Biology subjects

Bolhuis, P. G.

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

5 recordsLinked to original sources

bioRxiv2025

MDNA: a software module for DNA structure generation and analysis

Exploring the dynamical and structural properties of molecular complexes involving DNA is a fundamentally important aspect of understanding many biological processes. Although tools exist for modeling linear DNA and simple complexes, significant challenges remain in generating intricate biomolecular assemblies and incorporating biologically relevant modifications. These limitations restrict the ability to create accurate starting configurations for advanced molecular simulation studies. Here, we introduce MDNA, a molecular modeling toolkit that bridges these gaps by enabling the construction and analysis of complex DNA structures. MDNA offers a versatile solution to generate DNA shapes using a spline-based mapping technique that enables the construction of DNA configurations with arbitrary shapes. Key features include support for (non-)canonical base modifications, such as Watson-Crick-Franklin to Hoogsteen transitions, DNA methylation, and the ability to refine structures using Monte Carlo minimization. The toolkit also provides geometric analysis tools based on rigid body formalism to evaluate DNA structures and trajectories. Together, these features enable users to model and analyze DNA configurations in high detail with a modular Python interface. By integrating structure generation and analysis into a single workflow, MDNA improves the study of DNA-protein interactions, paving the way for new insights into DNA dynamics and molecular simulations.

biophysics↗

bioRxiv2020

Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas

The GTPase KRas is a signaling protein in networks for cell differentiation, growth, and division. KRas mutations can prolong activation of these networks, resulting in tumor formation. When active, KRas tightly binds GTP. Several oncogenic mutations affect the conversion between this rigid state and inactive, more flexible states. Detailed understanding of these transitions may provide valuable insights into how mutations affect KRas. Path sampling simulations, which focus on transitions, show KRas visiting several states, which are the same for wild type and the oncogenic mutant Q61L. Large differences occur when converting between these states, indicating the dramatic effect of the Q61L mutation on KRas dynamics. For Q61L a route to the flexible state is inaccessible, thus shifting the equilibrium to more rigid states. Our methodology presents a novel way to predict dynamical effects of KRas mutations, which may aid in identifying potential therapeutic targets. Author summaryCancer cells frequently contain mutations in the protein KRas. However, KRas is a challenging target for anti-cancer drugs, in part because the dynamic behavior of flexible regions in the protein is difficult to characterize experimentally, and occurs on timescales that are too long for straightforward molecular dynamics simulations. We have used path sampling, an advanced simulation technique that overcomes long timescales, to obtain atomistic insight into the dynamics of KRas. Comparing the oncogenic mutant Q61L to the wild type revealed that the mutation closes off one transition channel for deactivating KRas. Our approach opens up the way for predicting the dynamical effects of mutations in KRas, which may aid in identifying potential therapeutic targets.

cancer biology↗

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↗