Abstract

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity—the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking “hotspots,” or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

Highlights

  • Proteins can be modified to serve multiple different functional roles, with practical applications in medicine, industry, and basic science

  • Rational design can be used to predict specific amino acid modifications — even a single mutation can significantly impact protein solubility, stability, or affinity — and this may be the best approach to a given design problem

  • We present results for computational affinity predictions of single mutations at protein-protein interfaces using the MM-GBSA approach [24,25] in BioLuminate, which incorporates the OPLS2005 force field [26,27], VSGB solvent model [28], and rotamer search algorithms from Prime

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Summary

Introduction

Proteins can be modified to serve multiple different functional roles, with practical applications in medicine, industry, and basic science. For most of the systems, a very conservative level of protein flexibility (i.e. minimization of only the mutated side chain of interest) produced the best correlations between the calculated and experimental results (Figure 1).

Results
Conclusion
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