Biology subjects

Bolhuis, P. G.

Publications and source records attributed to Bolhuis, P. G..

3 recordsLinked to original sources

bioRxiv2018

Unbiased Atomistic Insight in the Mechanisms and Solvent role for Globular protein Dimer Dissociation

Association and dissociation of proteins are fundamental processes in nature. While this process is simple to understand conceptually, the details of the underlying mechanism and role of the solvent are poorly understood. Here we investigate the mechanism and solvent role for the dissociation of the hydrophilic {beta}-lactoglobulin dimer by employing transition path sampling. Analysis of the sampled path ensembles indicates that dissociation (and association) occurs via a variety of mechanisms: 1) a direct aligned dissociation 2) a hopping and rebinding transition followed by unbinding 3) a sliding transition before unbinding. Reaction coordinate and transition state analysis predicts that, besides native contact and vicinity salt-bridge interactions, solvent degrees of freedom play an important role in the dissociation process. Analysis of the structure and dynamics of the solvent molecules reveals that the dry native interface induces enhanced populations of both disordered hydration water and hydration water with higher tetrahedrality, mainly nearby hydrophobic residues. Bridging waters, hydrogen bonded to both proteins, support contacts, and exhibit a faster decay and reorientation dynamics in the transition state than in the native state interface, which renders the proteins more mobile and assists in rebinding. While not exhaustive, our sampling of rare un-biased reactive molecular dynamics trajectories shows in full detail how proteins can dissociate via complex pathways including (multiple) rebinding events. The atomistic insight obtained assists in further understanding and control of the dynamics of protein-protein interaction including the role of solvent.\n\nPACS numbers:

biophysics↗

bioRxiv2018

OpenPathSampling: A Python framework for path sampling simulations. I. Basics

Transition path sampling techniques allow molecular dynamics simulations of complex systems to focuson rare dynamical events, providing insight into mechanisms and the ability to calculate rates inaccessibleby ordinary dynamics simulations. While path sampling algorithms are conceptually as simple as importancesampling Monte Carlo, the technical complexity of their implementation has kept these techniquesout of reach of the broad community. Here, we introduce an easy-to-use Python framework called Open-PathSampling (OPS) that facilitates path sampling for (bio)molecular systems with minimal effort and yetis still extensible. Interfaces to OpenMM and an internal dynamics engine for simple models are providedin the initial release, but new molecular simulation packages can easily be added. Multiple ready-to-usetransition path sampling methodologies are implemented, including standard transition path sampling (TPS)between reactant and product states, transition interface sampling (TIS) and its replica exchange variant(RETIS), as well as recent multistate and multiset extensions of transition interface sampling (MSTIS, MISTIS).In addition, tools are provided to facilitate the implementation of new path sampling schemes built on basicpath sampling components. In this paper, we give an overview of the design of this framework and illustratethe simplicity of applying the available path sampling algorithms to a variety of benchmark problems.

biophysics↗

bioRxiv2018

OpenPathSampling: A Python framework for path sampling simulations. II. Building and customizing path ensembles and sample schemes

The OpenPathSampling (OPS) package provides an easy-to-use framework to apply transition path sampling methodologies to complex molecular systems with a minimum of effort. Yet, the extensibility of OPS allows for the exploration of new path sampling algorithms by building on a variety of basic operations. In a companion paper [Swenson et al 2018] we introduced the basic concepts and the structure of the OPS package, and how it can be employed to perform standard transition path sampling and (replica exchange) transition interface sampling. In this paper, we elaborate on two theoretical developments that went into the design of OPS. The first development relates to the construction of path ensembles, the what is being sampled. We introduce a novel set-based notation forthepath ensemble, which provides an alternative paradigm for constructing path ensembles, and allows building arbitrarily complex path ensembles from fundamental ones. The second fundamental development is the structure for the customisation of Monte Carlo procedures; how path ensembles are being sampled. We describe in detail the OPS objects that implement this approach to customization, the MoveScheme and the PathMover, and provide tools to create and manipulate these objects. We illustrate both the path ensemble building and sampling scheme customization with several examples. OPS thus facilitates both standard path sampling application in complex systems as well as the development of new path sampling methodology, beyond the default.

biophysics↗