Tobias Cramer

Molecular Mechanism of Water Bridge Buildup: Field-Induced Formation of Nanoscale Menisci

We perform molecular dynamics calculations to describe, at the molecular level, the formation of a water bridge induced by an electric field. Restriction of orientational degrees of freedom (confinement) of water dipoles at the interfaces leads to a polarizability that depends on the shape of the water system, that is, droplet versus pillar. Above a threshold field of 1.2 V nm(-1), the competition between orientational confinement and electric field leads to the sudden formation of a water pillar. The formation of a water bridge is marked by a first order discontinuity in the total energy of the system. The simulations offer a molecular explanation for the threshold voltage and hysteresis behavior observed in the formation of nanoscale liquid bridges with a force microscope.

Charge transfer through the nucleosome: a theoretical approach

In this work, we approach the problem of charge transfer in deoxyribonucleic acid (DNA) from a theoretical and numerical perspective. We focus on a DNA geometry characteristic of the eukaryotic genome and study transport along a superhelix that contains 292 nucleobases. The electronic structure is described within the Su–Schrieffer–Heeger model in an atomistic parameterization, which has been extended by a nonretarded reaction field to take dielectric polarization effects into account. The emerging potential energy surface is analyzed using the Marcus theory of electron transfer. The computed reaction coefficients are compared to their counterparts originating from idealized geometries and to experimental findings. This comparison and the palindromic nature of the DNA sequence used here permit the assessment of fluctuations in the local orientation of the bases and their impact upon transport properties.

Latent structure pattern mining

Pattern mining methods for graph data have largely been restricted to ground features, such as frequent or correlated subgraphs. Kazius et al. have demonstrated the use of elaborate patterns in the biochemical domain, summarizing several ground features at once. Such patterns bear the potential to reveal latent information not present in any individual ground feature. However, those patterns were handcrafted by chemical experts. In this paper, we present a data-driven bottom-up method for pattern generation that takes advantage of the embedding relationships among individual ground features. The method works fully automatically and does not require data preprocessing (e.g., to introduce abstract node or edge labels). Controlling the process of generating ground features, it is possible to align them canonically and merge (stack) them, yielding a weighted edge graph. In a subsequent step, the subgraph features can further be reduced by singular value decomposition (SVD). Our experiments show that the resulting features enable substantial performance improvements on chemical datasets that have been problematic so far for graph mining approaches.

Atomistic models of biological charge transfer

Abstract In this contribution, we highlight the potential of atomically resolved models to address the problem of ground-state charge transfer reactions in biological systems like (i) DNA or (ii) proteins that incorporate transition metal complexes as model cofactors. We describe an extended version of the Su–Schrieffer–Heeger Hamiltonian as an atomistic model and the procedures that enable its efficient numerical solution. As applications, we present the computation of charge transfer rates within DNA oligomers, complex DNA arrangements like bulges or three-way junctions, and in model proteins. The results are compared with experimental findings and phenomenological concepts of biological charge transfer.