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Calculation of substrate binding affinities for a bacterial GH78 rhamnosidase through molecular dynamics simulations.

ABSTRACT: Ram2 from Pediococcus acidilactici is a rhamnosidase from the glycoside hydrolase family 78. It shows remarkable selectivity for rutinose rather than para-nitrophenyl-alpha-l-rhamnopyranoside (p-NPR). Molecular dynamics simulations were performed using a homology model of this enzyme, in complex with both substrates. Free energy calculations lead to predicted binding affinities of -34.4 and -30.6 kJ mol-1 respectively, agreeing well with an experimentally estimated relative free energy of 5.4 kJ mol-1. Further, the most relevant binding poses could be determined. While p-NPR preferably orients its rhamnose moiety toward the active site, rutinose interacts most strongly with its glucose moiety. A detailed hydrogen bond analysis confirms previously implicated residues in the active site (Asp217, Asp222, Trp226, Asp229 and Glu488) and quantifies the importance of individual residues for the binding. The most important amino acids are Asp229 and Phe339 which are involved in many interactions during the simulations. While Phe339 was observed in more simulations, Asp229 was involved in more persistent interactions (forming an average of at least 2 hydrogen bonds during the simulation). These analyses directly suggest mutations that could be used in a further experimental characterization of the enzyme. This study shows once more the strength of computer simulations to rationalize and guide experiments at an atomic level.

SUBMITTER: Grandits M

PROVIDER: S-EPMC3663046 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Calculation of substrate binding affinities for a bacterial GH78 rhamnosidase through molecular dynamics simulations.

Grandits Melanie M Michlmayr Herbert H Sygmund Christoph C Oostenbrink Chris C

Journal of molecular catalysis. B, Enzymatic 20130801 100

Ram2 from <i>Pediococcus acidilactici</i> is a rhamnosidase from the glycoside hydrolase family 78. It shows remarkable selectivity for rutinose rather than para-nitrophenyl-alpha-l-rhamnopyranoside (p-NPR). Molecular dynamics simulations were performed using a homology model of this enzyme, in complex with both substrates. Free energy calculations lead to predicted binding affinities of -34.4 and -30.6 kJ mol<sup>-1</sup> respectively, agreeing well with an experimentally estimated relative fre ...[more]

PMID: 23914137

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Project description:The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Dataset Information

Calculation of substrate binding affinities for a bacterial GH78 rhamnosidase through molecular dynamics simulations.

Publications

Calculation of substrate binding affinities for a bacterial GH78 rhamnosidase through molecular dynamics simulations.

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