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Christoph Jung

Publications and source records attributed to Christoph Jung.

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Computationally guided modifications of CviUPO to improve catalytic activity

Unspecific peroxygenases (UPOs) are promising biocatalysts that selectively oxyfunctionalize saturated hydrocarbons using only hydrogen peroxide as a co-substrate. Peroxide-induced enzyme inactivation makes targeted enzyme engineering essential to mitigate this effect and also enhance catalytic performance. To meet this need, systematic approaches are used, including extensive database studies for rational enzyme design, as well as computational enzyme engineering. In this study, we followed the latter strategy and explored the possibility for computationally-guided modification of UPOs. Specifically, our focus was on uncovering the influence of active site amino acids on the catalytic activity of the enzyme CviUPO. Two mutations were introduced close to the active center, and the changes in the energy barriers leading to the activated complex were investigated in detail by Quantum Mechanics/Molecular Mechanics Nudged Elastic Band simulations. Our studies revealed that a change of the glutamic acid, assisting the catalytic cycle, by the shorter aspartic acid, leads to an increased reaction barrier, probably decreasing the catalytic activity of the enzyme. Exchanging the heme-anchoring cysteine group by a histidine exhibited promising behavior as the energy barriers decreased significantly. However, it is possible that the histidine modification also alters the reaction behavior of the peroxygenase, turning it into a peroxidase, an aspect that so far could not be confirmed beyond doubt. Simulations alone cannot conclusively determine whether substrate specificity and reactivity are maintained in the modifications tested. Nevertheless, our results highlight the importance of spin states and active pocket hydration for the catalytic reaction and demonstrate why a synergistic approach of theoretical predictions and experimental verifications is required for efficient enzyme engineering.

physics.bio-ph

Phenylalanine modification in plasma-driven biocatalysis revealed by solvent accessibility and reactive dynamics in combination with protein mass spectrometry

Biocatalysis is an emerging field that provides an environmentally friendly alternative to conventional catalysis, but still it faces some challenges. One of the major difficulties for biocatalysts that require reactive species like H2O2 as co-substrates lies in the concentration of these reactive species. On the one hand, they are used as reactants, but on the other hand, they inactivate the enzymes at high concentrations. When utilizing non-thermal plasma to deliver H2O2 for biocatalysis, it is essential to understand the potential interactions between plasma-generated species (PGS) and enzymes. This is particularly important because, alongside \ch{H2O2}, other reactive species such as hydroxyl radicals, atomic oxygen, superoxide, and nitric oxide are also produced. The investigation of the localized reactivity of the solvent accessible surface area (SASA) of an enzyme, with certain species, is an important tool for predicting these interactions. In combination with reactive molecular dynamics (MD) simulations this enabled us to identify amino acid residues that are likely targets for modifications by the PGS. A subset of the theoretical predictions made in the present study were confirmed experimentally by mass spectrometry, underlining the utility of the SASA and MD based screening approach to direct time-consuming experiments and assist their interpretation.

physics.bio-ph

Low Overhead Allocation Sampling in a Garbage Collected Virtual Machine

Compared to the more commonly used time-based profiling, allocation profiling provides an alternate view of the execution of allocation heavy dynamically typed languages. However, profiling every single allocation in a program is very inefficient. We present a sampling allocation profiler that is deeply integrated into the garbage collector of PyPy, a Python virtual machine. This integration ensures tunable low overhead for the allocation profiler, which we measure and quantify. Enabling allocation sampling profiling with a sampling period of 4 MB leads to a maximum time overhead of 25% in our benchmarks, over un-profiled regular execution.

cs.PL

In-Silico Characterization of Nanoparticle Catalysts

Nanoparticles (NPs) make for intriguing heterogeneous catalysts due to their large active surface area and excellent and often size-dependent catalytic properties that emerge from a multitude of chemically different surface reaction sites. NP catalysts are, in principle, also highly tunable: even small changes to the NP size or surface facet composition, doping with heteroatoms, or changes of the supporting material can significantly alter their physicochemical properties. Because synthesis of size- and shape-controlled NP catalysts is challenging, the ability to computationally predict the most favorable NP structures for a catalytic reaction of interest is an in-demand skill that can help accelerate and streamline the material optimization process. Fundamentally, simulations of NP model systems present unique challenges to computational scientists. Not only must considerable methodological hurdles be overcome in performing calculations with hundreds to thousands of atoms while retaining appropriate accuracy to be able to probe the desired properties. Also, the data generated by simulations of NPs are typically more complex than data from simulations of, for example, single crystal surface models, and therefore often requires different data analysis strategies. To this end, the present work aims to review analytical methods and data analysis strategies that have proven useful in extracting thermodynamic trends from NP simulations.

physics.chem-ph

Simulations of the Electrochemical Oxidation of Pt Nanoparticles of Various Shapes

The activity and stability of a platinum nanoparticle (NP) is not only affected by its size but additionally depends on its shape. To this end, simulations can identify structure-property relationships to make a priori decisions on the most promising structures. While activity is routinely probed by electronic structure calculations on simplified surface models, modeling the stability of NP model systems in electrochemical reactions is challenging due to the long timescale of relevant processes such as oxidation beyond the point of reversibility. In this work, a routine for simulating electrocatalyst stability is presented. The procedure is referred to as GREG after its main ingredients - a grand-canonical simulation approach using reactive force fields to model electrochemical reactions as a function of the galvanic cell potential. The GREG routine is applied to study the oxidation of 3 nm octahedral, cubic, dodecahedral, cuboctahedral, spherical, and tetrahexahedral platinum NPs. The oxidation process is analyzed using adsorption isobars as well as interaction energy heat maps that provide the basis for constructing electrochemical phase diagrams. Onset potentials for surface oxidation increase in the sequence cube ~= dodecahedron <= octahedron <= tetrahexahdron < sphere < cuboctahedron, establishing a relationship between oxidation behavior and surface facet structure. The electrochemical results are rationalized using structural and electronic analysis.

physics.chem-ph

Superperturbation theory on the real-axis

In this article we formulate the superperturbation theory for the Anderson impurity model on the real axis. The resulting impurity solver allows to evaluate dynamical quantities without numerical analytical continuation by the maximum entropy method or Padé approximants. This makes the solver well suited to study multiplet effects in solids within the dynamical mean field theory. First examples including multi-orbital problems are discussed.

cond-mat.str-el

Superperturbation solver for quantum impurity models

We present a very efficient solver for the general Anderson impurity problem. It is based on the perturbation around a solution obtained from exact diagonalization using a small number of bath sites. We formulate a perturbation theory which is valid for both weak and strong coupling and interpolates between these limits. Good agreement with numerically exact quantum Monte-Carlo results is found for a single bath site over a wide range of parameters. In particular, the Kondo resonance in the intermediate coupling regime is well reproduced for a single bath site and the lowest order correction. The method is particularly suited for low temperatures and alleviates analytical continuation of imaginary time data due to the absence of statistical noise compared to quantum Monte-Carlo impurity solvers.

cond-mat.str-el