arXiv subjects

Rebecca C. Wade

Publications and source records attributed to Rebecca C. Wade.

5 recordsLinked to original sources

arXiv2021

Three-fold mechanism of inhibition of SARS-CoV-2 infection by the interaction of the spike glycoprotein with heparin

Heparin has been found to have antiviral activity against SARS-CoV-2. Here, by means of sliding window docking, molecular dynamics simulations and biochemical assays, we investigate the binding mode of heparin to the virus spike glycoprotein and the molecular basis for its antiviral activity. The simulations show that heparin binds at long, mostly positively charged patches on the spike, thereby masking the basic residues of the receptor binding domain and of the S1/S2 site. Experiments corroborated the simulation results by showing that heparin inhibits the cleavage of spike by furin by binding to the basic S1/S2 site. Our results indicate that heparin exerts its antiviral activity by both direct and allosteric mechanisms. Furthermore, the simulations provide insights into how heparan sulfate proteoglycans on the host cell can facilitate viral infection. Our results will aid the rational optimization of heparin derivatives for SARS-CoV-2 antiviral therapy.

q-bio.BM↗

arXiv2021

Brownian Dynamics Simulations of Proteins in the Presence of Surfaces: Long-range Electrostatics and Mean-field Hydrodynamics

Simulations of macromolecular diffusion and adsorption in confined environments can offer valuable mechanistic insights into numerous biophysical processes. In order to model solutes at atomic detail on relevant time scales, Brownian Dynamics simulations can be carried out with the approximation of rigid body solutes moving through a continuum solvent. This allows the precomputation of interaction potential grids for the solutes, thereby allowing the computationally efficient calculation of forces. However, hydrodynamic and long-range electrostatic interactions cannot be fully treated with grid-based approaches alone. Here, we develop a treatment of both hydrodynamic and electrostatic interactions to include the presence of surfaces by modeling grid-based and long-range interactions. We describe its application to simulate the self-association and many-molecule adsorption of the well-characterized protein Hen Egg-White Lysozyme to mica-like and silica-like surfaces. We find that the computational model can recover a number of experimental observables of the adsorption process and provide insights into their determinants. The computational model is implemented in the Simulation of Diffusional Association (SDA) software package.

cond-mat.soft↗

arXiv2020

Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from tauRAMD simulations

The protein-ligand residence time, tau, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict tau, computational methods must overcome the problem that tau often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the tau-Random Acceleration Molecular Dynamics (tauRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with engineered binding cavities. tauRAMD yields relative ligand dissociation rates in good accordance with experiments and thereby allows a comprehensive characterization of the ligand egress routes and determinants of tau. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of tau. We also find that the presence of metastable states along egress pathways slows down protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.

q-bio.QM↗

arXiv2020

A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories

The dissociation of ligands from proteins and other biomacromolecules occurs over a wide range of timescales. For most pharmaceutically relevant inhibitors, these timescales are far beyond those that are accessible by conventional molecular dynamics (MD) simulation. Consequently, to explore ligand egress mechanisms and compute dissociation rates, it is necessary to enhance the sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site, which enables the exploration of ligand egress routes without prior knowledge of the reaction coordinates. Furthermore, the tauRAMD procedure can be used to compute the relative residence times of ligands. When combined with a machine-learning analysis of protein-ligand interaction fingerprints (IFPs), molecular features that affect ligand unbinding kinetics can be identified. Here, we describe the implementation of RAMD in GROMACS 2020, which provides significantly improved computational performance, with scaling to large molecular systems. For the automated analysis of RAMD results, we developed MD-IFP, a set of tools for the generation of IFPs along unbinding trajectories and for their use in the exploration of ligand dynamics. We demonstrate that the analysis of ligand dissociation trajectories by mapping them onto the IFP space enables the characterization of ligand dissociation routes and metastable states. The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of tauRAMD in drug design.

q-bio.BM↗

arXiv2020

Recent progress in molecular simulation methods for drug binding kinetics

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

q-bio.QM↗