Three-Dimensional Polyglycerol-PEG-Based Hydrogels as a Universal High-Sensitivity Platform for SPR Analysis.

We developed a three-dimensional (3D) polyglycerol-poly(ethylene glycol)-based hydrogel as a new biosensing matrix for affinity analysis by surface plasmon resonance to enable a high loading of ligands for small molecule analysis while lacking a carbohydrate structure to reduce nonspecific binding. The hydrogel was synthesized by cross-linking a polyglycerol functionalized with carboxylate and maleimide groups with a dithiolated poly(ethylene glycol) by thiol-click chemistry. We demonstrated that the hydrogel coating enabled a high immobilization capacity of biomolecules and led to less nonspecific binding.

Memory and Friction: From the Nanoscale to the Macroscale.

Friction is a phenomenon that manifests across all spatial and temporal scales, from the molecular to the macroscopic scale. It describes the dissipation of energy from the motion of particles or abstract reaction coordinates and arises in the transition from a detailed molecular-level description to a simplified, coarse-grained model. It has long been understood that time-dependent (non-Markovian) friction effects are critical for describing the dynamics of many systems, but that they are notoriously difficult to evaluate for complex physical, chemical, and biological systems.

Data-driven classification of individual cells by their non-Markovian motion.

We present a method to differentiate organisms solely by their motion based on the generalized Langevin equation (GLE) and use it to distinguish two different swimming modes of strongly confined unicellular microalgae Chlamydomonas reinhardtii. The GLE is a general model for active or passive motion of organisms and particles that can be derived from a time-dependent general many-body Hamiltonian and in particular includes non-Markovian effects (i.e.

Fast protein folding is governed by memory-dependent friction.

When described by a low-dimensional reaction coordinate, the folding rates of most proteins are determined by a subtle interplay between free-energy barriers, which separate folded and unfolded states, and friction. While it is commonplace to extract free-energy profiles from molecular trajectories, a direct evaluation of friction is far more elusive and typically relies on fits of measured reaction rates to memoryless reaction-rate theories. Here, using memory-kernel extraction methods founded on a generalized Langevin equation (GLE) formalism, we directly calculate the time-dependent friction acting on the fraction of native contacts reaction coordinate , evaluated for eight fast-folding proteins, taken from a published set of large-scale molecular dynamics protein simulations.