EphrinA5 regulates cell motility by modulating Snhg15/DNA triplex-dependent targeting of DNMT1 to the Ncam1 promoter.

Cell-cell communication is mediated by membrane receptors and their ligands, such as the Eph/ephrin system, orchestrating cell migration during development and in diverse cancer types. Epigenetic mechanisms are key for integrating external "signals", e.g.

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories.

The disordered nature of Intrinsically Disordered Proteins (IDPs) makes their structural ensembles particularly susceptible to changes in chemical environmental conditions, often leading to an alteration of their normal functions. A Radial Distribution Function (RDF) is considered a standard method for characterizing the chemical environment surrounding particles during atomistic simulations, commonly averaged over an entire or part of a trajectory. Given their high structural variability, such averaged information might not be reliable for IDPs.

Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for Lipase A.

Improving an enzyme's (thermo-)stability or tolerance against solvents and detergents is highly relevant in protein engineering and biotechnology. Recent developments have tended toward data-driven approaches, where available knowledge about the protein is used to identify substitution sites with high potential to yield protein variants with improved stability, and subsequently, substitutions are engineered by site-directed or site-saturation (SSM) mutagenesis. However, the development and validation of algorithms for data-driven approaches have been hampered by the lack of availability of large-scale data measured in a uniform way and being unbiased with respect to substitution types and locations.

Backbone Dihedral Angle Prediction.

More than two decades of research have enabled dihedral angle predictions at an accuracy that makes them an interesting alternative or supplement to secondary structure prediction that provides detailed local structure information for every residue of a protein. The evolution of dihedral angle prediction methods is closely linked to advancements in machine learning and other relevant technologies. Consequently recent improvements in large-scale training of deep neural networks have led to the best method currently available, which achieves a mean absolute error of 19° for phi, and 30° for psi.

Discrete-continuous reaction-diffusion model with mobile point-like sources and sinks.

In many applications in soft and biological physics, there are multiple time and length scales involved but often with a distinct separation between them. For instance, in enzyme kinetics, enzymes are relatively large, move slowly and their copy numbers are typically small, while the metabolites (being transformed by these enzymes) are often present in abundance, are small in size and diffuse fast. It seems thus natural to apply different techniques to different time and length levels and couple them.

Scientific workflow optimization for improved peptide and protein identification.

Background: Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages.

The effect of composition on diffusion of macromolecules in a crowded environment.

We study diffusion of macromolecules in a crowded cytoplasm-like environment, focusing on its dependence on composition and its crossover to the anomalous subdiffusion. The crossover and the diffusion itself depend on both the volume fraction and the relative concentration of macromolecules. In accordance with previous theoretical and experimental studies, diffusion slows down when the volume fraction increases.

Comprehensive large-scale assessment of intrinsic protein disorder.

Motivation: Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.

Folding of Top7 in unbiased all-atom Monte Carlo simulations.

For computational studies of protein folding, proteins with both helical and β-sheet secondary structure elements are very challenging, as they expose subtle biases of the physical models. Here, we present reproducible folding of a 92 residue α/β protein (residues 3-94 of Top7, PDB ID: 1QYS) in computer simulations starting from random initial conformations using a transferable physical model which has been previously shown to describe the folding and thermodynamic properties of about 20 other smaller proteins of different folds. Top7 is a de novo designed protein with two α-helices and a five stranded β-sheet.

LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach.

Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al.

Simulation of Top7-CFr: a transient helix extension guides folding.

Protein structures often feature beta-sheets in which adjacent beta-strands have large sequence separation. How the folding process orchestrates the formation and correct arrangement of these strands is not comprehensively understood. Particularly challenging are proteins in which beta-strands at the N and C termini are neighbors in a beta-sheet.

BETTY: prediction of beta-strand type from sequence.

Most secondary structure prediction programs do not distinguish between parallel and antiparallel beta-sheets. However, such knowledge would constrain the available topologies of a protein significantly, and therefore aid existing fold recognition algorithms. For this reason, we propose a technique which, in combination with existing secondary structure programs such as PSIPRED, allows one to distinguish between parallel and antiparallel beta-sheets.

Understanding protein folding: small proteins in silico.

Recent improvements in methodology and increased computer power now allow atomistic computer simulations of protein folding. We briefly review several advanced Monte Carlo algorithms that have contributed to this development. Details of folding simulations of three designed mini proteins are shown.

Support vector machines for prediction of dihedral angle regions.

Motivation: Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern.