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. 2018 Mar 1;34(5):779-786.
doi: 10.1093/bioinformatics/btx698.

Predicting protein-DNA Binding Free Energy Change Upon Missense Mutations Using Modified MM/PBSA Approach: SAMPDI Webserver

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Free PMC article

Predicting protein-DNA Binding Free Energy Change Upon Missense Mutations Using Modified MM/PBSA Approach: SAMPDI Webserver

Yunhui Peng et al. Bioinformatics. .
Free PMC article

Abstract

Motivation: Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments.

Results: Here we report a new method Single Amino acid Mutation binding free energy change of Protein-DNA Interaction (SAMPDI). The method utilizes modified Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge-based terms delivered from investigations of the physicochemical properties of protein-DNA complexes. The method is benchmarked against experimentally determined binding free energy changes caused by 105 mutations in 13 proteins (compiled ProNIT database and data from recent references), and results in correlation coefficient of 0.72.

Availability and implementation: http://compbio.clemson.edu/SAMPDI.

Contact: ealexov@clemson.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

Figures

Fig. 1.
Fig. 1.
Thermodynamic cycle for binding free energy change calculations. The side chain of wild type and mutant residues are show in green and red color, respectively
Fig 2.
Fig 2.
The correlation coefficient calculated with various dielectric constants used in Delphi and NAMD. The left panel shows the dependence of correlation coefficient when dielectric constant was varied from 1 to 5 in NAMD and 1 to 10 in Delphi, while the right panel shows the same for dielectric constant varied from 1 to 5 in NAMD and 11 to 20 in Delphi. The size of the circles are proportional to the magnitude of the correlation coefficient which is also indicated by the corresponding color (see the color scheme of the right)
Fig. 3.
Fig. 3.
A plot of experimentally measured binding free energy changes and predicted binding free energy changes. The corresponding linear fit and correlation coefficient are shown as well
Fig. 4.
Fig. 4.
(A) Plot of predicted ΔΔG and experimental ΔΔG in 5-fold cross validation (see Table 3 for details). (B) Receiver operating characteristic curve of classification of large effects (|ΔΔG| > 1 kcal/mol) and small effects (|ΔΔG| < 1 kcal/mol)
Fig. 5.
Fig. 5.
Case study of consistent and inconsistent predictions. The backbone of DNA is marked as orange while protein is shown as brown. Mutation site is labeled as red along with the side chain of the wild-type residue. (A) The estrogen receptor DNA-binding domain bound to DNA (PDB: 1HCQ). (B) DNA complex of the Myb DNA-binding domain (PDB: 1MSE). (C) TN916 integrase n-terminal domain/DNA complex (PDB: 1TN9). (D) F Factor TraI Relaxase Domain bound to F oriT Single-stranded DNA (PDB: 2A0I)
Fig. 6.
Fig. 6.
(A) Work flowchart of SAMPDI webserver. (B) Performance of SAMPDI webserver showing the execution time for different size proteins

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