Protein engineers use a combination of computational approaches and experimental techniques to find sequence variations that can modulate protein function. Proteins can be modified to serve multiple different functional roles, with practical applications in medicine, industry, and basic science. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. There are no patents, products in development or marketed products to declare. funded this study and is the employer of all authors. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have the following interests. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: Schrodinger was the only funder of our study.
Received: JAccepted: OctoPublished: December 10, 2013Ĭopyright: © 2013 Beard et al. PLoS ONE 8(12):Įditor: Freddie Salsbury, Jr, Wake Forest University, United States of America 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.Ĭitation: Beard H, Cholleti A, Pearlman D, Sherman W, Loving KA (2013) Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes. 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. 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.
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 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. 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.
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