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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 mutated residue location (WT, left panel; 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 WT (left panel) and MT (right panel) residues being in the given location cause 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/.
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pcbi.1004276.g004: Distribution of mutated residue location (WT, left panel; 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 WT (left panel) and MT (right panel) residues being in the given location cause 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/.

Mentions: With respect to mutation site location, we select all available cases K for which the mutation site in the WT is located at Y, where Y is either COR, SUP, RIM, INT or SUR. Then we define a probability of mutations within K to cause large effect as:P(Y,WT)=KlargeK(2)where Klarge are the cases experimentally found to result in absolute binding free energy change larger than 1kcal/mol (Fig 4, left panel). Since mutations involve amino acids with different side chain length and MT and MT structures are subjected to energy minimization, it is quite likely that mutation site location is different in MT compared with WT. For this reason, the same analysis is done for the MT and the corresponding probabilities are defined as P(Y, MT) (Fig 4, 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 mutated residue location (WT, left panel; 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 WT (left panel) and MT (right panel) residues being in the given location cause 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/.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004276.g004: Distribution of mutated residue location (WT, left panel; 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 WT (left panel) and MT (right panel) residues being in the given location cause 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/.
Mentions: With respect to mutation site location, we select all available cases K for which the mutation site in the WT is located at Y, where Y is either COR, SUP, RIM, INT or SUR. Then we define a probability of mutations within K to cause large effect as:P(Y,WT)=KlargeK(2)where Klarge are the cases experimentally found to result in absolute binding free energy change larger than 1kcal/mol (Fig 4, left panel). Since mutations involve amino acids with different side chain length and MT and MT structures are subjected to energy minimization, it is quite likely that mutation site location is different in MT compared with WT. For this reason, the same analysis is done for the MT and the corresponding probabilities are defined as P(Y, MT) (Fig 4, 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