Limits...
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

The distribution of the absolute values of the experimental ΔΔG in sDB.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4492929&req=5

pcbi.1004276.g002: The distribution of the absolute values of the experimental ΔΔG in sDB.

Mentions: The experimentally measured changes of the binding free energy caused by mutations vary from zero to very large positive values (+8.803) and very small negative values (-3.786). It can be anticipated that there may be some structural or sequence characteristics associated with the magnitude of the binding free energy change. To test such a possibility, we first provide the distribution of the absolute changes of experimental binding free energy in sDB dataset (Fig 2). It can be seen that the cases with absolute binding free energy change of less than 1kcal/mol account for about 50% of the cases. Therefore we chose to split the whole database into two sets with similar number of entries: one set with small effect (/ΔΔG/<1kcal/mol); and another with large effect (/ΔΔG/≥1kcal/mol).


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)

The distribution of the absolute values of the experimental ΔΔG in sDB.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004276.g002: The distribution of the absolute values of the experimental ΔΔG in sDB.
Mentions: The experimentally measured changes of the binding free energy caused by mutations vary from zero to very large positive values (+8.803) and very small negative values (-3.786). It can be anticipated that there may be some structural or sequence characteristics associated with the magnitude of the binding free energy change. To test such a possibility, we first provide the distribution of the absolute changes of experimental binding free energy in sDB dataset (Fig 2). It can be seen that the cases with absolute binding free energy change of less than 1kcal/mol account for about 50% of the cases. Therefore we chose to split the whole database into two sets with similar number of entries: one set with small effect (/ΔΔG/<1kcal/mol); and another with large effect (/ΔΔG/≥1kcal/mol).

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