Interactions In Crowded Environment Research Articles

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Death and chemotaxis: Dynamics of phage-bacteria interactions in crowded environments

Death and chemotaxis: Dynamics of phage-bacteria interactions in crowded environments

Exploiting compositional disorder in collectives of light-driven circle walkers.

Emergent behavior in collectives of "robotic" units with limited capabilities that is robust and programmable is a promising route to perform tasks on the micro and nanoscale that are otherwise difficult to realize. However, a comprehensive theoretical understanding of the physical principles, in particular steric interactions in crowded environments, is still largely missing. Here, we study simple light-driven walkers propelled through internal vibrations. We demonstrate that their dynamics is well captured by the model of active Brownian particles, albeit with an angular speed that differs between individual units. Transferring to a numerical model, we show that this polydispersity of angular speeds gives rise to specific collective behavior: self-sorting under confinement and enhancement of translational diffusion. Our results show that, while naively perceived as imperfection, disorder of individual properties can provide another route to realize programmable active matter.

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. In this work, we cast the problem of human trajectory forecasting as learning a representation of human social interactions. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learn about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two domain-knowledge inspired data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark <i>TrajNet</i>&#x002B;&#x002B;, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet&#x002B;&#x002B; validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.

Understanding DNA interactions in crowded environments with a coarse-grained model.

Nucleic acid interactions under crowded environments are of great importance for biological processes and nanotechnology. However, the kinetics and thermodynamics of nucleic acid interactions in a crowded environment remain poorly understood. We use a coarse-grained model of DNA to study the kinetics and thermodynamics of DNA duplex and hairpin formation in crowded environments. We find that crowders can increase the melting temperature of both an 8-mer DNA duplex and a hairpin with a stem of 6-nt depending on the excluded volume fraction of crowders in solution and the crowder size. The crowding induced stability originates from the entropic effect caused by the crowding particles in the system. Additionally, we study the hybridization kinetics of DNA duplex formation and the formation of hairpin stems, finding that the reaction rate kon is increased by the crowding effect, while koff is changed only moderately. The increase in kon mostly comes from increasing the probability of reaching a transition state with one base pair formed. A DNA strand displacement reaction in a crowded environment is also studied with the model and we find that rate of toehold association is increased, with possible applications to speeding up strand displacement cascades in nucleic acid nanotechnology.

The effects of osmolytes on in vitro kinesin-microtubule motility assays.

The gliding motility of microtubule filaments has been used to study the biophysical properties of kinesin motors, as well as being used in a variety of nanotechnological applications. While microtubules are generally stabilized in vitro with paclitaxel (Taxol®), osmolytes such as polyethylene glycol (PEG) and trimethylamine N-oxide (TMAO) are also able to inhibit depolymerization over extended periods of time. High concentrations of TMAO have also been reported to reversibly inhibit kinesin motility of paclitaxel-stabilized microtubules. Here, we examined the effects of the osmolytes PEG, TMAO, and glycerol on stabilizing microtubules during gliding motility on kinesin-coated substrates. As previously observed, microtubule depolymerization was inhibited in a concentration dependent manner by the addition of the different osmolytes. Kinesin-driven motility also exhibited concentration dependent effects with the addition of the osmolytes, specifically reducing the velocity, increasing rates of pinning, and altering trajectories of the microtubules. These data suggest that there is a delicate balance between the ability of osmolytes to stabilize microtubules without inhibiting motility. Overall, these findings provide a more comprehensive understanding of how osmolytes affect the dynamics of microtubules and kinesin motors, and their interactions in crowded environments.

Effects of osmolytes on protein-solvent interactions in crowded environment: Analyzing the effect of TMAO on proteins in crowded solutions

We analyzed the effect of a natural osmolyte, trimethylamine N-oxide (TMAO), on structural properties and conformational stabilities of several proteins under macromolecular crowding conditions by a set of biophysical techniques. We also used the solvent interaction analysis method to look at the peculiarities of the TMAO-protein interactions under crowded conditions. To this end, we analyzed the partitioning of these proteins in TMAO-free and TMAO-containing aqueous two-phase systems (ATPSs). These ATPSs had the same polymer composition of 6.0wt.% PEG-8000 and 12.0wt.% dextran-75, and same ionic composition of 0.01M K/NaPB, pH 7.4. These analyses revealed that there is no direct interaction of TMAO with proteins, suggesting that the TMAO effects on the protein structure in crowded solutions occur via the effects of this osmolyte on solvent properties of aqueous media. The effects of TMAO on protein structure in the presence of polymers were rather complex and protein-specific. Curiously, our study revealed that in highly concentrated polymer solutions, TMAO does not always act to promote further protein folding.

Effects of osmolytes on protein–solvent interactions in crowded environments: study of sucrose and trehalose effects on different proteins by solvent interaction analysis

The logarithms of the partition coefficients of proteins in the presence of 0.5 M sucrose and trehalose are linearly interrelated.