Dynamics of protein-protein encounter: a Langevin equation approach with reaction patches

We study the formation of protein-protein encounter complexes with a Langevin equation approach that considers direct, steric and thermal forces. As three model systems with distinctly different properties we consider the pairs barnase:barstar, cytochrome c:cytochrome c peroxidase and p53:MDM2. In each case, proteins are modeled either as spherical particles, as dipolar spheres or as collection of several small beads with one dipole. Spherical reaction patches are placed on the model proteins according to the known experimental structures of the protein complexes. In the computer simulations, concentration is varied by changing box size. Encounter is defined as overlap of the reaction patches and the corresponding first passage times are recorded together with the number of unsuccessful contacts before encounter. We find that encounter frequency scales linearly with protein concentration, thus proving that our microscopic model results in a well-defined macroscopic encounter rate. The number of unsuccessful contacts before encounter decreases with increasing encounter rate and ranges from 20-9000. For all three models, encounter rates are obtained within one order of magnitude of the experimentally measured association rates. Electrostatic steering enhances association up to 50-fold. If diffusional encounter is dominant (p53:MDM2) or similarly important as electrostatic steering (barnase:barstar), then encounter rate decreases with decreasing patch radius. More detailed modeling of protein shapes decreases encounter rates by 5-95 percent. Our study shows how generic principles of protein-protein association are modulated by molecular features of the systems under consideration. Moreover it allows us to assess different coarse-graining strategies for the future modelling of the dynamics of large protein complexes

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy

Structure and energetics of nickel, copper, and gold clusters

The most stable structures of CuN, NiN, and AuN clusters with $2\le N\le 60$ have been determined using a combination of the embedded-atom (EAM), the quasi-Newton, and our own Aufbau/Abbau methods for the calculation of the total energy for a given structure, the structures of the local total-energy minima, and the structure of the global total-energy minimum, respectively. We have employed two well-known versions of the EAM: (1) the `bulk' version of Daw, Baskes, and Foiles and (2) the Voter-Chen version which takes into account also properties of the dimer in the parameterization. The lower-energy structures (also for the smallest) of CuN and NiN clusters (i.e., structural details as well as symmetry) obtained with the two versions are very similar. Thus, our study supports an universality of the bulk embedding functions for copper and nickel. But for gold clusters the differences between structures calculated with the two different versions of the EAM are significant, even for larger clusters

Deposition of Ni

The soft deposition of Ni13 and Cu13 clusters on Ni(111) and Cu(111) surfaces is studied by means of constant-energy molecular-dynamics simulations. The atomic interactions are described by the Embedded Atom Method. It is shown that the shape of the nickel clusters deposited on Cu(111) surfaces remains rather intact, while the copper clusters impacting on Ni(111) surfaces collapse forming double and triple layered products. Furthermore, it is found that for an impact energy of 0.5 eV/atom the structures of all investigated clusters show the lowest similarity to the original structures, except for the case of nickel clusters deposited on a Cu(111) surface. Finally, it is demonstrated that when cluster and substrate are of different materials, it is possible to control whether the deposition results in largely intact clusters on the substrate or in a spreading of the clusters. This separation into hard and soft clusters can be related to the relative cohesive energy of the crystalline materials

Theoretical Studies of Structural, Energetic, and Electronic Properties of Clusters.

International audienceSize in combination with low symmetry makes theoretical studies of the properties of clusters a challenge. This is in particular the case when the studies also shall identify the structures of the lowest total energy. We discuss here various methods for calculating the structural, energetic, and electronic properties of nanoparticles, emphasizing that the computational method always should be chosen carefully according to the scientific questions that shall be addressed. Therefore, different approximate methods for calculating the total energy of a given structure are discussed, including the embedded-atom method and a parameterized density-functional method. Moreover, different approaches for choosing/determining the structures are presented, including an Aufbau/Abbau method and genetic algorithms. In order to illustrate the approaches we present results from calculations on metallic and semiconducting nanoparticles as well as on nanostructured HAlO