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Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

Petukh M, Li M, Alexov E - PLoS Comput. Biol. (2015)

Bottom Line: While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants.This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins.The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

View Article: PubMed Central - PubMed

Affiliation: Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina, United States of America.

ABSTRACT
A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

No MeSH data available.


Related in: MedlinePlus

Distribution of residue types (being as WT, left panel; being as MT, right panel) by "small/large effect" regions of experimentally obtained change in binding free energy in sDB.On the x-axis: the probability of the particular type of residue substitution (WT on left panel, MT—on the right one) to result in a large change in binding free energy. On the y-axis: the averaged absolute value of experimental ΔΔG provided with standard error of mean at an error bar and the total number of cases across whole sDB. The actual data is presented in black color, while the orange one is based on the weighted distribution of /ΔΔG/ (see text for details).
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pcbi.1004276.g003: Distribution of residue types (being as WT, left panel; being as MT, right panel) by "small/large effect" regions of experimentally obtained change in binding free energy in sDB.On the x-axis: the probability of the particular type of residue substitution (WT on left panel, MT—on the right one) to result in a large change in binding free energy. On the y-axis: the averaged absolute value of experimental ΔΔG provided with standard error of mean at an error bar and the total number of cases across whole sDB. The actual data is presented in black color, while the orange one is based on the weighted distribution of /ΔΔG/ (see text for details).

Mentions: Furthermore we collect all available substitutions M of a given type X → any residue, where X is a particular amino acid (for example, Ala, Arg, etc). Then we calculate the mean and variance of experimental change of the binding free energy for these M cases. In addition, we introduce an estimation of the probability (P) of mutation type X → any to cause large effect by:P(X→any)=MlargeM(1)where Mlarge is the number of cases within M subset for which the absolute change of the binding free energy is larger than 1kcal/mol (large effect) (Fig 3, left panel). Similarly we perform the same analysis for substitutions of (any → X) and define the corresponding probabilities P(any → X) (Fig 3, right panel).


Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

Petukh M, Li M, Alexov E - PLoS Comput. Biol. (2015)

Distribution of residue types (being as WT, left panel; being as MT, right panel) by "small/large effect" regions of experimentally obtained change in binding free energy in sDB.On the x-axis: the probability of the particular type of residue substitution (WT on left panel, MT—on the right one) to result in a large change in binding free energy. On the y-axis: the averaged absolute value of experimental ΔΔG provided with standard error of mean at an error bar and the total number of cases across whole sDB. The actual data is presented in black color, while the orange one is based on the weighted distribution of /ΔΔG/ (see text for details).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4492929&req=5

pcbi.1004276.g003: Distribution of residue types (being as WT, left panel; being as MT, right panel) by "small/large effect" regions of experimentally obtained change in binding free energy in sDB.On the x-axis: the probability of the particular type of residue substitution (WT on left panel, MT—on the right one) to result in a large change in binding free energy. On the y-axis: the averaged absolute value of experimental ΔΔG provided with standard error of mean at an error bar and the total number of cases across whole sDB. The actual data is presented in black color, while the orange one is based on the weighted distribution of /ΔΔG/ (see text for details).
Mentions: Furthermore we collect all available substitutions M of a given type X → any residue, where X is a particular amino acid (for example, Ala, Arg, etc). Then we calculate the mean and variance of experimental change of the binding free energy for these M cases. In addition, we introduce an estimation of the probability (P) of mutation type X → any to cause large effect by:P(X→any)=MlargeM(1)where Mlarge is the number of cases within M subset for which the absolute change of the binding free energy is larger than 1kcal/mol (large effect) (Fig 3, left panel). Similarly we perform the same analysis for substitutions of (any → X) and define the corresponding probabilities P(any → X) (Fig 3, right panel).

Bottom Line: While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants.This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins.The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

View Article: PubMed Central - PubMed

Affiliation: Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina, United States of America.

ABSTRACT
A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

No MeSH data available.


Related in: MedlinePlus