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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

Correlation between experimental and calculated with SAAMBE approach data of change in binding free energy due to single point mutations for tDB (grey dots) and the one within ±2SD (black dots).
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pcbi.1004276.g005: Correlation between experimental and calculated with SAAMBE approach data of change in binding free energy due to single point mutations for tDB (grey dots) and the one within ±2SD (black dots).

Mentions: Thus, if P ≥ 0.5 the mutation is classified as a mutation expected to cause large change of the binding free energy. Otherwise, the mutation is expected to cause a small change. Thus, the final refinement of SAAMBE method is to take advantage of estimated probabilities. For each entry in the tDB we calculated the average probability P and split the database into tDB_small (P < 0.5) and tDB_large (P ≥ 0.5). For each of subsets we calculated the change in binding free energy (Eq (12)) and obtained the optimal coefficients of each energy terms in SAAMBE by multiple linear regression analysis. This resulted in two sets of SAAMBE coefficients (Table 1). For comparison we also provide the optimized weights and the correlation coefficient for the total tDB as well (Table 1). Comparing the weight coefficients in Table 1, one can see that there are some energy terms that are important for both subsets (such as EE, VE, SP, IE, entropy and Interface). Most of the mutations in the sDB_small are non-interfacial (for more than 30% of this subset the WT residue is located in the INT or SUR) and solvent exposed (~50% in RIM). Based on the magnitude of the weight coefficients, one can speculate that the changes of the binding free energy might be caused by the slight reorganization of the whole protein-protein complex that is reflected in the component energy term as well as the change in nonpolar component of salvation energy (SN). On the other hand most of the mutations in the sDB_large are located at the interface (95% are in COR, 5% in SUP area). In addition to other energy terms, for the cases of sDB_large, the change in hydrogen bonds network and the change in hydrophobicity also play significant roles. Thus, adding such features into the SAAMBE protocol, namely having different weight coefficients in the SAAMBE formula for mutations expected to cause small/large effect on the binding free energy change, increases the correlation coefficient from 0.575 to 0.624 (see Table 1 and Fig 5).


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)

Correlation between experimental and calculated with SAAMBE approach data of change in binding free energy due to single point mutations for tDB (grey dots) and the one within ±2SD (black dots).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004276.g005: Correlation between experimental and calculated with SAAMBE approach data of change in binding free energy due to single point mutations for tDB (grey dots) and the one within ±2SD (black dots).
Mentions: Thus, if P ≥ 0.5 the mutation is classified as a mutation expected to cause large change of the binding free energy. Otherwise, the mutation is expected to cause a small change. Thus, the final refinement of SAAMBE method is to take advantage of estimated probabilities. For each entry in the tDB we calculated the average probability P and split the database into tDB_small (P < 0.5) and tDB_large (P ≥ 0.5). For each of subsets we calculated the change in binding free energy (Eq (12)) and obtained the optimal coefficients of each energy terms in SAAMBE by multiple linear regression analysis. This resulted in two sets of SAAMBE coefficients (Table 1). For comparison we also provide the optimized weights and the correlation coefficient for the total tDB as well (Table 1). Comparing the weight coefficients in Table 1, one can see that there are some energy terms that are important for both subsets (such as EE, VE, SP, IE, entropy and Interface). Most of the mutations in the sDB_small are non-interfacial (for more than 30% of this subset the WT residue is located in the INT or SUR) and solvent exposed (~50% in RIM). Based on the magnitude of the weight coefficients, one can speculate that the changes of the binding free energy might be caused by the slight reorganization of the whole protein-protein complex that is reflected in the component energy term as well as the change in nonpolar component of salvation energy (SN). On the other hand most of the mutations in the sDB_large are located at the interface (95% are in COR, 5% in SUP area). In addition to other energy terms, for the cases of sDB_large, the change in hydrogen bonds network and the change in hydrophobicity also play significant roles. Thus, adding such features into the SAAMBE protocol, namely having different weight coefficients in the SAAMBE formula for mutations expected to cause small/large effect on the binding free energy change, increases the correlation coefficient from 0.575 to 0.624 (see Table 1 and Fig 5).

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