Skip to main content

DrugScorePPI for scoring protein-protein interactions: improving a knowledge-based scoring function by atomtype-based QSAR

Protein-protein complexes are known to play key roles in many cellular processes. Therefore, knowledge of the three-dimensional structure of protein-complexes is of fundamental importance. A key goal in protein-protein docking is to identify near-native protein-complex structures. In this work, we address this problem by deriving a knowledge-based scoring function from protein-protein complex structures and further fine-tuning of the statistical potentials against experimentally determined alanine-scanning results.

Based on the formalism of the DrugScore approach1, distance-dependent pair potentials are derived from 850 crystallographically determined protein-protein complexes 2. These DrugScorePPI potentials display quantitative differences compared to those of DrugScore, which was derived from protein-ligand complexes. When used as an objective function to score a non-redundant dataset of 54 targets with "unbound perturbation" solutions, DrugscorePPI was able to rank a near-native solution in the top ten in 89% and in the top five in 65% of the cases. Applied to a dataset of "unbound docking" solutions, DrugscorePPI was able to rank a near-native solution in the top ten in 100% and in the top five in 67% of the cases. Furthermore, Drugscore-PPI was used for computational alanine-scanning of a dataset of 18 targets with a total of 309 mutations to predict changes in the binding free energy upon mutations in the interface. Computed and experimental values showed a correlation of R2 = 0.34. To improve the predictive power, a QSAR-model was built based on 24 residue-specific atom types that improves the correlation coefficient to a value of 0.53, with a root mean square deviation of 0.89 kcal/mol. A Leave-One-Out analysis yields a correlation coefficient of 0.41. This clearly demonstrates the robustness of the model. The application to an independent validation dataset of alanine-mutations was used to show the predictive power of the method and yields a correlation coefficient of 0.51. Based on these findings, Drugscore-PPI was used to successful identify hotspots in multiple protein-interfaces. These results suggest that DrugscorePPI is an adequate method to score protein-protein interactions.

References

  1. 1.

    Gohlke H, Hendlich M, Klebe G: Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol. 2000, 295 (2): 337-356. 10.1006/jmbi.1999.3371.

    CAS  Article  Google Scholar 

  2. 2.

    Huang SY, Zou X: An iterative knowledge-based scoring function for protein-protein recognition. Proteins. 2008, 72 (2): 557-579. 10.1002/prot.21949.

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Dennis M Krüger.

Rights and permissions

Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License ( https://creativecommons.org/licenses/by-nc/2.0 ), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Reprints and Permissions

About this article

Cite this article

Krüger, D.M., Gohlke, H. DrugScorePPI for scoring protein-protein interactions: improving a knowledge-based scoring function by atomtype-based QSAR. J Cheminform 2, P20 (2010). https://doi.org/10.1186/1758-2946-2-S1-P20

Download citation

Keywords

  • Free Energy
  • Predictive Power
  • Pair Potential
  • Atom Type
  • Validation Dataset