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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).

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Related in: MedlinePlus

The dependence of the mean time of the algorithm execution from the total number of residues in the WT-complex.
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pcbi.1004276.g006: The dependence of the mean time of the algorithm execution from the total number of residues in the WT-complex.

Mentions: One of the main considerations in developing SAAMBE algorithm was the requirement of the predictions to be made in reasonable time. We tested the time of the algorithm execution for all entries in sDB. The average time was 0.21953 min for one mutation calculation (SE = 0.00316min) when employing 16 nodes for WT- and MT-complexes minimization and single node for the rest of calculation on Clemson University Palmetto Supercomputer (http://citi.clemson.edu/palmetto/). We also analyzed the effect of particular parameters such as the number of residues in the complex and the largest dimension for the WT-complex on the time of calculations. It was found that the shape of the protein has no impact on the time of calculations. However the total number of residues in the complex affects the total calculation time (Fig 6). One can see that the dependence of time of algorithm execution vs the total number of residues in the WT-complex can be described with polynomial (second power) function (R = 0.99, 81 points). The free coefficient is 5.59E-2 min, the linear and quadratic weights are -5.13E-5 min and 1.18E-6 min respectively.


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 dependence of the mean time of the algorithm execution from the total number of residues in the WT-complex.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004276.g006: The dependence of the mean time of the algorithm execution from the total number of residues in the WT-complex.
Mentions: One of the main considerations in developing SAAMBE algorithm was the requirement of the predictions to be made in reasonable time. We tested the time of the algorithm execution for all entries in sDB. The average time was 0.21953 min for one mutation calculation (SE = 0.00316min) when employing 16 nodes for WT- and MT-complexes minimization and single node for the rest of calculation on Clemson University Palmetto Supercomputer (http://citi.clemson.edu/palmetto/). We also analyzed the effect of particular parameters such as the number of residues in the complex and the largest dimension for the WT-complex on the time of calculations. It was found that the shape of the protein has no impact on the time of calculations. However the total number of residues in the complex affects the total calculation time (Fig 6). One can see that the dependence of time of algorithm execution vs the total number of residues in the WT-complex can be described with polynomial (second power) function (R = 0.99, 81 points). The free coefficient is 5.59E-2 min, the linear and quadratic weights are -5.13E-5 min and 1.18E-6 min respectively.

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