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Improved Free-Energy Landscape Quantification Illustrated with a Computationally Designed Protein-Ligand Interaction.

ABSTRACT: Quantifying the energy landscape underlying protein-ligand interactions leads to an enhanced understanding of molecular recognition. A powerful yet accessible single-molecule technique is atomic force microscopy (AFM)-based force spectroscopy, which generally yields the zero-force dissociation rate constant (koff ) and the distance to the transition state (?x? ). Here, we introduce an enhanced AFM assay and apply it to probe the computationally designed protein DIG10.3 binding to its target ligand, digoxigenin. Enhanced data quality enabled an analysis that yielded the height of the transition state (?G? =6.3±0.2?kcal?mol-1 ) and the shape of the energy barrier at the transition state (linear-cubic) in addition to the traditional parameters [koff (=4±0.1×10-4 ?s-1 ) and ?x? (=8.3±0.1?Å)]. We expect this automated and relatively rapid assay to provide a more complete energy landscape description of protein-ligand interactions and, more broadly, the diverse systems studied by AFM-based force spectroscopy.

SUBMITTER: Van Patten WJ

PROVIDER: S-EPMC5760306 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other

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Improved Free-Energy Landscape Quantification Illustrated with a Computationally Designed Protein-Ligand Interaction.

Van Patten William J WJ Walder Robert R Adhikari Ayush A Okoniewski Stephen R SR Ravichandran Rashmi R Tinberg Christine E CE Baker David D Perkins Thomas T TT

Chemphyschem : a European journal of chemical physics and physical chemistry 20171204 1

Quantifying the energy landscape underlying protein-ligand interactions leads to an enhanced understanding of molecular recognition. A powerful yet accessible single-molecule technique is atomic force microscopy (AFM)-based force spectroscopy, which generally yields the zero-force dissociation rate constant (k<sub>off</sub> ) and the distance to the transition state (Δx<sup>≠</sup> ). Here, we introduce an enhanced AFM assay and apply it to probe the computationally designed protein DIG10.3 bind ...[more]

PMID: 29069529

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Project description:The molecular simulation of chemical reaction equilibrium (CRE) is a challenging and important problem of broad applicability in chemistry and chemical engineering. The primary molecular-based approach for solving this problem has been the reaction ensemble Monte Carlo (REMC) algorithm [Turner et al. Molec. Simulation2008, 34, (2), 119-146], based on classical force-field methodology. In spite of the vast improvements in computer hardware and software since its original development almost 25 years ago, its more widespread application is impeded by its computational inefficiency. A fundamental problem is that its MC basis inhibits the implementation of significant parallelization, and its successful implementation often requires system-specific tailoring and the incorporation of special MC approaches such as replica exchange, expanded ensemble, umbrella sampling, configurational bias, and continuous fractional component methodologies. We describe herein a novel CRE algorithm (reaction ensemble molecular dynamics, ReMD) that exploits modern computer hardware and software capabilities, and which can be straightforwardly implemented for systems of arbitrary size and complexity by exploiting the parallel computing methodology incorporated within many MD software packages (herein, we use GROMACS for illustrative purposes). The ReMD algorithm utilizes these features in the context of a macroscopically inspired and generally applicable free energy minimization approach based on the iterative approximation of the system Gibbs free energy function by a mathematically simple convex ideal solution model using the composition at each iteration as a reference state. Finally, we additionally describe a simple and computationally efficient a posteriori method to estimate the equilibrium concentrations of species present in very small amounts relative to others in the primary calculation. To demonstrate the algorithm, we show its application to two classic example systems considered previously in the literature: the N2-O2-NO system and the ammonia synthesis system.

Dataset Information

Improved Free-Energy Landscape Quantification Illustrated with a Computationally Designed Protein-Ligand Interaction.

Publications

Improved Free-Energy Landscape Quantification Illustrated with a Computationally Designed Protein-Ligand Interaction.

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