Latest SCI publications
Research project (§ 26 & § 27)
Duration : 2017-07-01 - 2019-06-30
Cellular DNA is tightly packed with histones, proteins directly involved in regulation of gene expression with impact on numerous biological processes including cell differentiation, epigenetics and disease development. In particular, histones achieve this regulation by various types and combinations of post-translational modifications that are interpreted by interactions with specific effector proteins. Despite their pivotal role in different biological contexts, effects of histone modifications on recruitment of effectors at the atomistic level remains elusive. Here, our main goal is to further our understanding of microscopic mechanisms determining the function of histone modifications. To do this, we will use molecular dynamics simulations, a widely used high-resolution computational method for studying biomolecular properties and behavior at the atomistic level. More specifically, we intend to systematically investigate how different histone modifications and combinations thereof affect interactions with related effectors. In addition, histone effectors dedicated to recognition of lysine methylation and acetylation have been recently shown as promising targets for small molecule drugs. To this end, we intend to use molecular dynamics simulations to model interactions of effectors with known active molecules in order to examine binding mechanism as well as explore binding of other compounds by using perturbation free energy calculations.
Research project (§ 26 & § 27)
Duration : 2017-09-01 - 2020-08-31
Soil organic matter (SOM) is a key part in the composition of soil, playing a crucial role in the transport and absorption of plant nutrients as well as pollutants or other xenobiotic compounds. Given the increasing environmental pollution, development and implementation of effective soil remediation strategies is of utmost importance. Various engineering approaches have been applied based on different types of physical and chemical treatments. In particular, soil bioremediation, taking advantage of utilization of biological agents for pollutant degradation and removal, presents an appealing, cost-effective alternative and for that reason has gained a growing interest in recent years. Numerous enzymes, primarily of bacterial and fungal origin, have a great remediation potential. Peroxidases, laccases and oxygenases are only a few examples of enzymes involved in detoxification of various hazardous substances, including lignin, phenolic species, other organic compounds, etc. However, their efficiency greatly varies depending on the conditions at which they are applied and may be significantly lower at polluted sites than in laboratory conditions, greatly impeding their usability. SOM is largely made up of humic substances, such as humic acids and fulvic acids. We hypothesize that different conditions and compositions of SOM result in microscopically distinct local environments, directly affecting structure and dynamics of enzymes involved in remediation processes on the one hand and the distribution of pollutants on the other. Computer models of molecular systems allow us to zoom in at the microscopic level and to interpret the experimental findings in terms of atomistic interactions and motions, and are therefore ideally suited tools for addressing this problem. We have recently developed an automated online tool “Vienna Soil-Organic-Matter Modeler” (VSOMM) for generating physics-based SOM models. We will use the modeler to create various SOM models corresponding to realistic, experimentally available SOM samples with varying compositions and use molecular dynamics simulations in combination with free energy calculations to characterize the SOM models at the atomistic level. We aim (1) to study the effect of conditions and SOM composition in combination with the level, type and position of oxidative modifications on structure and dynamics of bioremediation enzymes, (2) to explore how sorption properties of selected pollutant compounds depend on the composition and conditions of SOM.
Research project (§ 26 & § 27)
Duration : 2015-01-01 - 2018-05-31
Modern molecular life sciences place a large emphasis on the complex interactions between proteins and corresponding networks. In the pharmaceutical sciences, the focus is shifted from small-molecule drugs to so-called ‘biologicals’, which may be complex protein systems. Computational descriptions of such interactions lead to insight at the molecular level and predictions of affinities between proteins open the way to the rational design of novel therapeutics. An accurate description of protein-protein interactions and the relevant free energy differences crucially depend on appropriate sampling of all relevant conformational states, both in the bound as well as in the unbound state of the binding partners. While relatively efficient computational tools have been described for the interactions between proteins and small molecules, the protein-protein interactions pose additional challenges due to the large diversity in amino acid sequences and the intrinsic flexibility of protein structures. Another challenge is posed by proteins of which parts seem to be intrinsically disordered. In the current proposal, an international, interdisciplinary team of researchers suggests to develop efficient free energy methods and enhanced sampling tools to compute the binding free energy for complex protein systems. As a model system, the 14-3-3 family of proteins and their interaction with tyrosine hydroxylase is selected. In the unbound state, the relevant region in tyrosine hydroxylase is intrinsically disordered, and the affinity for many different sequences is to be evaluated. NMR experiments will be supported by Hamiltonian and Temperature Replica Exchange Molecular Dynamics simulations to describe the conformational ensemble of the partner protein, while the third-power fitting / one-step perturbation method will be extended to develop an universal model to compute the free energy differences between amino acids, allowing for an efficient prediction of binding affinities. The binding process itself will be described using Hamiltonian replica exchange calculations, in combination with distance field distance restraints. Overall, the developed methods will be applicable for a wide variety of protein-protein interactions and the enhanced sampling tools will allow for the calculation of complex potential of mean force profiles to describe the interaction between very flexible molecules.