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On the effect of protein conformation diversity in discriminating among neutral and disease related single amino acid substitutions.

Juritz E, Fornasari MS, Martelli PL, Fariselli P, Casadio R, Parisi G - BMC Genomics (2012)

Bottom Line: Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB).At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.Our results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina.

ABSTRACT

Background: Non-synonymous coding SNPs (nsSNPs) that are associated to disease can also be related with alterations in protein stability. Computational methods are available to predict the effect of single amino acid substitutions (SASs) on protein stability based on a single folded structure. However, the native state of a protein is not unique and it is better represented by the ensemble of its conformers in dynamic equilibrium. The maintenance of the ensemble is essential for protein function. In this work we investigated how protein conformational diversity can affect the discrimination of neutral and disease related SASs based on protein stability estimations. For this purpose, we used 119 proteins with 803 associated SASs, 60% of which are disease related. Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB). Our dataset contains proteins with different extensions of conformational diversity summing up a total number of 1023 conformers.

Results: The existence of different conformers for a given protein introduces great variability in the estimation of the protein stability (ΔΔG) after a single amino acid substitution (SAS) as computed with FoldX. Indeed, in 35% of our protein set at least one SAS can be described as stabilizing, destabilizing or neutral when a cutoff value of ±2 kcal/mol is adopted for discriminating neutral from perturbing SASs. However, when the ΔΔG variability among conformers is taken into account, the correlation among the perturbation of protein stability and the corresponding disease or neutral phenotype increases as compared with the same analysis on single protein structures. At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.

Conclusions: Our results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

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Distributions of maximum and minimum values of ΔΔG obtained for the different conformers for each protein in the dataset. Disease and neutral SASs are shown as separate distributions.
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Figure 3: Distributions of maximum and minimum values of ΔΔG obtained for the different conformers for each protein in the dataset. Disease and neutral SASs are shown as separate distributions.

Mentions: For each of the 803 SASs a ΔΔG estimation was performed for all the conformers of each protein using FoldX [44]. The accuracy of FoldX to predict stability changes has been discussed before [20,46]. For each mutation we registered the maximum and minimum ΔΔG values and the maximum difference of the ΔΔG values among different conformers of the same protein (maximum Δ(ΔΔG)). The distributions of maximum and minimum ΔΔG values and the distribution of the maximum difference of ΔΔG values (max. Δ(ΔΔG)) are shown in Figures 3 and 4, respectively, for both disease related and neutral SASs. We found that maximum and minimum values of ΔΔG of disease related SASs have higher (destabilizing) values compared with those of neutral SASs. The distributions of minimum ΔΔG values have average values of 1.47kcal/mol and of 0.38 kcal/mol for disease related and neutral SASs, respectively. This distribution difference is significant (Kolmogorov-Smirnov test with P-value < 1 10-5). In turn, average maximum ΔΔG values for disease and neutral SASs are 4.63 and 1.86 kcal/mol respectively (Kolmogorov-Smirnov test with P-value < 1 10-5). Considering the distributions of maximum variation of the Δ(ΔΔG), most of the values (69%) are below 1kcal/mol. This value can be regarded as a typical standard error in the estimation of ΔΔG [44] (Figure 4). However, 31% of the SASs have maximum Δ(ΔΔG) above the standard error and a significant difference between the ΔΔG estimations of the different conformers.


On the effect of protein conformation diversity in discriminating among neutral and disease related single amino acid substitutions.

Juritz E, Fornasari MS, Martelli PL, Fariselli P, Casadio R, Parisi G - BMC Genomics (2012)

Distributions of maximum and minimum values of ΔΔG obtained for the different conformers for each protein in the dataset. Disease and neutral SASs are shown as separate distributions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3303731&req=5

Figure 3: Distributions of maximum and minimum values of ΔΔG obtained for the different conformers for each protein in the dataset. Disease and neutral SASs are shown as separate distributions.
Mentions: For each of the 803 SASs a ΔΔG estimation was performed for all the conformers of each protein using FoldX [44]. The accuracy of FoldX to predict stability changes has been discussed before [20,46]. For each mutation we registered the maximum and minimum ΔΔG values and the maximum difference of the ΔΔG values among different conformers of the same protein (maximum Δ(ΔΔG)). The distributions of maximum and minimum ΔΔG values and the distribution of the maximum difference of ΔΔG values (max. Δ(ΔΔG)) are shown in Figures 3 and 4, respectively, for both disease related and neutral SASs. We found that maximum and minimum values of ΔΔG of disease related SASs have higher (destabilizing) values compared with those of neutral SASs. The distributions of minimum ΔΔG values have average values of 1.47kcal/mol and of 0.38 kcal/mol for disease related and neutral SASs, respectively. This distribution difference is significant (Kolmogorov-Smirnov test with P-value < 1 10-5). In turn, average maximum ΔΔG values for disease and neutral SASs are 4.63 and 1.86 kcal/mol respectively (Kolmogorov-Smirnov test with P-value < 1 10-5). Considering the distributions of maximum variation of the Δ(ΔΔG), most of the values (69%) are below 1kcal/mol. This value can be regarded as a typical standard error in the estimation of ΔΔG [44] (Figure 4). However, 31% of the SASs have maximum Δ(ΔΔG) above the standard error and a significant difference between the ΔΔG estimations of the different conformers.

Bottom Line: Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB).At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.Our results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina.

ABSTRACT

Background: Non-synonymous coding SNPs (nsSNPs) that are associated to disease can also be related with alterations in protein stability. Computational methods are available to predict the effect of single amino acid substitutions (SASs) on protein stability based on a single folded structure. However, the native state of a protein is not unique and it is better represented by the ensemble of its conformers in dynamic equilibrium. The maintenance of the ensemble is essential for protein function. In this work we investigated how protein conformational diversity can affect the discrimination of neutral and disease related SASs based on protein stability estimations. For this purpose, we used 119 proteins with 803 associated SASs, 60% of which are disease related. Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB). Our dataset contains proteins with different extensions of conformational diversity summing up a total number of 1023 conformers.

Results: The existence of different conformers for a given protein introduces great variability in the estimation of the protein stability (ΔΔG) after a single amino acid substitution (SAS) as computed with FoldX. Indeed, in 35% of our protein set at least one SAS can be described as stabilizing, destabilizing or neutral when a cutoff value of ±2 kcal/mol is adopted for discriminating neutral from perturbing SASs. However, when the ΔΔG variability among conformers is taken into account, the correlation among the perturbation of protein stability and the corresponding disease or neutral phenotype increases as compared with the same analysis on single protein structures. At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.

Conclusions: Our results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

Show MeSH