasoned that the false prediction must arise from a systematic difference between the wild type and 6B versus all other mutants of Rao et al. Thus, we mutually compared all unfolding pathways of the systems as described in “Materials and Methods”. After partitioning unfolding pathways of BsLipA variants characterized on a residue basis by the percolation index pi into 10 clusters (see Fig 1 for the pi profiles of the 10 cluster medoids), we calculated correlation coefficients from the resulting cluster distributions for all pairs of variants (Fig 5; Tables A and B in S1 File).

Bottom Line: Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability.Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability.Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pharmaceutical and Medical Chemistry, Heinrich-Heine-University, Düsseldorf, Germany.

ABSTRACT
Understanding the origin of thermostability is of fundamental importance in protein biochemistry. Opposing views on increased or decreased structural rigidity of the folded state have been put forward in this context. They have been related to differences in the temporal resolution of experiments and computations that probe atomic mobility. Here, we find a significant (p = 0.004) and fair (R2 = 0.46) correlation between the structural rigidity of a well-characterized set of 16 mutants of lipase A from Bacillus subtilis (BsLipA) and their thermodynamic thermostability. We apply the rigidity theory-based Constraint Network Analysis (CNA) approach, analyzing directly and in a time-independent manner the statics of the BsLipA mutants. We carefully validate the CNA results on macroscopic and microscopic experimental observables and probe for their sensitivity with respect to input structures. Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability. Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability. Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

No MeSH data available.


Related in: MedlinePlus