18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 43

          • Record: found
          • Abstract: found
          • Article: not found

          Very fast empirical prediction and rationalization of protein pKa values.

          A very fast empirical method is presented for structure-based protein pKa prediction and rationalization. The desolvation effects and intra-protein interactions, which cause variations in pKa values of protein ionizable groups, are empirically related to the positions and chemical nature of the groups proximate to the pKa sites. A computer program is written to automatically predict pKa values based on these empirical relationships within a couple of seconds. Unusual pKa values at buried active sites, which are among the most interesting protein pKa values, are predicted very well with the empirical method. A test on 233 carboxyl, 12 cysteine, 45 histidine, and 24 lysine pKa values in various proteins shows a root-mean-square deviation (RMSD) of 0.89 from experimental values. Removal of the 29 pKa values that are upper or lower limits results in an RMSD = 0.79 for the remaining 285 pKa values. Proteins 2005. 2005 Wiley-Liss, Inc.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Kemp elimination catalysts by computational enzyme design.

            The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10(5) and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a >200-fold increase in k(cat)/K(m) (k(cat)/K(m) of 2,600 M(-1)s(-1) and k(cat)/k(uncat) of >10(6)). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A hot spot of binding energy in a hormone-receptor interface.

              The x-ray crystal structure of the complex between human growth hormone (hGH) and the extracellular domian of its first bound receptor (hGHbp) shows that about 30 side chains from each protein make contact. Individual replacement of contact residues in the hGHbp with alanine showed that a central hydrophobic region, dominated by two tryptophan residues, accounts for more than three-quarters of the binding free energy. This "functional epitope" is surrounded by less important contact residues that are generally hydrophilic and partially hydrated, so that the interface resembles a cross section through a globular protein. The functionally important residues on the hGHbp directly contact those on hGH. Thus, only a small and complementary set of contact residues maintains binding affinity, a property that may be general to protein-protein interfaces.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                10 December 2013
                : 8
                : 12
                Affiliations
                [1 ]Schrödinger, New York, New York, United States of America
                [2 ]Schrödinger, Hyderabad, Andhra Pradesh, India
                Wake Forest University, United States of America
                Author notes

                Competing Interests: The authors have the following interests. Schrodinger, Inc. funded this study and is the employer of all authors. There are no patents, products in development or marketed products to declare. 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.

                Conceived and designed the experiments: HB WS KL. Performed the experiments: HB AC. Analyzed the data: HB AC KL. Contributed reagents/materials/analysis tools: DP. Wrote the manuscript: HB WS DP KL.

                Article
                PONE-D-13-27356
                10.1371/journal.pone.0082849
                3858304

                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.

                Funding
                Schrodinger was the only funder of our study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Uncategorized

                Comments

                Comment on this article