Welcome to the Computational Biophysics Group at Saarland University.
We develop methods related to molecular dynamics simulations, with the aim to understand the relationship between structure, dynamics, and function of biological macromolecules.
We have several interesting Bachelor and Master projects available. Find out more.
The function of biological membranes goes far beyond the formation of a mere barrier. Membranes are subject to ongoing structural remodeling, which is controlled by interactions with proteins and by the lipid composition. We develop free energy calculation techniques to understand how membrane composition and interactions with proteins (such as viral fusion proteins) enable functionally important events at membranes including membrane fusion, pore formation, or drug permeation.
Collecting experimental data is often difficult – but the interpretation of the data may be even more challenging, for instance because the information content of the experimental signals is low. We develop methods for combining MD simulations with experimental data to get the best of two worlds, with some focus on small-angle X-ray and neutron scattering data (SAXS/SANS). Our developments involve accurate SAXS/SANS predictions, protein structure and ensemble refinement, studies on the protein hydration shell, and modeling of experiments at X-ray free electron lasers. We share our methods via the web server WAXSiS and GROMACS-SWAXS.
Proteins are not static building blocks but instead carry out their function –and malfunction– by structural transitions (Structure-function-dynamics relationship). We combine MD simulations with experiential data and enhanced-sampling techniques, to observe proteins while they function in atomic detail. Our portfolio comprises studies of molecular motors, protein-RNA/DNA complexes, membrane channels, and enzymes related to cancer progression.
The protein hydration shell is a key mediator of processes such as molecular recognition, protein folding, and proton transfer. How surface-exposed amino acids shape the hydration shell structure is not well understood. We combine molecular dynamics simulations with explicit-solvent predictions of small-angle X-ray scattering (SAXS) curves to quantify the contributions of all 20 proteinogenic amino acids to the hydration shell of the globular GB3 domain and the intrinsically disordered protein (IDP) XAO. We focus on two quantities encoded by SAXS curves: the hydration shell effect on the radius of gyration and the electron density contrast between protein and solvent. We derive an amino-acid-specific contrast score, revealing that acidic residues generate the strongest contrast with 1 to 1.5 excess water molecules relative to alanine, followed by cationic and polar residues. In contrast, apolar residues generate a water depletion layer. These trends are consistent across simulations with different water models. Around the XAO peptide, the hydration shell is generally far weaker compared to the globular GB3 domain, indicating unfavorable water–peptide packing at the IDP surface. The hydration shell effect on the radius of gyration of the IDP is strongly conformation-dependent. Together, the calculations show that the composition and spatial arrangement of surface-exposed amino acids govern the hydration shell structure, with implications for a wide range of biological functions and for hydration-sensitive experimental techniques such as solution scattering.
We present Moldrug, a computational tool for accelerating the hit-to-lead phase in structure-based drug design. Moldrug explores the chemical space using structural modifications suggested by the CReM library and by optimizing an adaptable fitness function with a genetic algorithm. Moldrug is complemented by Moldrug-Dashboard, a cross-platform and user-friendly graphical interface tailored for the analysis of Moldrug simulations. To illustrate Moldrug, we designed new potential inhibitors targeting the main protease (MPro) of SARS-CoV-2 by optimizing a consensus fitness function that balances binding affinity, drug-likeness, and synthetic accessibility. The designed molecules exhibited high chemical diversity. A subset of the designed molecules were ranked using MM/GBSA and alchemical binding free energy calculations, revealing predicted affinities as low as −10 kcal mol−1. Moldrug is distributed as a Python package under the Apache 2.0 license. It offers pre-configured multi-parameter fitness functions for molecular design, while being highly adaptable for integrating functionalities from external software. Documentation and tutorials are available at https://moldrug.rtfd.io.
Viral infection requires stable binding of viral fusion proteins to host membranes, which contain hundreds of lipid species. The mechanisms by which fusion proteins utilize specific host lipids to drive virus–host membrane fusion remains elusive. We conducted molecular simulations of classes I, II, and III fusion proteins interacting with membranes of diverse lipid compositions. Free energy calculations reveal that class I fusion proteins generally exhibit stronger membrane binding compared to classes II and III — a trend consistent across 74 fusion proteins from 13 viral families as suggested by sequence analysis. Class II fusion proteins utilize a lipid binding pocket formed by fusion protein monomers, stabilizing the initial binding of monomers to the host membrane prior to assembling into fusogenic trimers. In contrast, class III fusion proteins form a lipid binding pocket at the monomer–monomer interface through a unique fusion loop crossover. The distinct lipid binding modes correlate with the differing maturation pathways of classes II and III proteins. Binding affinity was predominantly controlled by cholesterol and gangliosides as well as via local enrichment of polyunsaturated lipids, thereby locally enhancing membrane disorder. Our study reveals energetics and atomic details underlying lipid recognition and reorganization by different viral fusion protein classes, offering insights into their specialized membrane fusion pathways.
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