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PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality.

Dehouck Y, Kwasigroch JM, Gilis D, Rooman M - BMC Bioinformatics (2011)

Bottom Line: Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute.This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatique génomique et structurale, Université Libre de Bruxelles, Av, Fr, Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be

ABSTRACT

Background: The rational design of modified proteins with controlled stability is of extreme importance in a whole range of applications, notably in the biotechnological and environmental areas, where proteins are used for their catalytic or other functional activities. Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.

Results: PoPMuSiC-2.1 is a web server that predicts the thermodynamic stability changes caused by single site mutations in proteins, using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the mutated residue. PoPMuSiC presents good prediction performances (correlation coefficient of 0.8 between predicted and measured stability changes, in cross validation, after exclusion of 10% outliers). It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute. This unique functionality is user-friendly implemented in PoPMuSiC and is particularly easy to exploit. Another new functionality of our server concerns the estimation of the optimality of each amino acid in the sequence, with respect to the stability of the structure. It may be used to detect structural weaknesses, i.e. clusters of non-optimal residues, which represent particularly interesting sites for introducing targeted mutations. This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions. To illustrate the interest of this new functionality, we apply it to a dataset of known catalytic sites, and show that a much larger than average concentration of structural weaknesses is detected, quantifying how these sites have been optimized for function rather than stability.

Conclusion: The freely available PoPMuSiC-2.1 web server is highly useful for identifying very rapidly a list of possibly relevant mutations with the desired stability properties, on which subsequent experimental studies can be focused. It can also be used to detect sequence regions corresponding to structural weaknesses, which could be functionally important or structurally delicate regions, with obvious applications in rational protein design.

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Distribution of the measured values of ΔΔG in the dataset of 2648 mutants used to train and validate PoPMuSiC.
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Figure 1: Distribution of the measured values of ΔΔG in the dataset of 2648 mutants used to train and validate PoPMuSiC.

Mentions: The dataset used to train and validate the model contains 2648 different single-site mutations, in 131 proteins of known structure, whose impact on the folding free energy of the protein has been experimentally determined [29]. The data was originally extracted from the the ProTherm database [39], and thoroughly checked to correct or eliminate erroneous inputs. Mutations introduced in heme proteins or in pseudo-wild type constructs were not considered. Mutations that involve a proline or destabilize the structure by more than 5 kcal/mol were also rejected, since they are likely to induce structural modifications that are not taken into account by PoPMuSiC. The distribution of the measured changes in folding free energy caused by the mutations that are present in our dataset is given in Figure 1, and is very similar to previously published and discussed distributions of free energy changes upon mutations [10].


PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality.

Dehouck Y, Kwasigroch JM, Gilis D, Rooman M - BMC Bioinformatics (2011)

Distribution of the measured values of ΔΔG in the dataset of 2648 mutants used to train and validate PoPMuSiC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113940&req=5

Figure 1: Distribution of the measured values of ΔΔG in the dataset of 2648 mutants used to train and validate PoPMuSiC.
Mentions: The dataset used to train and validate the model contains 2648 different single-site mutations, in 131 proteins of known structure, whose impact on the folding free energy of the protein has been experimentally determined [29]. The data was originally extracted from the the ProTherm database [39], and thoroughly checked to correct or eliminate erroneous inputs. Mutations introduced in heme proteins or in pseudo-wild type constructs were not considered. Mutations that involve a proline or destabilize the structure by more than 5 kcal/mol were also rejected, since they are likely to induce structural modifications that are not taken into account by PoPMuSiC. The distribution of the measured changes in folding free energy caused by the mutations that are present in our dataset is given in Figure 1, and is very similar to previously published and discussed distributions of free energy changes upon mutations [10].

Bottom Line: Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute.This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatique génomique et structurale, Université Libre de Bruxelles, Av, Fr, Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be

ABSTRACT

Background: The rational design of modified proteins with controlled stability is of extreme importance in a whole range of applications, notably in the biotechnological and environmental areas, where proteins are used for their catalytic or other functional activities. Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.

Results: PoPMuSiC-2.1 is a web server that predicts the thermodynamic stability changes caused by single site mutations in proteins, using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the mutated residue. PoPMuSiC presents good prediction performances (correlation coefficient of 0.8 between predicted and measured stability changes, in cross validation, after exclusion of 10% outliers). It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute. This unique functionality is user-friendly implemented in PoPMuSiC and is particularly easy to exploit. Another new functionality of our server concerns the estimation of the optimality of each amino acid in the sequence, with respect to the stability of the structure. It may be used to detect structural weaknesses, i.e. clusters of non-optimal residues, which represent particularly interesting sites for introducing targeted mutations. This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions. To illustrate the interest of this new functionality, we apply it to a dataset of known catalytic sites, and show that a much larger than average concentration of structural weaknesses is detected, quantifying how these sites have been optimized for function rather than stability.

Conclusion: The freely available PoPMuSiC-2.1 web server is highly useful for identifying very rapidly a list of possibly relevant mutations with the desired stability properties, on which subsequent experimental studies can be focused. It can also be used to detect sequence regions corresponding to structural weaknesses, which could be functionally important or structurally delicate regions, with obvious applications in rational protein design.

Show MeSH
Related in: MedlinePlus