arXiv subjects

Martin Vögele

Publications and source records attributed to Martin Vögele.

4 recordsLinked to original sources

arXiv2025

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA

Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions -- for example, with various mutations or bound ligands -- in order to identify differences between the molecular conformations adopted under these conditions. However, the large amount of data produced by simulations of ever larger and more complex systems often renders it difficult to identify the structural features that are relevant for a particular biochemical phenomenon. We present a flexible software package named PENSA that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides featurizations and feature transformations that allow for a complete representation of biomolecules like proteins and nucleic acids, including water and ion binding sites, thus avoiding bias that would come with manual feature selection. PENSA implements methods to systematically compare the distributions of molecular features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule, which allows, e.g., tracing information flow to identify allosteric pathways. PENSA also comes with convenient tools for loading data and visualizing results, making them quick to process and easy to interpret. PENSA is an open-source Python library maintained at https://github.com/drorlab/pensa along with an example workflow and a tutorial. We demonstrate its usefulness in real-world examples by showing how it helps to determine molecular mechanisms efficiently.

q-bio.BM↗

arXiv2022

ATOM3D: Tasks On Molecules in Three Dimensions

Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of either systematic performance benchmarks or a unified toolkit for interacting with molecular data. To address this, we present ATOM3D, a collection of both novel and existing benchmark datasets spanning several key classes of biomolecules. We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, graph networks performing well on systems requiring detailed positional information, and the more recently developed equivariant networks showing significant promise. Our results indicate that many molecular problems stand to gain from three-dimensional molecular learning, and that there is potential for improvement on many tasks which remain underexplored. To lower the barrier to entry and facilitate further developments in the field, we also provide a comprehensive suite of tools for dataset processing, model training, and evaluation in our open-source atom3d Python package. All datasets are available for download from https://www.atom3d.ai .

cs.LG↗

arXiv2019

Atomistic simulation of PDADMAC/PSS oligoelectrolyte multilayers: overall comparison of tri- and tetra-layer systems

By employing large-scale molecular dynamics simulations of atomistically resolved oligoelectrolytes in aqueous solutions, we study in detail the first four layer-by-layer deposition cycles of an oligoelectrolyte multilayer made of poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt) (PDADMAC/PSS). The multilayers are grown on a silica substrate in 0.1M {NaCl} electrolyte solutions and the swollen structures are then subsequently exposed to varying added salt concentration. We investigated the microscopic properties of the films, analyzing in detail the differences between three- and four-layers systems. Our simulations provide insights on the early stages of growth of a multilayer, which are particularly challenging for experimental observations. We found a rather strong entanglement of the oligoelectrolytes, with a fuzzy layering of the film structure. The main charge compensation mechanism is for all cases intrinsic, whereas extrinsic compensation is relatively ehanced for the layer of the last deposition cycle. In addition, we quantified other fundamental observables of these systems, as the film thickness, water uptake, and overcharge fractions for each deposition layer.

cond-mat.soft↗

arXiv2018

Hydrodynamics of Diffusion in Lipid Membrane Simulations

By performing molecular dynamics simulations with up to 132 million coarse-grained particles in half-micron sized boxes, we show that hydrodynamics quantitatively explains the finite-size effects on diffusion of lipids, proteins, and carbon nanotubes in membranes. The resulting Oseen correction allows us to extract infinite-system diffusion coefficients and membrane surface viscosities from membrane simulations despite the logarithmic divergence of apparent diffusivities with increasing box width. The hydrodynamic theory of diffusion applies also to membranes with asymmetric leaflets and embedded proteins, and to a complex plasma-membrane mimetic.

cond-mat.soft↗